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# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import click
from lancedb.utils import CONFIG
@click.group()
@click.version_option(help="LanceDB command line interface entry point")
def cli():
"LanceDB command line interface"
diagnostics_help = """
Enable or disable LanceDB diagnostics. When enabled, LanceDB will send anonymous events
to help us improve LanceDB. These diagnostics are used only for error reporting and no
data is collected. You can find more about diagnosis on our docs:
https://lancedb.github.io/lancedb/cli_config/
"""
@cli.command(help=diagnostics_help)
@click.option("--enabled/--disabled", default=True)
def diagnostics(enabled):
CONFIG.update({"diagnostics": True if enabled else False})
click.echo("LanceDB diagnostics is %s" % ("enabled" if enabled else "disabled"))
@cli.command(help="Show current LanceDB configuration")
def config():
# TODO: pretty print as table with colors and formatting
click.echo("Current LanceDB configuration:")
cfg = CONFIG.copy()
cfg.pop("uuid") # Don't show uuid as it is not configurable
for item, amount in cfg.items():
click.echo("{} ({})".format(item, amount))
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# Copyright (c) Hegel AI, Inc.
# All rights reserved.
#
# This source code's license can be found in the
# LICENSE file in the root directory of this source tree.
import itertools
import warnings
import pandas as pd
from typing import Callable, Optional
try:
import lancedb
from lancedb.embeddings import with_embeddings
except ImportError:
lancedb = None
import logging
from time import perf_counter
from .experiment import Experiment
from ._utils import _get_dynamic_columns
VALID_TASKS = [""]
def query_builder(
table: "lancedb.Table",
embed_fn: Callable,
text: str,
metric: str = "cosine",
limit: int = 3,
filter: str = None,
nprobes: int = None,
refine_factor: int = None,
):
if nprobes is not None or refine_factor is not None:
warnings.warn(
"`nprobes` and `refine_factor` are not used by the default `query_builder`. "
"Feel free to open an issue to request adding support for them."
)
query = table.search(embed_fn(text)[0]).metric(metric)
if filter:
query = query.where(filter)
return query.limit(limit).to_df()
class LanceDBExperiment(Experiment):
r"""
Perform an experiment with ``LanceDB`` to test different embedding functions or retrieval arguments.
You can query from an existing table, or create a new one (and insert documents into it) during
the experiment.
Args:
uri (str): LanceDB uri to interact with your database. Default is "lancedb"
table_name (str): the table that you will get or create. Default is "table"
use_existing_table (bool): determines whether to create a new collection or use
an existing one
embedding_fns (list[Callable]): embedding functions to test in the experiment
by default only uses the default one in LanceDB
query_args (dict[str, list]): parameters used to query the table
Each value is expected to be a list to create all possible combinations
data (Optional[list[dict]]): documents or embeddings that will be added to
the newly created table
text_col_name (str): name of the text column in the table. Default is "text"
clean_up (bool): determines whether to drop the table after the experiment ends
"""
def __init__(
self,
embedding_fns: dict[str, Callable],
query_args: dict[str, list],
uri: str = "lancedb",
table_name: str = "table",
use_existing_table: bool = False,
data: Optional[list[dict]] = None,
text_col_name: str = "text",
clean_up: bool = False,
):
if lancedb is None:
raise ModuleNotFoundError(
"Package `lancedb` is required to be installed to use this experiment."
"Please use `pip install lancedb` to install the package"
)
self.table_name = table_name
self.use_existing_table = use_existing_table
self.embedding_fns = embedding_fns
if use_existing_table and data:
raise RuntimeError("You can either use an existing collection or create a new one during the experiment.")
if not use_existing_table and data is None:
raise RuntimeError("If you choose to create a new collection, you must also add to it.")
self.data = data if data is not None else []
self.argument_combos: list[dict] = []
self.text_col_name = text_col_name
self.db = lancedb.connect(uri)
self.completion_fn = self.lancedb_completion_fn
self.query_args = query_args
self.clean_up = clean_up
super().__init__()
def prepare(self):
for combo in itertools.product(*self.query_args.values()):
self.argument_combos.append(dict(zip(self.query_args.keys(), combo)))
def run(self, runs: int = 1):
input_args = [] # This will be used to construct DataFrame table
results = []
latencies = []
if not self.argument_combos:
logging.info("Preparing first...")
self.prepare()
for emb_fn_name, emb_fn in self.embedding_fns.items():
if self.use_existing_table: # Use existing table
table = self.db.open_table(self.table_name)
if not table:
raise RuntimeError(f"Table {self.table_name} does not exist.")
else: # Create table and insert data
data = with_embeddings(emb_fn, self.data, self.text_col_name)
table = self.db.create_table(self.table_name, data, mode="overwrite")
# Query from table
for query_arg_dict in self.argument_combos:
query_args = query_arg_dict.copy()
for _ in range(runs):
start = perf_counter()
results.append(self.lancedb_completion_fn(table=table, embedding_fn=emb_fn, **query_args))
latencies.append(perf_counter() - start)
query_args["emb_fn"] = emb_fn_name # Saving for visualization
input_args.append(query_args)
# Clean up
if self.clean_up:
self.db.drop_table(self.table_name)
self._construct_result_dfs(input_args, results, latencies)
def lancedb_completion_fn(self, table, embedding_fn, **kwargs):
return query_builder(table, embedding_fn, **kwargs)
def _construct_result_dfs(
self,
input_args: list[dict[str, object]],
results: list[dict[str, object]],
latencies: list[float],
):
r"""
Construct a few DataFrames that contain all relevant data (i.e. input arguments, results, evaluation metrics).
This version only extract the most relevant objects returned by LanceDB.
Args:
input_args (list[dict[str, object]]): list of dictionaries, where each of them is a set of
input argument that was passed into the model
results (list[dict[str, object]]): list of responses from the model
latencies (list[float]): list of latency measurements
"""
# `input_arg_df` contains all all input args
input_arg_df = pd.DataFrame(input_args)
# `dynamic_input_arg_df` contains input args that has more than one unique values
dynamic_input_arg_df = _get_dynamic_columns(input_arg_df)
# `response_df` contains the extracted response (often being the text response)
response_dict = dict()
response_dict["top doc ids"] = [self._extract_top_doc_ids(result) for result in results]
response_dict["distances"] = [self._extract_lancedb_dists(result) for result in results]
response_dict["documents"] = [self._extract_lancedb_docs(result) for result in results]
response_df = pd.DataFrame(response_dict)
# `result_df` contains everything returned by the completion function
result_df = response_df # pd.concat([self.response_df, pd.DataFrame(results)], axis=1)
# `score_df` contains computed metrics (e.g. latency, evaluation metrics)
self.score_df = pd.DataFrame({"latency": latencies})
# `partial_df` contains some input arguments, extracted responses, and score
self.partial_df = pd.concat([dynamic_input_arg_df, response_df, self.score_df], axis=1)
# `full_df` contains all input arguments, responses, and score
self.full_df = pd.concat([input_arg_df, result_df, self.score_df], axis=1)
@staticmethod
def _extract_top_doc_ids(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["ids"]
@staticmethod
def _extract_lancedb_dists(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["_distance"]
@staticmethod
def _extract_lancedb_docs(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["text"]
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"""LanceDB vector store with cloud storage support."""
import os
from typing import Any, Optional
from dotenv import load_dotenv
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase
from llama_index.vector_stores.lancedb import _to_lance_filter, _to_llama_similarities
from llama_index.vector_stores.types import VectorStoreQuery, VectorStoreQueryResult
from pandas import DataFrame
load_dotenv()
class LanceDBVectorStore(LanceDBVectorStoreBase):
"""Advanced LanceDB Vector Store supporting cloud storage and prefiltering."""
from lancedb.query import LanceQueryBuilder
from lancedb.table import Table
def __init__(
self,
uri: str,
table_name: str = "vectors",
nprobes: int = 20,
refine_factor: Optional[int] = None,
api_key: Optional[str] = None,
region: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Init params."""
self._setup_connection(uri, api_key, region)
self.uri = uri
self.table_name = table_name
self.nprobes = nprobes
self.refine_factor = refine_factor
self.api_key = api_key
self.region = region
def _setup_connection(self, uri: str, api_key: Optional[str] = None, region: Optional[str] = None):
"""Establishes a robust connection to LanceDB."""
api_key = api_key or os.getenv('LANCEDB_API_KEY')
region = region or os.getenv('LANCEDB_REGION')
import_err_msg = "`lancedb` package not found, please run `pip install lancedb`"
try:
import lancedb
except ImportError:
raise ImportError(import_err_msg)
if api_key and region:
self.connection = lancedb.connect(uri, api_key=api_key, region=region)
else:
self.connection = lancedb.connect(uri)
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""Enhanced query method to support prefiltering in LanceDB queries."""
table = self.connection.open_table(self.table_name)
lance_query = self._prepare_lance_query(query, table, **kwargs)
results = lance_query.to_df()
return self._construct_query_result(results)
def _prepare_lance_query(self, query: VectorStoreQuery, table: Table, **kwargs) -> LanceQueryBuilder:
"""Prepares the LanceDB query considering prefiltering and additional parameters."""
if query.filters is not None:
if "where" in kwargs:
raise ValueError(
"Cannot specify filter via both query and kwargs. "
"Use kwargs only for lancedb specific items that are "
"not supported via the generic query interface.")
where = _to_lance_filter(query.filters)
else:
where = kwargs.pop("where", None)
prefilter = kwargs.pop("prefilter", False)
table = self.connection.open_table(self.table_name)
lance_query = (
table.search(query.query_embedding).limit(query.similarity_top_k).where(
where, prefilter=prefilter).nprobes(self.nprobes))
if self.refine_factor is not None:
lance_query.refine_factor(self.refine_factor)
return lance_query
def _construct_query_result(self, results: DataFrame) -> VectorStoreQueryResult:
"""Constructs a VectorStoreQueryResult from a LanceDB query result."""
nodes = []
for _, row in results.iterrows():
node = TextNode(
text=row.get('text', ''), # ensure text is a string
id_=row['id'],
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(node_id=row['doc_id']),
})
nodes.append(node)
return VectorStoreQueryResult(
nodes=nodes,
similarities=_to_llama_similarities(results),
ids=results["id"].tolist(),
)
| [
"lancedb.connect"
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from pathlib import Path
from typing import Any, Callable
from lancedb import DBConnection as LanceDBConnection
from lancedb import connect as lancedb_connect
from lancedb.table import Table as LanceDBTable
from openai import Client as OpenAIClient
from pydantic import Field, PrivateAttr
from crewai_tools.tools.rag.rag_tool import Adapter
def _default_embedding_function():
client = OpenAIClient()
def _embedding_function(input):
rs = client.embeddings.create(input=input, model="text-embedding-ada-002")
return [record.embedding for record in rs.data]
return _embedding_function
class LanceDBAdapter(Adapter):
uri: str | Path
table_name: str
embedding_function: Callable = Field(default_factory=_default_embedding_function)
top_k: int = 3
vector_column_name: str = "vector"
text_column_name: str = "text"
_db: LanceDBConnection = PrivateAttr()
_table: LanceDBTable = PrivateAttr()
def model_post_init(self, __context: Any) -> None:
self._db = lancedb_connect(self.uri)
self._table = self._db.open_table(self.table_name)
return super().model_post_init(__context)
def query(self, question: str) -> str:
query = self.embedding_function([question])[0]
results = (
self._table.search(query, vector_column_name=self.vector_column_name)
.limit(self.top_k)
.select([self.text_column_name])
.to_list()
)
values = [result[self.text_column_name] for result in results]
return "\n".join(values)
| [
"lancedb.connect"
] | [((393, 407), 'openai.Client', 'OpenAIClient', ([], {}), '()\n', (405, 407), True, 'from openai import Client as OpenAIClient\n'), ((724, 774), 'pydantic.Field', 'Field', ([], {'default_factory': '_default_embedding_function'}), '(default_factory=_default_embedding_function)\n', (729, 774), False, 'from pydantic import Field, PrivateAttr\n'), ((898, 911), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (909, 911), False, 'from pydantic import Field, PrivateAttr\n'), ((939, 952), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (950, 952), False, 'from pydantic import Field, PrivateAttr\n'), ((1028, 1053), 'lancedb.connect', 'lancedb_connect', (['self.uri'], {}), '(self.uri)\n', (1043, 1053), True, 'from lancedb import connect as lancedb_connect\n')] |
import logging
from typing import Any, Dict, Generator, List, Optional, Sequence, Tuple, Type
import lancedb
import pandas as pd
from dotenv import load_dotenv
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import LanceVectorQueryBuilder
from pydantic import BaseModel, ValidationError, create_model
from langroid.embedding_models.base import (
EmbeddingModel,
EmbeddingModelsConfig,
)
from langroid.embedding_models.models import OpenAIEmbeddingsConfig
from langroid.mytypes import Document, EmbeddingFunction
from langroid.utils.configuration import settings
from langroid.utils.pydantic_utils import (
dataframe_to_document_model,
dataframe_to_documents,
extend_document_class,
extra_metadata,
flatten_pydantic_instance,
flatten_pydantic_model,
nested_dict_from_flat,
)
from langroid.vector_store.base import VectorStore, VectorStoreConfig
logger = logging.getLogger(__name__)
class LanceDBConfig(VectorStoreConfig):
cloud: bool = False
collection_name: str | None = "temp"
storage_path: str = ".lancedb/data"
embedding: EmbeddingModelsConfig = OpenAIEmbeddingsConfig()
distance: str = "cosine"
# document_class is used to store in lancedb with right schema,
# and also to retrieve the right type of Documents when searching.
document_class: Type[Document] = Document
flatten: bool = False # flatten Document class into LanceSchema ?
class LanceDB(VectorStore):
def __init__(self, config: LanceDBConfig = LanceDBConfig()):
super().__init__(config)
self.config: LanceDBConfig = config
emb_model = EmbeddingModel.create(config.embedding)
self.embedding_fn: EmbeddingFunction = emb_model.embedding_fn()
self.embedding_dim = emb_model.embedding_dims
self.host = config.host
self.port = config.port
self.is_from_dataframe = False # were docs ingested from a dataframe?
self.df_metadata_columns: List[str] = [] # metadata columns from dataframe
self._setup_schemas(config.document_class)
load_dotenv()
if self.config.cloud:
logger.warning(
"LanceDB Cloud is not available yet. Switching to local storage."
)
config.cloud = False
else:
try:
self.client = lancedb.connect(
uri=config.storage_path,
)
except Exception as e:
new_storage_path = config.storage_path + ".new"
logger.warning(
f"""
Error connecting to local LanceDB at {config.storage_path}:
{e}
Switching to {new_storage_path}
"""
)
self.client = lancedb.connect(
uri=new_storage_path,
)
# Note: Only create collection if a non-null collection name is provided.
# This is useful to delay creation of vecdb until we have a suitable
# collection name (e.g. we could get it from the url or folder path).
if config.collection_name is not None:
self.create_collection(
config.collection_name, replace=config.replace_collection
)
def _setup_schemas(self, doc_cls: Type[Document] | None) -> None:
doc_cls = doc_cls or self.config.document_class
self.unflattened_schema = self._create_lance_schema(doc_cls)
self.schema = (
self._create_flat_lance_schema(doc_cls)
if self.config.flatten
else self.unflattened_schema
)
def clear_empty_collections(self) -> int:
coll_names = self.list_collections()
n_deletes = 0
for name in coll_names:
nr = self.client.open_table(name).head(1).shape[0]
if nr == 0:
n_deletes += 1
self.client.drop_table(name)
return n_deletes
def clear_all_collections(self, really: bool = False, prefix: str = "") -> int:
"""Clear all collections with the given prefix."""
if not really:
logger.warning("Not deleting all collections, set really=True to confirm")
return 0
coll_names = [
c for c in self.list_collections(empty=True) if c.startswith(prefix)
]
if len(coll_names) == 0:
logger.warning(f"No collections found with prefix {prefix}")
return 0
n_empty_deletes = 0
n_non_empty_deletes = 0
for name in coll_names:
nr = self.client.open_table(name).head(1).shape[0]
n_empty_deletes += nr == 0
n_non_empty_deletes += nr > 0
self.client.drop_table(name)
logger.warning(
f"""
Deleted {n_empty_deletes} empty collections and
{n_non_empty_deletes} non-empty collections.
"""
)
return n_empty_deletes + n_non_empty_deletes
def list_collections(self, empty: bool = False) -> List[str]:
"""
Returns:
List of collection names that have at least one vector.
Args:
empty (bool, optional): Whether to include empty collections.
"""
colls = self.client.table_names(limit=None)
if len(colls) == 0:
return []
if empty: # include empty tbls
return colls # type: ignore
counts = [self.client.open_table(coll).head(1).shape[0] for coll in colls]
return [coll for coll, count in zip(colls, counts) if count > 0]
def _create_lance_schema(self, doc_cls: Type[Document]) -> Type[BaseModel]:
"""
Create a subclass of LanceModel with fields:
- id (str)
- Vector field that has dims equal to
the embedding dimension of the embedding model, and a data field of type
DocClass.
- other fields from doc_cls
Args:
doc_cls (Type[Document]): A Pydantic model which should be a subclass of
Document, to be used as the type for the data field.
Returns:
Type[BaseModel]: A new Pydantic model subclassing from LanceModel.
Raises:
ValueError: If `n` is not a non-negative integer or if `DocClass` is not a
subclass of Document.
"""
if not issubclass(doc_cls, Document):
raise ValueError("DocClass must be a subclass of Document")
n = self.embedding_dim
# Prepare fields for the new model
fields = {"id": (str, ...), "vector": (Vector(n), ...)}
sorted_fields = dict(
sorted(doc_cls.__fields__.items(), key=lambda item: item[0])
)
# Add both statically and dynamically defined fields from doc_cls
for field_name, field in sorted_fields.items():
fields[field_name] = (field.outer_type_, field.default)
# Create the new model with dynamic fields
NewModel = create_model(
"NewModel", __base__=LanceModel, **fields
) # type: ignore
return NewModel # type: ignore
def _create_flat_lance_schema(self, doc_cls: Type[Document]) -> Type[BaseModel]:
"""
Flat version of the lance_schema, as nested Pydantic schemas are not yet
supported by LanceDB.
"""
lance_model = self._create_lance_schema(doc_cls)
FlatModel = flatten_pydantic_model(lance_model, base_model=LanceModel)
return FlatModel
def create_collection(self, collection_name: str, replace: bool = False) -> None:
"""
Create a collection with the given name, optionally replacing an existing
collection if `replace` is True.
Args:
collection_name (str): Name of the collection to create.
replace (bool): Whether to replace an existing collection
with the same name. Defaults to False.
"""
self.config.collection_name = collection_name
collections = self.list_collections()
if collection_name in collections:
coll = self.client.open_table(collection_name)
if coll.head().shape[0] > 0:
logger.warning(f"Non-empty Collection {collection_name} already exists")
if not replace:
logger.warning("Not replacing collection")
return
else:
logger.warning("Recreating fresh collection")
self.client.create_table(collection_name, schema=self.schema, mode="overwrite")
if settings.debug:
level = logger.getEffectiveLevel()
logger.setLevel(logging.INFO)
logger.setLevel(level)
def _maybe_set_doc_class_schema(self, doc: Document) -> None:
"""
Set the config.document_class and self.schema based on doc if needed
Args:
doc: an instance of Document, to be added to a collection
"""
extra_metadata_fields = extra_metadata(doc, self.config.document_class)
if len(extra_metadata_fields) > 0:
logger.warning(
f"""
Added documents contain extra metadata fields:
{extra_metadata_fields}
which were not present in the original config.document_class.
Trying to change document_class and corresponding schemas.
Overriding LanceDBConfig.document_class with an auto-generated
Pydantic class that includes these extra fields.
If this fails, or you see odd results, it is recommended that you
define a subclass of Document, with metadata of class derived from
DocMetaData, with extra fields defined via
`Field(..., description="...")` declarations,
and set this document class as the value of the
LanceDBConfig.document_class attribute.
"""
)
doc_cls = extend_document_class(doc)
self.config.document_class = doc_cls
self._setup_schemas(doc_cls)
def add_documents(self, documents: Sequence[Document]) -> None:
super().maybe_add_ids(documents)
colls = self.list_collections(empty=True)
if len(documents) == 0:
return
embedding_vecs = self.embedding_fn([doc.content for doc in documents])
coll_name = self.config.collection_name
if coll_name is None:
raise ValueError("No collection name set, cannot ingest docs")
self._maybe_set_doc_class_schema(documents[0])
if (
coll_name not in colls
or self.client.open_table(coll_name).head(1).shape[0] == 0
):
# collection either doesn't exist or is empty, so replace it,
self.create_collection(coll_name, replace=True)
ids = [str(d.id()) for d in documents]
# don't insert all at once, batch in chunks of b,
# else we get an API error
b = self.config.batch_size
def make_batches() -> Generator[List[BaseModel], None, None]:
for i in range(0, len(ids), b):
batch = [
self.unflattened_schema(
id=ids[i + j],
vector=embedding_vecs[i + j],
**doc.dict(),
)
for j, doc in enumerate(documents[i : i + b])
]
if self.config.flatten:
batch = [
flatten_pydantic_instance(instance) # type: ignore
for instance in batch
]
yield batch
tbl = self.client.open_table(self.config.collection_name)
try:
tbl.add(make_batches())
except Exception as e:
logger.error(
f"""
Error adding documents to LanceDB: {e}
POSSIBLE REMEDY: Delete the LancdDB storage directory
{self.config.storage_path} and try again.
"""
)
def add_dataframe(
self,
df: pd.DataFrame,
content: str = "content",
metadata: List[str] = [],
) -> None:
"""
Add a dataframe to the collection.
Args:
df (pd.DataFrame): A dataframe
content (str): The name of the column in the dataframe that contains the
text content to be embedded using the embedding model.
metadata (List[str]): A list of column names in the dataframe that contain
metadata to be stored in the database. Defaults to [].
"""
self.is_from_dataframe = True
actual_metadata = metadata.copy()
self.df_metadata_columns = actual_metadata # could be updated below
# get content column
content_values = df[content].values.tolist()
embedding_vecs = self.embedding_fn(content_values)
# add vector column
df["vector"] = embedding_vecs
if content != "content":
# rename content column to "content", leave existing column intact
df = df.rename(columns={content: "content"}, inplace=False)
if "id" not in df.columns:
docs = dataframe_to_documents(df, content="content", metadata=metadata)
ids = [str(d.id()) for d in docs]
df["id"] = ids
if "id" not in actual_metadata:
actual_metadata += ["id"]
colls = self.list_collections(empty=True)
coll_name = self.config.collection_name
if (
coll_name not in colls
or self.client.open_table(coll_name).head(1).shape[0] == 0
):
# collection either doesn't exist or is empty, so replace it
# and set new schema from df
self.client.create_table(
self.config.collection_name,
data=df,
mode="overwrite",
)
doc_cls = dataframe_to_document_model(
df,
content=content,
metadata=actual_metadata,
exclude=["vector"],
)
self.config.document_class = doc_cls # type: ignore
self._setup_schemas(doc_cls) # type: ignore
else:
# collection exists and is not empty, so append to it
tbl = self.client.open_table(self.config.collection_name)
tbl.add(df)
def delete_collection(self, collection_name: str) -> None:
self.client.drop_table(collection_name)
def _lance_result_to_docs(self, result: LanceVectorQueryBuilder) -> List[Document]:
if self.is_from_dataframe:
df = result.to_pandas()
return dataframe_to_documents(
df,
content="content",
metadata=self.df_metadata_columns,
doc_cls=self.config.document_class,
)
else:
records = result.to_arrow().to_pylist()
return self._records_to_docs(records)
def _records_to_docs(self, records: List[Dict[str, Any]]) -> List[Document]:
if self.config.flatten:
docs = [
self.unflattened_schema(**nested_dict_from_flat(rec)) for rec in records
]
else:
try:
docs = [self.schema(**rec) for rec in records]
except ValidationError as e:
raise ValueError(
f"""
Error validating LanceDB result: {e}
HINT: This could happen when you're re-using an
existing LanceDB store with a different schema.
Try deleting your local lancedb storage at `{self.config.storage_path}`
re-ingesting your documents and/or replacing the collections.
"""
)
doc_cls = self.config.document_class
doc_cls_field_names = doc_cls.__fields__.keys()
return [
doc_cls(
**{
field_name: getattr(doc, field_name)
for field_name in doc_cls_field_names
}
)
for doc in docs
]
def get_all_documents(self, where: str = "") -> List[Document]:
if self.config.collection_name is None:
raise ValueError("No collection name set, cannot retrieve docs")
tbl = self.client.open_table(self.config.collection_name)
pre_result = tbl.search(None).where(where or None).limit(None)
return self._lance_result_to_docs(pre_result)
def get_documents_by_ids(self, ids: List[str]) -> List[Document]:
if self.config.collection_name is None:
raise ValueError("No collection name set, cannot retrieve docs")
_ids = [str(id) for id in ids]
tbl = self.client.open_table(self.config.collection_name)
docs = []
for _id in _ids:
results = self._lance_result_to_docs(tbl.search().where(f"id == '{_id}'"))
if len(results) > 0:
docs.append(results[0])
return docs
def similar_texts_with_scores(
self,
text: str,
k: int = 1,
where: Optional[str] = None,
) -> List[Tuple[Document, float]]:
embedding = self.embedding_fn([text])[0]
tbl = self.client.open_table(self.config.collection_name)
result = (
tbl.search(embedding).metric(self.config.distance).where(where).limit(k)
)
docs = self._lance_result_to_docs(result)
# note _distance is 1 - cosine
if self.is_from_dataframe:
scores = [
1 - rec["_distance"] for rec in result.to_pandas().to_dict("records")
]
else:
scores = [1 - rec["_distance"] for rec in result.to_arrow().to_pylist()]
if len(docs) == 0:
logger.warning(f"No matches found for {text}")
return []
if settings.debug:
logger.info(f"Found {len(docs)} matches, max score: {max(scores)}")
doc_score_pairs = list(zip(docs, scores))
self.show_if_debug(doc_score_pairs)
return doc_score_pairs
| [
"lancedb.connect",
"lancedb.pydantic.Vector"
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import json
import lancedb
from lancedb.pydantic import Vector, LanceModel
from datetime import datetime
# import pyarrow as pa
TABLE_NAME = "documents"
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
# vector: list of vectors
# file_name: name of file
# file_path: path of file
# id
# updated_at
# created_at
class Document(LanceModel):
id: str
file_name: str
file_path: str
created_at: datetime
updated_at: datetime
vector: Vector(768) # Palm Embeddings size
try:
table = db.create_table(TABLE_NAME, schema=Document)
except OSError:
print("table exists")
table = db.open_table(TABLE_NAME)
except Exception as inst:
# Print out the type of exceptions.
print(type(inst))
print(inst.args)
print(inst)
if True:
now = datetime.now()
# Idempotent upsert. Alternatively we can delete first, then insert.
table.add(
[
Document(
id="1",
file_name="test_name",
file_path="test_path",
created_at=now,
updated_at=now,
vector=[i for i in range(768)],
)
]
)
table.delete(f'id="1" AND created_at != timestamp "{now}"')
if False:
table.update(
where='id="1"',
values=Document(
id="1",
file_name="test_name",
file_path="test_path",
created_at=datetime.now(),
updated_at=datetime.now(),
vector=[i for i in range(768)],
),
)
vector = [i for i in range(768)]
result = table.search(vector).limit(2).to_list()
for item in result:
print(item)
# print(json.dumps(item, indent=2))
print(db[TABLE_NAME].head())
| [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((189, 209), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (204, 209), False, 'import lancedb\n'), ((461, 472), 'lancedb.pydantic.Vector', 'Vector', (['(768)'], {}), '(768)\n', (467, 472), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((786, 800), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (798, 800), False, 'from datetime import datetime\n'), ((1421, 1435), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1433, 1435), False, 'from datetime import datetime\n'), ((1460, 1474), 'datetime.datetime.now', 'datetime.now', ([], {}), '()\n', (1472, 1474), False, 'from datetime import datetime\n')] |
import json
from sentence_transformers import SentenceTransformer
from pydantic.main import ModelMetaclass
from pathlib import Path
import pandas as pd
import sqlite3
from uuid import uuid4
import lancedb
encoder = SentenceTransformer('all-MiniLM-L6-v2')
data_folder = Path('data/collections')
config_file = Path('data/config/indexes.yaml')
index_folder = Path('indexes')
lance_folder = Path('indexes')
lance_folder.mkdir(parents=True, exist_ok=True)
sqlite_folder = Path('data/indexes/')
class LanceDBDocument():
def __init__(self, document:dict, title:str, text:str, fields, tags=None, date=None, file_path=None):
self.document = self.fill_missing_fields(document, text, title, tags, date)
# self.text = document[text]
# self.tags = document[tags] if tags is not None else list()
# self.date = document[date] if date is not None else None
self.file_path = file_path
self.metadata = {k:document[k] for k in fields if k not in [title, text, tags, date]}
self.uuid = str(uuid4()) if 'uuid' not in document else document['uuid']
self.save_uuids = list()
self.sqlite_fields = list()
self.lance_exclude = list()
def fill_missing_fields(self, document, text, title, tags, date):
if title not in document:
self.title = ''
else:
self.title = document[title]
if text not in document:
self.text = ''
else:
self.text = document[text]
if date not in document:
self.date = ''
else:
self.date = document[date]
if tags not in document:
self.tags = list()
else:
self.tags = document[tags]
def create_json_document(self, text, uuids=None):
"""Creates a custom dictionary object that can be used for both sqlite and lancedb
The full document is always stored in sqlite where fixed fields are:
title
text
date
filepath
document_uuid - used for retrieval from lancedb results
Json field contains the whole document for retrieval and display
Lancedb only gets searching text, vectorization of that, and filter fields
"""
_document = {'title':self.title,
'text':text,
'tags':self.tags,
'date':self.date,
'file_path':str(self.file_path),
'uuid':self.uuid,
'metadata': self.metadata}
self._enforce_tags_schema()
for field in ['title','date','file_path']:
self.enforce_string_schema(field, _document)
return _document
def enforce_string_schema(self, field, test_document):
if not isinstance(test_document[field], str):
self.lance_exclude.append(field)
def _enforce_tags_schema(self):
# This enforces a simple List[str] format for the tags to match what lancedb can use for filtering
# If they are of type List[Dict] as a nested field, they are stored in sqlite for retrieval
if isinstance(self.tags, list):
tags_are_list = True
for _tag in self.tags:
if not isinstance(_tag, str):
tags_are_list = False
break
if not tags_are_list:
self.lance_exclude.append('tags')
def return_document(self):
document = self.create_json_document(self.text)
return document
class SqlLiteIngest():
def __init__(self, documents, source_file, db_location, index_name, overwrite):
self.documents = documents
self.source_file = source_file
self.db_location = db_location
self.index_name = index_name
self.overwrite = overwrite
def initialize(self):
self.connection = sqlite3.connect(self.db_location)
if self.overwrite:
self.connection.execute(f"""DROP TABLE IF EXISTS {self.index_name};""")
table_exists = self.connection.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{self.index_name}';").fetchall()
if len(table_exists) == 0:
self.connection.execute(f"""
CREATE TABLE {self.index_name}(
id INTEGER PRIMARY KEY NOT NULL,
uuid STRING NOT NULL,
text STRING NOT NULL,
title STRING,
date STRING,
source_file STRING,
metadata JSONB);""")
def insert(self, document):
self.connection.execute(f"""INSERT INTO
{self.index_name} (uuid, text, title, date, source_file, metadata)
VALUES ('{document.uuid.replace("'","''")}', '{document.text.replace("'","''")}',
'{document.title.replace("'","''")}', '{document.date.replace("'","''")}',
'{self.index_name.replace("'","''")}', '{json.dumps(document.metadata).replace("'","''")}');""")
def bulk_insert(self):
for document in self.documents:
self.insert(document)
self.connection.commit()
self.connection.close()
from lancedb.pydantic import LanceModel, Vector, List
class LanceDBSchema384(LanceModel):
uuid: str
text: str
title: str
tags: List[str]
vector: Vector(384)
class LanceDBSchema512(LanceModel):
uuid: str
text: str
title: str
tags: List[str]
vector: Vector(512)
class LanceDBIngest():
def __init__(self, documents, lance_location, index_name, overwrite, encoder, schema):
self.documents = documents
self.lance_location = lance_location
self.index_name = index_name
self.overwrite = overwrite
self.encoder = encoder
self.schema = schema
def initialize(self):
self.db = lancedb.connect(self.lance_location)
existing_tables = self.db.table_names()
self.documents = [self.prep_documents(document) for document in self.documents]
if self.overwrite:
self.table = self.db.create_table(self.index_name, data=self.documents, mode='overwrite', schema=self.schema.to_arrow_schema())
else:
if self.index_name in existing_tables:
self.table = self.db.open_table(self.index_name)
self.table.add(self.documents)
else:
self.table = self.db.create_table(self.index_name, data=self.documents, schema=self.schema.to_arrow_schema())
def prep_documents(self, document):
lance_document = dict()
lance_document['text'] = document.text
lance_document['vector'] = self.encoder.encode(document.text)
lance_document['uuid'] = document.uuid
lance_document['title'] = document.title
lance_document['tags'] = document.tags
return lance_document
def insert(self, document):
document['vector'] = self.encoder.encode(document.text)
self.table.add(document)
def bulk_insert(self, create_vectors=False):
if create_vectors:
self.table.create_index(vector_column_name='vector', metric='cosine')
self.table.create_fts_index(field_names=['title','text'], replace=True)
return self.table
class IndexDocuments():
def __init__(self,field_mapping, source_file, index_name, overwrite):
self.field_mapping = field_mapping
self.source_file = source_file
self.index_name = index_name
self.overwrite = overwrite
def open_json(self):
with open(self.source_file, 'r') as f:
self.data = json.load(f)
print(self.data)
def open_csv(self):
self.data = pd.read_csv(self.source_file)
def create_document(self, document):
document = LanceDBDocument(document,
text=self.field_mapping['text'],
title=self.field_mapping['title'],
tags=self.field_mapping['tags'],
date=self.field_mapping['date'],
fields=list(document.keys()),
file_path=self.source_file
)
return document
def create_documents(self):
self.documents = [self.create_document(document) for document in self.data]
def ingest(self, overwrite=False):
# lance_path = Path(f'../indexes/lance')
lance_folder.mkdir(parents=True, exist_ok=True)
lance_ingest = LanceDBIngest(documents=self.documents,
lance_location=lance_folder,
# field_mapping=self.field_mapping,
index_name=self.index_name,
overwrite=self.overwrite,
encoder=encoder,
schema=LanceDBSchema384)
lance_ingest.initialize()
if len(self.documents) <= 256:
_table = lance_ingest.bulk_insert(create_vectors=False)
else:
_table = lance_ingest.bulk_insert(create_vectors=True)
sql_path = sqlite_folder.joinpath('documents.sqlite')
sqlite_ingest = SqlLiteIngest(documents=self.documents,
source_file=self.source_file,
db_location=sql_path,
index_name=self.index_name,
overwrite=self.overwrite)
sqlite_ingest.initialize()
sqlite_ingest.bulk_insert()
| [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((216, 255), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (235, 255), False, 'from sentence_transformers import SentenceTransformer\n'), ((271, 295), 'pathlib.Path', 'Path', (['"""data/collections"""'], {}), "('data/collections')\n", (275, 295), False, 'from pathlib import Path\n'), ((310, 342), 'pathlib.Path', 'Path', (['"""data/config/indexes.yaml"""'], {}), "('data/config/indexes.yaml')\n", (314, 342), False, 'from pathlib import Path\n'), ((358, 373), 'pathlib.Path', 'Path', (['"""indexes"""'], {}), "('indexes')\n", (362, 373), False, 'from pathlib import Path\n'), ((390, 405), 'pathlib.Path', 'Path', (['"""indexes"""'], {}), "('indexes')\n", (394, 405), False, 'from pathlib import Path\n'), ((471, 492), 'pathlib.Path', 'Path', (['"""data/indexes/"""'], {}), "('data/indexes/')\n", (475, 492), False, 'from pathlib import Path\n'), ((5306, 5317), 'lancedb.pydantic.Vector', 'Vector', (['(384)'], {}), '(384)\n', (5312, 5317), False, 'from lancedb.pydantic import LanceModel, Vector, List\n'), ((5430, 5441), 'lancedb.pydantic.Vector', 'Vector', (['(512)'], {}), '(512)\n', (5436, 5441), False, 'from lancedb.pydantic import LanceModel, Vector, List\n'), ((3896, 3929), 'sqlite3.connect', 'sqlite3.connect', (['self.db_location'], {}), '(self.db_location)\n', (3911, 3929), False, 'import sqlite3\n'), ((5814, 5850), 'lancedb.connect', 'lancedb.connect', (['self.lance_location'], {}), '(self.lance_location)\n', (5829, 5850), False, 'import lancedb\n'), ((7670, 7699), 'pandas.read_csv', 'pd.read_csv', (['self.source_file'], {}), '(self.source_file)\n', (7681, 7699), True, 'import pandas as pd\n'), ((7583, 7595), 'json.load', 'json.load', (['f'], {}), '(f)\n', (7592, 7595), False, 'import json\n'), ((1035, 1042), 'uuid.uuid4', 'uuid4', ([], {}), '()\n', (1040, 1042), False, 'from uuid import uuid4\n'), ((4948, 4977), 'json.dumps', 'json.dumps', (['document.metadata'], {}), '(document.metadata)\n', (4958, 4977), False, 'import json\n')] |
import os
import urllib.request
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np
import lancedb
from lancedb.embeddings import with_embeddings
import pandas as pd
os.environ['PREDICTIONGUARD_TOKEN'] = "q1VuOjnffJ3NO2oFN8Q9m8vghYc84ld13jaqdF7E"
# Let's get the html off of a website.
fp = urllib.request.urlopen("file:////home/shaunak_joshi/gt/insuranceagent.html")
mybytes = fp.read()
html = mybytes.decode("utf8")
fp.close()
# And convert it to text.
h = html2text.HTML2Text()
h.ignore_links = True
text = h.handle(html)
# Clean things up just a bit.
text = text.split("Introduction")[1]
#print(text)
#text = text.split("Location, Location, Location")[0]
#print(text)
#print(type(text))
# Chunk the text into smaller pieces for injection into LLM prompts.
text_splitter = CharacterTextSplitter(chunk_size=700, chunk_overlap=50)
docs = text_splitter.split_text(text)
# Let's checkout some of the chunks!
#for i in range(0, 10):
# print("Chunk", str(i+1))
# print("----------------------------")
# print(docs[i])
# print("")
# Let's take care of some of the formatting so it doesn't conflict with our
# typical prompt template structure
docs = [x.replace('#', '-') for x in docs]
# Now we need to embed these documents and put them into a "vector store" or
# "vector db" that we will use for semantic search and retrieval.
# Embeddings setup
name="all-MiniLM-L12-v2"
model = SentenceTransformer(name)
def embed_batch(batch):
return [model.encode(sentence) for sentence in batch]
def embed(sentence):
return model.encode(sentence)
# LanceDB setup
os.mkdir(".lancedb")
uri = ".lancedb"
db = lancedb.connect(uri)
# Create a dataframe with the chunk ids and chunks
metadata = []
for i in range(len(docs)):
metadata.append([i,docs[i]])
doc_df = pd.DataFrame(metadata, columns=["chunk", "text"])
# Embed the documents
data = with_embeddings(embed_batch, doc_df)
# Create the DB table and add the records.
db.create_table("linux", data=data)
table = db.open_table("linux")
table.add(data=data)
# Let's try to match a query to one of our documents.
#message = "What plays a crucial role in deciding insurance policies?"
#results = table.search(embed(message)).limit(5).to_pandas()
#print(results.head())
# Now let's augment our Q&A prompt with this external knowledge on-the-fly!!!
template = """### Instruction:
Read the below input context and respond with a short answer to the given question. Use only the information in the bel>
### Input:
Context: {context}
Question: {question}
### Response:
"""
qa_prompt = PromptTemplate(
input_variables=["context", "question"],
template=template,
)
def rag_answer(message):
# Search the for relevant context
results = table.search(embed(message)).limit(5).to_pandas()
results.sort_values(by=['_distance'], inplace=True, ascending=True)
doc_use = results['text'].values[0]
# Augment the prompt with the context
prompt = qa_prompt.format(context=doc_use, question=message)
# Get a response
result = pg.Completion.create(
model="Nous-Hermes-Llama2-13B",
prompt=prompt
)
return result['choices'][0]['text']
response = rag_answer("A house has been destroyed by a tornado and also has been set on fire. The water doesn't work but the gas lines are fine. The area the house is in is notorious for crime. It is built in an earthquake prone zone. There are cracks in the walls and it is quite old. Based on this information, generate three insights about the type of insurance policy the house will require and any other thing you find important. Keep the insights under 20 words each.")
print('')
print("RESPONSE:", response)
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((670, 691), 'html2text.HTML2Text', 'html2text.HTML2Text', ([], {}), '()\n', (689, 691), False, 'import html2text\n'), ((1001, 1056), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(700)', 'chunk_overlap': '(50)'}), '(chunk_size=700, chunk_overlap=50)\n', (1022, 1056), False, 'from langchain.text_splitter import CharacterTextSplitter\n'), ((1627, 1652), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['name'], {}), '(name)\n', (1646, 1652), False, 'from sentence_transformers import SentenceTransformer\n'), ((1818, 1838), 'os.mkdir', 'os.mkdir', (['""".lancedb"""'], {}), "('.lancedb')\n", (1826, 1838), False, 'import os\n'), ((1863, 1883), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1878, 1883), False, 'import lancedb\n'), ((2025, 2074), 'pandas.DataFrame', 'pd.DataFrame', (['metadata'], {'columns': "['chunk', 'text']"}), "(metadata, columns=['chunk', 'text'])\n", (2037, 2074), True, 'import pandas as pd\n'), ((2108, 2144), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embed_batch', 'doc_df'], {}), '(embed_batch, doc_df)\n', (2123, 2144), False, 'from lancedb.embeddings import with_embeddings\n'), ((2827, 2901), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['context', 'question']", 'template': 'template'}), "(input_variables=['context', 'question'], template=template)\n", (2841, 2901), False, 'from langchain import PromptTemplate, FewShotPromptTemplate\n'), ((3294, 3361), 'predictionguard.Completion.create', 'pg.Completion.create', ([], {'model': '"""Nous-Hermes-Llama2-13B"""', 'prompt': 'prompt'}), "(model='Nous-Hermes-Llama2-13B', prompt=prompt)\n", (3314, 3361), True, 'import predictionguard as pg\n')] |
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import EmbeddingFunctionRegistry
registry = EmbeddingFunctionRegistry.get_instance()
func = registry.get("openai").create()
class Questions(LanceModel):
question: str = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
| [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
] | [((117, 157), 'lancedb.embeddings.EmbeddingFunctionRegistry.get_instance', 'EmbeddingFunctionRegistry.get_instance', ([], {}), '()\n', (155, 157), False, 'from lancedb.embeddings import EmbeddingFunctionRegistry\n')] |
import logging
import os
import time
from functools import wraps
from pathlib import Path
from random import random, seed
import lancedb
import pyarrow as pa
import pyarrow.parquet as pq
import typer
from lancedb.db import LanceTable
log_level = os.environ.get("LOG_LEVEL", "info")
logging.basicConfig(
level=getattr(logging, log_level.upper()),
format="%(asctime)s %(levelname)s | %(processName)s %(name)s | %(message)s",
)
logger = logging.getLogger(__name__)
app = typer.Typer()
V_SIZE = 256
DB_PATH = "benchmark"
DB_TABLE = "vectors"
DB_TABLE_SIZE = os.environ.get("DB_TABLE_SIZE", 100000)
Q_PATH = "query"
Q_SIZE = os.environ.get("Q_SIZE", 100)
Q_V = "v.parquet"
Q_KNN = "knn.parquet"
Q_ANN = "ann.parquet"
def timeit(func):
@wraps(func)
def f(*args, **kwargs):
start_time = time.perf_counter()
result = func(*args, **kwargs)
end_time = time.perf_counter()
total_time = end_time - start_time
logger.info(f"{func.__name__} {args} done in {total_time:.2f} secs")
return result
return f
def get_db():
if int(os.environ["AZURE"]) == 0:
f = Path(os.environ["DATA"])
f.mkdir(parents=True, exist_ok=True)
return lancedb.connect(f / DB_PATH)
else:
return lancedb.connect(
f"az://{os.environ['AZURE_STORAGE_CONTAINER']}/{DB_PATH}"
)
def open_table(table: str):
return LanceTable(get_db(), table)
def get_q(what="v"):
tables = {
"v": Q_V,
"knn": Q_KNN,
"ann": Q_ANN,
}
f = Path(os.environ["DATA"]) / Q_PATH
f.mkdir(parents=True, exist_ok=True)
return f / tables[what]
def gen_data(n: int, start=1):
seed()
for i in range(start, start + n):
yield ({"id": i, "vector": list(random() for _ in range(V_SIZE))})
@app.command()
def db_init(n: int = DB_TABLE_SIZE):
get_db().create_table(DB_TABLE, data=list(gen_data(n)))
@app.command()
def db_info():
table = open_table(DB_TABLE)
logger.debug(table.head(10))
@app.command()
def db_add(n: int, start: int):
table = open_table(DB_TABLE)
table.add(list(gen_data(n, start=start)))
@app.command()
def q_init(n: int = Q_SIZE):
pq.write_table(pa.Table.from_pylist(list(gen_data(n))), get_q())
@app.command()
def q_info():
logger.debug(pq.read_table(get_q()))
@timeit
def q_process(what: str):
table = open_table(DB_TABLE)
r = pa.Table.from_pylist(
[
{
"id": v["id"],
"neighbours": table.search(v["vector"])
.limit(10)
.select(["id"])
.to_arrow()["id"]
.to_pylist(),
}
for v in pq.read_table(get_q()).to_pylist()
]
)
pq.write_table(r, get_q(what))
@app.command()
@timeit
def create_index():
open_table(DB_TABLE).create_index(
num_sub_vectors=8
) # TODO :avoid hard coded params
@app.command()
def q_knn():
q_process("knn")
@app.command()
def q_ann():
create_index()
q_process("ann")
if __name__ == "__main__":
app()
| [
"lancedb.connect"
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import argparse
import os
import shutil
from functools import lru_cache
from pathlib import Path
from typing import Any, Iterator
import srsly
from codetiming import Timer
from config import Settings
from dotenv import load_dotenv
from rich import progress
from schemas.wine import LanceModelWine, Wine
from sentence_transformers import SentenceTransformer
import lancedb
from lancedb.pydantic import pydantic_to_schema
from lancedb.table import Table
load_dotenv()
# Custom types
JsonBlob = dict[str, Any]
class FileNotFoundError(Exception):
pass
@lru_cache()
def get_settings():
# Use lru_cache to avoid loading .env file for every request
return Settings()
def chunk_iterable(item_list: list[JsonBlob], chunksize: int) -> Iterator[list[JsonBlob]]:
"""
Break a large iterable into an iterable of smaller iterables of size `chunksize`
"""
for i in range(0, len(item_list), chunksize):
yield item_list[i : i + chunksize]
def get_json_data(data_dir: Path, filename: str) -> list[JsonBlob]:
"""Get all line-delimited json files (.jsonl) from a directory with a given prefix"""
file_path = data_dir / filename
if not file_path.is_file():
# File may not have been uncompressed yet so try to do that first
data = srsly.read_gzip_jsonl(file_path)
# This time if it isn't there it really doesn't exist
if not file_path.is_file():
raise FileNotFoundError(f"No valid .jsonl file found in `{data_dir}`")
else:
data = srsly.read_gzip_jsonl(file_path)
return data
def validate(
data: list[JsonBlob],
exclude_none: bool = False,
) -> list[JsonBlob]:
validated_data = [Wine(**item).model_dump(exclude_none=exclude_none) for item in data]
return validated_data
def embed_func(batch: list[str], model) -> list[list[float]]:
return [model.encode(sentence.lower()) for sentence in batch]
def vectorize_text(data: list[JsonBlob]) -> list[LanceModelWine] | None:
# Load a sentence transformer model for semantic similarity from a specified checkpoint
model_id = get_settings().embedding_model_checkpoint
assert model_id, "Invalid embedding model checkpoint specified in .env file"
MODEL = SentenceTransformer(model_id)
ids = [item["id"] for item in data]
to_vectorize = [text.get("to_vectorize") for text in data]
vectors = embed_func(to_vectorize, MODEL)
try:
data_batch = [{**d, "vector": vector} for d, vector in zip(data, vectors)]
except Exception as e:
print(f"{e}: Failed to add ID range {min(ids)}-{max(ids)}")
return None
return data_batch
def embed_batches(tbl: str, validated_data: list[JsonBlob]) -> Table:
"""Ingest vector embeddings in batches for ANN index"""
chunked_data = chunk_iterable(validated_data, CHUNKSIZE)
print(f"Adding vectors to table for ANN index...")
# Add rich progress bar
with progress.Progress(
"[progress.description]{task.description}",
progress.BarColumn(),
"[progress.percentage]{task.percentage:>3.0f}%",
progress.TimeElapsedColumn(),
) as prog:
overall_progress_task = prog.add_task(
"Starting vectorization...", total=len(validated_data) // CHUNKSIZE
)
for chunk in chunked_data:
batch = vectorize_text(chunk)
prog.update(overall_progress_task, advance=1)
tbl.add(batch, mode="append")
def main(tbl: Table, data: list[JsonBlob]) -> None:
"""Generate sentence embeddings and create ANN and FTS indexes"""
with Timer(
name="Data validation in pydantic",
text="Validated data using Pydantic in {:.4f} sec",
):
validated_data = validate(data, exclude_none=False)
with Timer(
name="Insert vectors in batches",
text="Created sentence embeddings in {:.4f} sec",
):
embed_batches(tbl, validated_data)
print(f"Finished inserting {len(tbl)} vectors into LanceDB table")
with Timer(name="Create ANN index", text="Created ANN index in {:.4f} sec"):
print("Creating ANN index...")
# Creating IVF-PQ index for now, as we eagerly await DiskANN
# Choose num partitions as a power of 2 that's closest to len(dataset) // 5000
# In this case, we have 130k datapoints, so the nearest power of 2 is 130000//5000 ~ 32)
tbl.create_index(metric="cosine", num_partitions=4, num_sub_vectors=32)
with Timer(name="Create FTS index", text="Created FTS index in {:.4f} sec"):
# Create a full-text search index via Tantivy (which implements Lucene + BM25 in Rust)
tbl.create_fts_index(["to_vectorize"])
if __name__ == "__main__":
# fmt: off
parser = argparse.ArgumentParser("Bulk index database from the wine reviews JSONL data")
parser.add_argument("--limit", "-l", type=int, default=0, help="Limit the size of the dataset to load for testing purposes")
parser.add_argument("--chunksize", type=int, default=1000, help="Size of each chunk to break the dataset into before processing")
parser.add_argument("--filename", type=str, default="winemag-data-130k-v2.jsonl.gz", help="Name of the JSONL zip file to use")
args = vars(parser.parse_args())
# fmt: on
LIMIT = args["limit"]
DATA_DIR = Path(__file__).parents[1] / "data"
FILENAME = args["filename"]
CHUNKSIZE = args["chunksize"]
data = list(get_json_data(DATA_DIR, FILENAME))
assert data, "No data found in the specified file"
data = data[:LIMIT] if LIMIT > 0 else data
DB_NAME = "./winemag"
TABLE = "wines"
if os.path.exists(DB_NAME):
shutil.rmtree(DB_NAME)
db = lancedb.connect(DB_NAME)
try:
tbl = db.create_table(TABLE, schema=pydantic_to_schema(LanceModelWine), mode="create")
except OSError:
tbl = db.open_table(TABLE)
main(tbl, data)
print("Finished execution!")
| [
"lancedb.connect",
"lancedb.pydantic.pydantic_to_schema"
] | [((455, 468), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (466, 468), False, 'from dotenv import load_dotenv\n'), ((560, 571), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (569, 571), False, 'from functools import lru_cache\n'), ((668, 678), 'config.Settings', 'Settings', ([], {}), '()\n', (676, 678), False, 'from config import Settings\n'), ((2230, 2259), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['model_id'], {}), '(model_id)\n', (2249, 2259), False, 'from sentence_transformers import SentenceTransformer\n'), ((4737, 4816), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (['"""Bulk index database from the wine reviews JSONL data"""'], {}), "('Bulk index database from the wine reviews JSONL data')\n", (4760, 4816), False, 'import argparse\n'), ((5613, 5636), 'os.path.exists', 'os.path.exists', (['DB_NAME'], {}), '(DB_NAME)\n', (5627, 5636), False, 'import os\n'), ((5679, 5703), 'lancedb.connect', 'lancedb.connect', (['DB_NAME'], {}), '(DB_NAME)\n', (5694, 5703), False, 'import lancedb\n'), ((1283, 1315), 'srsly.read_gzip_jsonl', 'srsly.read_gzip_jsonl', (['file_path'], {}), '(file_path)\n', (1304, 1315), False, 'import srsly\n'), ((1522, 1554), 'srsly.read_gzip_jsonl', 'srsly.read_gzip_jsonl', (['file_path'], {}), '(file_path)\n', (1543, 1554), False, 'import srsly\n'), ((3582, 3680), 'codetiming.Timer', 'Timer', ([], {'name': '"""Data validation in pydantic"""', 'text': '"""Validated data using Pydantic in {:.4f} sec"""'}), "(name='Data validation in pydantic', text=\n 'Validated data using Pydantic in {:.4f} sec')\n", (3587, 3680), False, 'from codetiming import Timer\n'), ((3770, 3864), 'codetiming.Timer', 'Timer', ([], {'name': '"""Insert vectors in batches"""', 'text': '"""Created sentence embeddings in {:.4f} sec"""'}), "(name='Insert vectors in batches', text=\n 'Created sentence embeddings in {:.4f} sec')\n", (3775, 3864), False, 'from codetiming import Timer\n'), ((4012, 4082), 'codetiming.Timer', 'Timer', ([], {'name': '"""Create ANN index"""', 'text': '"""Created ANN index in {:.4f} sec"""'}), "(name='Create ANN index', text='Created ANN index in {:.4f} sec')\n", (4017, 4082), False, 'from codetiming import Timer\n'), ((4466, 4536), 'codetiming.Timer', 'Timer', ([], {'name': '"""Create FTS index"""', 'text': '"""Created FTS index in {:.4f} sec"""'}), "(name='Create FTS index', text='Created FTS index in {:.4f} sec')\n", (4471, 4536), False, 'from codetiming import Timer\n'), ((5646, 5668), 'shutil.rmtree', 'shutil.rmtree', (['DB_NAME'], {}), '(DB_NAME)\n', (5659, 5668), False, 'import shutil\n'), ((3003, 3023), 'rich.progress.BarColumn', 'progress.BarColumn', ([], {}), '()\n', (3021, 3023), False, 'from rich import progress\n'), ((3090, 3118), 'rich.progress.TimeElapsedColumn', 'progress.TimeElapsedColumn', ([], {}), '()\n', (3116, 3118), False, 'from rich import progress\n'), ((1688, 1700), 'schemas.wine.Wine', 'Wine', ([], {}), '(**item)\n', (1692, 1700), False, 'from schemas.wine import LanceModelWine, Wine\n'), ((5304, 5318), 'pathlib.Path', 'Path', (['__file__'], {}), '(__file__)\n', (5308, 5318), False, 'from pathlib import Path\n'), ((5757, 5791), 'lancedb.pydantic.pydantic_to_schema', 'pydantic_to_schema', (['LanceModelWine'], {}), '(LanceModelWine)\n', (5775, 5791), False, 'from lancedb.pydantic import pydantic_to_schema\n')] |
from datasets import load_dataset
data = load_dataset('jamescalam/youtube-transcriptions', split='train')
from lancedb.context import contextualize
df = (contextualize(data.to_pandas())
.groupby("title").text_col("text")
.window(20).stride(4)
.to_df())
df.head(1)
import openai
import os
# Configuring the environment variable OPENAI_API_KEY
if "OPENAI_API_KEY" not in os.environ:
# OR set the key here as a variable
openai.api_key = ""
assert len(openai.Model.list()["data"]) > 0
def embed_func(c):
rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
return [record["embedding"] for record in rs["data"]]
import lancedb
from lancedb.embeddings import with_embeddings
# data = with_embeddings(embed_func, df, show_progress=True)
# data.to_pandas().head(1)
db = lancedb.connect("/tmp/lancedb")
# tbl = db.create_table("youtube-chatbot", data)
# get table
tbl = db.open_table("youtube-chatbot")
#print the length of the table
print(len(tbl))
tbl.to_pandas().head(1)
def create_prompt(query, context):
limit = 3750
prompt_start = (
"Answer the question based on the context below.\n\n"+
"Context:\n"
)
prompt_end = (
f"\n\nQuestion: {query}\nAnswer:"
)
# append contexts until hitting limit
for i in range(1, len(context)):
if len("\n\n---\n\n".join(context.text[:i])) >= limit:
prompt = (
prompt_start +
"\n\n---\n\n".join(context.text[:i-1]) +
prompt_end
)
break
elif i == len(context)-1:
prompt = (
prompt_start +
"\n\n---\n\n".join(context.text) +
prompt_end
)
print ( "prompt:", prompt )
return prompt
def complete(prompt):
# query text-davinci-003
res = openai.Completion.create(
engine='text-davinci-003',
prompt=prompt,
temperature=0,
max_tokens=400,
top_p=1,
frequency_penalty=0,
presence_penalty=0,
stop=None
)
return res['choices'][0]['text'].strip()
query = ("How do I use the Pandas library to create embeddings?")
# Embed the question
emb = embed_func(query)[0]
# Use LanceDB to get top 3 most relevant context
context = tbl.search(emb).limit(3).to_df()
# Get the answer from completion API
prompt = create_prompt(query, context)
print( "context:", context )
print ( complete( prompt )) | [
"lancedb.connect"
] | [((42, 106), 'datasets.load_dataset', 'load_dataset', (['"""jamescalam/youtube-transcriptions"""'], {'split': '"""train"""'}), "('jamescalam/youtube-transcriptions', split='train')\n", (54, 106), False, 'from datasets import load_dataset\n'), ((831, 862), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/lancedb"""'], {}), "('/tmp/lancedb')\n", (846, 862), False, 'import lancedb\n'), ((549, 614), 'openai.Embedding.create', 'openai.Embedding.create', ([], {'input': 'c', 'engine': '"""text-embedding-ada-002"""'}), "(input=c, engine='text-embedding-ada-002')\n", (572, 614), False, 'import openai\n'), ((1876, 2042), 'openai.Completion.create', 'openai.Completion.create', ([], {'engine': '"""text-davinci-003"""', 'prompt': 'prompt', 'temperature': '(0)', 'max_tokens': '(400)', 'top_p': '(1)', 'frequency_penalty': '(0)', 'presence_penalty': '(0)', 'stop': 'None'}), "(engine='text-davinci-003', prompt=prompt,\n temperature=0, max_tokens=400, top_p=1, frequency_penalty=0,\n presence_penalty=0, stop=None)\n", (1900, 2042), False, 'import openai\n'), ((483, 502), 'openai.Model.list', 'openai.Model.list', ([], {}), '()\n', (500, 502), False, 'import openai\n')] |
import hashlib
import io
import logging
from typing import List
import numpy as np
from lancedb.pydantic import LanceModel, vector
from PIL import Image
from pydantic import BaseModel, Field, computed_field
from homematch.config import IMAGES_DIR
logger = logging.getLogger(__name__)
class PropertyListingBase(BaseModel):
page_source: str
resource_title: str
resource_country: str
operation_type: str
active: bool
url: str
title: str
normalized_title: str
zone: str
current_price: float | None = None
ad_text: str
basic_info: List[str]
last_update: str
main_image_url: str
scraped_ts: str
@computed_field # type: ignore
@property
def identificator(self) -> str:
return hashlib.sha256(self.url.encode()).hexdigest()[:16]
@computed_field # type: ignore
@property
def text_description(self) -> str:
basic_info_text = ",".join(self.basic_info)
basic_info_text = basic_info_text.replace("habs", "bedrooms")
basic_info_text = basic_info_text.replace("baños", "bathrooms")
basic_info_text = basic_info_text.replace("baño", "bathroom")
basic_info_text = basic_info_text.replace("m²", "square meters")
basic_info_text = basic_info_text.replace("planta", "floor")
basic_info_text = basic_info_text.replace("Bajo", "0 floor")
description = ""
description += f"Zone: {self.zone}."
description += f"\nPrice: {self.current_price} euros."
description += f"\nFeatures: {basic_info_text}"
return description
class PropertyListing(PropertyListingBase):
images_dir: str = Field(str(IMAGES_DIR), description="Directory to store images")
@property
def image_path(self) -> str:
return str(self.images_dir) + f"/{self.identificator}.jpg"
def load_image(self) -> Image.Image:
try:
return Image.open(self.image_path)
except FileNotFoundError:
logger.error(f"Image file not found: {self.image_path}")
raise
@classmethod
def pil_to_bytes(cls, img: Image.Image) -> bytes:
buf = io.BytesIO()
img.save(buf, format="PNG")
return buf.getvalue()
@classmethod
def pil_to_numpy(cls, img: Image.Image) -> np.ndarray:
return np.array(img)
class PropertyData(PropertyListing):
class Config:
arbitrary_types_allowed = True
image: Image.Image
class ImageData(PropertyListing, LanceModel):
vector: vector(768) # type: ignore
image_bytes: bytes
| [
"lancedb.pydantic.vector"
] | [((259, 286), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (276, 286), False, 'import logging\n'), ((2511, 2522), 'lancedb.pydantic.vector', 'vector', (['(768)'], {}), '(768)\n', (2517, 2522), False, 'from lancedb.pydantic import LanceModel, vector\n'), ((2146, 2158), 'io.BytesIO', 'io.BytesIO', ([], {}), '()\n', (2156, 2158), False, 'import io\n'), ((2317, 2330), 'numpy.array', 'np.array', (['img'], {}), '(img)\n', (2325, 2330), True, 'import numpy as np\n'), ((1911, 1938), 'PIL.Image.open', 'Image.open', (['self.image_path'], {}), '(self.image_path)\n', (1921, 1938), False, 'from PIL import Image\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import logging
import os
from typing import Any, Callable, Dict, List, Optional, Union
from urllib.parse import urljoin
import attrs
import pyarrow as pa
import requests
from pydantic import BaseModel
from requests.adapters import HTTPAdapter
from urllib3 import Retry
from lancedb.common import Credential
from lancedb.remote import VectorQuery, VectorQueryResult
from lancedb.remote.connection_timeout import LanceDBClientHTTPAdapterFactory
from lancedb.remote.errors import LanceDBClientError
ARROW_STREAM_CONTENT_TYPE = "application/vnd.apache.arrow.stream"
def _check_not_closed(f):
@functools.wraps(f)
def wrapped(self, *args, **kwargs):
if self.closed:
raise ValueError("Connection is closed")
return f(self, *args, **kwargs)
return wrapped
def _read_ipc(resp: requests.Response) -> pa.Table:
resp_body = resp.content
with pa.ipc.open_file(pa.BufferReader(resp_body)) as reader:
return reader.read_all()
@attrs.define(slots=False)
class RestfulLanceDBClient:
db_name: str
region: str
api_key: Credential
host_override: Optional[str] = attrs.field(default=None)
closed: bool = attrs.field(default=False, init=False)
connection_timeout: float = attrs.field(default=120.0, kw_only=True)
read_timeout: float = attrs.field(default=300.0, kw_only=True)
@functools.cached_property
def session(self) -> requests.Session:
sess = requests.Session()
retry_adapter_instance = retry_adapter(retry_adapter_options())
sess.mount(urljoin(self.url, "/v1/table/"), retry_adapter_instance)
adapter_class = LanceDBClientHTTPAdapterFactory()
sess.mount("https://", adapter_class())
return sess
@property
def url(self) -> str:
return (
self.host_override
or f"https://{self.db_name}.{self.region}.api.lancedb.com"
)
def close(self):
self.session.close()
self.closed = True
@functools.cached_property
def headers(self) -> Dict[str, str]:
headers = {
"x-api-key": self.api_key,
}
if self.region == "local": # Local test mode
headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
if self.host_override:
headers["x-lancedb-database"] = self.db_name
return headers
@staticmethod
def _check_status(resp: requests.Response):
if resp.status_code == 404:
raise LanceDBClientError(f"Not found: {resp.text}")
elif 400 <= resp.status_code < 500:
raise LanceDBClientError(
f"Bad Request: {resp.status_code}, error: {resp.text}"
)
elif 500 <= resp.status_code < 600:
raise LanceDBClientError(
f"Internal Server Error: {resp.status_code}, error: {resp.text}"
)
elif resp.status_code != 200:
raise LanceDBClientError(
f"Unknown Error: {resp.status_code}, error: {resp.text}"
)
@_check_not_closed
def get(self, uri: str, params: Union[Dict[str, Any], BaseModel] = None):
"""Send a GET request and returns the deserialized response payload."""
if isinstance(params, BaseModel):
params: Dict[str, Any] = params.dict(exclude_none=True)
with self.session.get(
urljoin(self.url, uri),
params=params,
headers=self.headers,
timeout=(self.connection_timeout, self.read_timeout),
) as resp:
self._check_status(resp)
return resp.json()
@_check_not_closed
def post(
self,
uri: str,
data: Optional[Union[Dict[str, Any], BaseModel, bytes]] = None,
params: Optional[Dict[str, Any]] = None,
content_type: Optional[str] = None,
deserialize: Callable = lambda resp: resp.json(),
request_id: Optional[str] = None,
) -> Dict[str, Any]:
"""Send a POST request and returns the deserialized response payload.
Parameters
----------
uri : str
The uri to send the POST request to.
data: Union[Dict[str, Any], BaseModel]
request_id: Optional[str]
Optional client side request id to be sent in the request headers.
"""
if isinstance(data, BaseModel):
data: Dict[str, Any] = data.dict(exclude_none=True)
if isinstance(data, bytes):
req_kwargs = {"data": data}
else:
req_kwargs = {"json": data}
headers = self.headers.copy()
if content_type is not None:
headers["content-type"] = content_type
if request_id is not None:
headers["x-request-id"] = request_id
with self.session.post(
urljoin(self.url, uri),
headers=headers,
params=params,
timeout=(self.connection_timeout, self.read_timeout),
**req_kwargs,
) as resp:
self._check_status(resp)
return deserialize(resp)
@_check_not_closed
def list_tables(self, limit: int, page_token: Optional[str] = None) -> List[str]:
"""List all tables in the database."""
if page_token is None:
page_token = ""
json = self.get("/v1/table/", {"limit": limit, "page_token": page_token})
return json["tables"]
@_check_not_closed
def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
"""Query a table."""
tbl = self.post(f"/v1/table/{table_name}/query/", query, deserialize=_read_ipc)
return VectorQueryResult(tbl)
def mount_retry_adapter_for_table(self, table_name: str) -> None:
"""
Adds an http adapter to session that will retry retryable requests to the table.
"""
retry_options = retry_adapter_options(methods=["GET", "POST"])
retry_adapter_instance = retry_adapter(retry_options)
session = self.session
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/query/"), retry_adapter_instance
)
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/describe/"),
retry_adapter_instance,
)
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/index/list/"),
retry_adapter_instance,
)
def retry_adapter_options(methods=["GET"]) -> Dict[str, Any]:
return {
"retries": int(os.environ.get("LANCE_CLIENT_MAX_RETRIES", "3")),
"connect_retries": int(os.environ.get("LANCE_CLIENT_CONNECT_RETRIES", "3")),
"read_retries": int(os.environ.get("LANCE_CLIENT_READ_RETRIES", "3")),
"backoff_factor": float(
os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_FACTOR", "0.25")
),
"backoff_jitter": float(
os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_JITTER", "0.25")
),
"statuses": [
int(i.strip())
for i in os.environ.get(
"LANCE_CLIENT_RETRY_STATUSES", "429, 500, 502, 503"
).split(",")
],
"methods": methods,
}
def retry_adapter(options: Dict[str, Any]) -> HTTPAdapter:
total_retries = options["retries"]
connect_retries = options["connect_retries"]
read_retries = options["read_retries"]
backoff_factor = options["backoff_factor"]
backoff_jitter = options["backoff_jitter"]
statuses = options["statuses"]
methods = frozenset(options["methods"])
logging.debug(
f"Setting up retry adapter with {total_retries} retries," # noqa G003
+ f"connect retries {connect_retries}, read retries {read_retries},"
+ f"backoff factor {backoff_factor}, statuses {statuses}, "
+ f"methods {methods}"
)
return HTTPAdapter(
max_retries=Retry(
total=total_retries,
connect=connect_retries,
read=read_retries,
backoff_factor=backoff_factor,
backoff_jitter=backoff_jitter,
status_forcelist=statuses,
allowed_methods=methods,
)
)
| [
"lancedb.remote.VectorQueryResult",
"lancedb.remote.connection_timeout.LanceDBClientHTTPAdapterFactory",
"lancedb.remote.errors.LanceDBClientError"
] | [((1587, 1612), 'attrs.define', 'attrs.define', ([], {'slots': '(False)'}), '(slots=False)\n', (1599, 1612), False, 'import attrs\n'), ((1207, 1225), 'functools.wraps', 'functools.wraps', (['f'], {}), '(f)\n', (1222, 1225), False, 'import functools\n'), ((1733, 1758), 'attrs.field', 'attrs.field', ([], {'default': 'None'}), '(default=None)\n', (1744, 1758), False, 'import attrs\n'), ((1779, 1817), 'attrs.field', 'attrs.field', ([], {'default': '(False)', 'init': '(False)'}), '(default=False, init=False)\n', (1790, 1817), False, 'import attrs\n'), ((1851, 1891), 'attrs.field', 'attrs.field', ([], {'default': '(120.0)', 'kw_only': '(True)'}), '(default=120.0, kw_only=True)\n', (1862, 1891), False, 'import attrs\n'), ((1918, 1958), 'attrs.field', 'attrs.field', ([], {'default': '(300.0)', 'kw_only': '(True)'}), '(default=300.0, kw_only=True)\n', (1929, 1958), False, 'import attrs\n'), ((8166, 8402), 'logging.debug', 'logging.debug', (["(f'Setting up retry adapter with {total_retries} 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4008), 'urllib.parse.urljoin', 'urljoin', (['self.url', 'uri'], {}), '(self.url, uri)\n', (3993, 4008), False, 'from urllib.parse import urljoin\n'), ((5430, 5452), 'urllib.parse.urljoin', 'urljoin', (['self.url', 'uri'], {}), '(self.url, uri)\n', (5437, 5452), False, 'from urllib.parse import urljoin\n'), ((3373, 3462), 'lancedb.remote.errors.LanceDBClientError', 'LanceDBClientError', (['f"""Internal Server Error: {resp.status_code}, error: {resp.text}"""'], {}), "(\n f'Internal Server Error: {resp.status_code}, error: {resp.text}')\n", (3391, 3462), False, 'from lancedb.remote.errors import LanceDBClientError\n'), ((3544, 3620), 'lancedb.remote.errors.LanceDBClientError', 'LanceDBClientError', (['f"""Unknown Error: {resp.status_code}, error: {resp.text}"""'], {}), "(f'Unknown Error: {resp.status_code}, error: {resp.text}')\n", (3562, 3620), False, 'from lancedb.remote.errors import LanceDBClientError\n'), ((7643, 7710), 'os.environ.get', 'os.environ.get', (['"""LANCE_CLIENT_RETRY_STATUSES"""', '"""429, 500, 502, 503"""'], {}), "('LANCE_CLIENT_RETRY_STATUSES', '429, 500, 502, 503')\n", (7657, 7710), False, 'import os\n')] |
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
Language,
LatexTextSplitter,
)
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
import argparse, os, arxiv
os.environ["OPENAI_API_KEY"] = "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"
embeddings = OpenAIEmbeddings()
from langchain.vectorstores import LanceDB
from lancedb.pydantic import Vector, LanceModel
from Typing import List
from datetime import datetime
import lancedb
global embedding_out_length
embedding_out_length = 1536
class Content(LanceModel):
id: str
arxiv_id: str
vector: Vector(embedding_out_length)
text: str
uploaded_date: datetime
title: str
authors: List[str]
abstract: str
categories: List[str]
url: str
def PyPDF_to_Vector(table: LanceDB, embeddings: OpenAIEmbeddings, src_dir: str, n_threads: int = 1):
pass
if __name__ == "__main__":
argparser = argparse.ArgumentParser(description="Create Vector DB and perform ingestion from source files")
argparser.add_argument('-s', '--src_dir', type=str, required=True, help = "Source directory where arxiv sources are stored")
argparser.add_argument('-db', '--db_name', type=str, required=True, help = "Name of the LanceDB database to be created")
argparser.add_argument('-t', '--table_name', type=str, required=False, help = "Name of the LanceDB table to be created", default = "EIC_archive")
argparser.add_argument('-openai_key', '--openai_api_key', type=str, required=True, help = "OpenAI API key")
argparser.add_argument('-c', '--chunking', type = str, required=False, help = "Type of Chunking PDF or LATEX", default = "PDF")
argparser.add_argument('-n', '--nthreads', type=int, default=-1)
args = argparser.parse_args()
SRC_DIR = args.src_dir
DB_NAME = args.db_name
TABLE_NAME = args.table_name
OPENAI_API_KEY = args.openai_api_key
NTHREADS = args.nthreads
db = lancedb.connect(DB_NAME)
table = db.create_table(TABLE_NAME, schema=Content, mode="overwrite")
db = lancedb.connect()
meta_data = {"arxiv_id": "1", "title": "EIC LLM",
"category" : "N/A",
"authors": "N/A",
"sub_categories": "N/A",
"abstract": "N/A",
"published": "N/A",
"updated": "N/A",
"doi": "N/A"
},
table = db.create_table(
"EIC_archive",
data=[
{
"vector": embeddings.embed_query("EIC LLM"),
"text": "EIC LLM",
"id": "1",
"arxiv_id" : "N/A",
"title" : "N/A",
"category" : "N/A",
"published" : "N/A"
}
],
mode="overwrite",
)
vectorstore = LanceDB(connection = table, embedding = embeddings)
sourcedir = "PDFs"
count = 0
for source in os.listdir(sourcedir):
if not os.path.isdir(os.path.join("PDFs", source)):
continue
print (f"Adding the source document {source} to the Vector DB")
import arxiv
client = arxiv.Client()
search = arxiv.Search(id_list=[source])
paper = next(arxiv.Client().results(search))
meta_data = {"arxiv_id": paper.entry_id,
"title": paper.title,
"category" : categories[paper.primary_category],
"published": paper.published
}
for file in os.listdir(os.path.join(sourcedir, source)):
if file.endswith(".tex"):
latex_file = os.path.join(sourcedir, source, file)
print (source, latex_file)
documents = TextLoader(latex_file, encoding = 'latin-1').load()
latex_splitter = LatexTextSplitter(
chunk_size=120, chunk_overlap=10
)
documents = latex_splitter.split_documents(documents)
for doc in documents:
for k, v in meta_data.items():
doc.metadata[k] = v
vectorstore.add_documents(documents = documents)
count+=len(documents) | [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((342, 360), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (358, 360), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((2116, 2133), 'lancedb.connect', 'lancedb.connect', ([], {}), '()\n', (2131, 2133), False, 'import lancedb\n'), ((2820, 2867), 'langchain.vectorstores.LanceDB', 'LanceDB', ([], {'connection': 'table', 'embedding': 'embeddings'}), '(connection=table, embedding=embeddings)\n', (2827, 2867), False, 'from langchain.vectorstores import LanceDB\n'), ((2916, 2937), 'os.listdir', 'os.listdir', (['sourcedir'], {}), '(sourcedir)\n', (2926, 2937), False, 'import argparse, os, arxiv\n'), ((648, 676), 'lancedb.pydantic.Vector', 'Vector', (['embedding_out_length'], {}), '(embedding_out_length)\n', (654, 676), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((978, 1078), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Create Vector DB and perform ingestion from source files"""'}), "(description=\n 'Create Vector DB and perform ingestion from source files')\n", (1001, 1078), False, 'import argparse, os, arxiv\n'), ((2006, 2030), 'lancedb.connect', 'lancedb.connect', (['DB_NAME'], {}), '(DB_NAME)\n', (2021, 2030), False, 'import lancedb\n'), ((3110, 3124), 'arxiv.Client', 'arxiv.Client', ([], {}), '()\n', (3122, 3124), False, 'import arxiv\n'), ((3138, 3168), 'arxiv.Search', 'arxiv.Search', ([], {'id_list': '[source]'}), '(id_list=[source])\n', (3150, 3168), False, 'import arxiv\n'), ((3458, 3489), 'os.path.join', 'os.path.join', (['sourcedir', 'source'], {}), '(sourcedir, source)\n', (3470, 3489), False, 'import argparse, os, arxiv\n'), ((2964, 2992), 'os.path.join', 'os.path.join', (['"""PDFs"""', 'source'], {}), "('PDFs', source)\n", (2976, 2992), False, 'import argparse, os, arxiv\n'), ((3551, 3588), 'os.path.join', 'os.path.join', (['sourcedir', 'source', 'file'], {}), '(sourcedir, source, file)\n', (3563, 3588), False, 'import argparse, os, arxiv\n'), ((3733, 3784), 'langchain.text_splitter.LatexTextSplitter', 'LatexTextSplitter', ([], {'chunk_size': '(120)', 'chunk_overlap': '(10)'}), '(chunk_size=120, chunk_overlap=10)\n', (3750, 3784), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter, Language, LatexTextSplitter\n'), ((3186, 3200), 'arxiv.Client', 'arxiv.Client', ([], {}), '()\n', (3198, 3200), False, 'import arxiv\n'), ((3652, 3694), 'langchain.document_loaders.TextLoader', 'TextLoader', (['latex_file'], {'encoding': '"""latin-1"""'}), "(latex_file, encoding='latin-1')\n", (3662, 3694), False, 'from langchain.document_loaders import TextLoader\n')] |
import time
import os
import pandas as pd
import streamlit as st
import lancedb
from lancedb.embeddings import with_embeddings
from langchain import PromptTemplate
import predictionguard as pg
import streamlit as st
import duckdb
import re
import numpy as np
from sentence_transformers import SentenceTransformer
#---------------------#
# Lance DB Setup #
#---------------------#
uri = "schema.lancedb"
db = lancedb.connect(uri)
def embed(query, embModel):
return embModel.encode(query)
def batch_embed_func(batch):
return [st.session_state['en_emb'].encode(sentence) for sentence in batch]
#---------------------#
# Streamlit config #
#---------------------#
if "login" not in st.session_state:
st.session_state["login"] = False
# Hide the hamburger menu
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
#--------------------------#
# Define datasets #
#--------------------------#
#JOBS
df1=pd.read_csv('datasets/jobs.csv')
#SOCIAL
df2=pd.read_csv('datasets/social.csv')
#movies
df3=pd.read_csv('datasets/movies.csv')
conn = duckdb.connect(database=':memory:')
conn.register('jobs', df1)
conn.register('social', df2)
conn.register('movies', df3)
#--------------------------#
# Prompt Templates #
#--------------------------#
### PROMPT TEMPLATES
### PROMPT TEMPLATES
qa_template = """### System:
You are a data chatbot who answers the user question. To answer these questions we need to run SQL queries on our data and its output is given below in context. You just have to frame your answer using that context. Give a short and crisp response.Don't add any notes or any extra information after your response.
### User:
Question: {question}
context: {context}
### Assistant:
"""
qa_prompt = PromptTemplate(template=qa_template,input_variables=["question", "context"])
sql_template = """<|begin_of_sentence|>You are a SQL expert and you only generate SQL queries which are executable. You provide no extra explanations.
You respond with a SQL query that answers the user question in the below instruction by querying a database with the schema provided in the below instruction.
Always start your query with SELECT statement and end with a semicolon.
### Instruction:
User question: \"{question}\"
Database schema:
{schema}
### Response:
"""
sql_prompt=PromptTemplate(template=sql_template, input_variables=["question","schema"])
#--------------------------#
# Generate SQL Query #
#--------------------------#
# Embeddings setup
name="all-MiniLM-L12-v2"
def load_model():
return SentenceTransformer(name)
model = load_model()
def generate_sql_query(question, schema):
prompt_filled = sql_prompt.format(question=question,schema=schema)
try:
result = pg.Completion.create(
model="deepseek-coder-6.7b-instruct",
prompt=prompt_filled,
max_tokens=300,
temperature=0.1
)
sql_query = result["choices"][0]["text"]
return sql_query
except Exception as e:
return None
def extract_and_refine_sql_query(sql_query):
# Extract SQL query using a regular expression
match = re.search(r"(SELECT.*?);", sql_query, re.DOTALL)
if match:
refined_query = match.group(1)
# Check for and remove any text after a colon
colon_index = refined_query.find(':')
if colon_index != -1:
refined_query = refined_query[:colon_index]
# Ensure the query ends with a semicolon
if not refined_query.endswith(';'):
refined_query += ';'
return refined_query
else:
return ""
def get_answer_from_sql(question):
# Search Relavent Tables
table = db.open_table("schema")
results = table.search(embed(question, model)).limit(2).to_df()
print(results)
results = results[results['_distance'] < 1.5]
print("Results:", results)
if len(results) == 0:
completion = "We did not find any relevant tables."
return completion
else:
results.sort_values(by=['_distance'], inplace=True, ascending=True)
doc_use = ""
for _, row in results.iterrows():
if len(row['text'].split(' ')) < 10:
continue
else:
schema=row['schema']
table_name=row['text']
st.sidebar.info(table_name)
st.sidebar.code(schema)
break
sql_query = generate_sql_query(question, schema)
sql_query = extract_and_refine_sql_query(sql_query)
try:
# print("Executing SQL Query:", sql_query)
result = conn.execute(sql_query).fetchall()
# print("Result:", result)
return result, sql_query
except Exception as e:
print(f"Error executing SQL query: {e}")
return "There was an error executing the SQL query."
#--------------------------#
# Get Answer #
#--------------------------#
def get_answer(question,context):
try:
prompt_filled = qa_prompt.format(question=question, context=context)
# Respond to the user
output = pg.Completion.create(
model="Neural-Chat-7B",
prompt=prompt_filled,
max_tokens=200,
temperature=0.1
)
completion = output['choices'][0]['text']
return completion
except Exception as e:
completion = "There was an error executing the SQL query."
return completion
#--------------------------#
# Streamlit app #
#--------------------------#
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask a question"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
# contruct prompt thread
examples = []
turn = "user"
example = {}
for m in st.session_state.messages:
latest_message = m["content"]
example[turn] = m["content"]
if turn == "user":
turn = "assistant"
else:
turn = "user"
examples.append(example)
example = {}
if len(example) > 2:
examples = examples[-2:]
else:
thread = ""
# # Check for PII
# with st.spinner("Checking for PII..."):
# pii_result = pg.PII.check(
# prompt=latest_message,
# replace=False,
# replace_method="fake"
# )
# # Check for injection
# with st.spinner("Checking for security vulnerabilities..."):
# injection_result = pg.Injection.check(
# prompt=latest_message,
# detect=True
# )
# # Handle insecure states
# elif "[" in pii_result['checks'][0]['pii_types_and_positions']:
# st.warning('Warning! PII detected. Please avoid using personal information.')
# full_response = "Warning! PII detected. Please avoid using personal information."
# elif injection_result['checks'][0]['probability'] > 0.5:
# st.warning('Warning! Injection detected. Your input might result in a security breach.')
# full_response = "Warning! Injection detected. Your input might result in a security breach."
# generate response
with st.spinner("Generating an answer..."):
context=get_answer_from_sql(latest_message)
print("context",context)
completion = get_answer(latest_message,context)
# display response
for token in completion.split(" "):
full_response += " " + token
message_placeholder.markdown(full_response + "▌")
time.sleep(0.075)
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response}) | [
"lancedb.connect"
] | [((413, 433), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (428, 433), False, 'import lancedb\n'), ((890, 947), 'streamlit.markdown', 'st.markdown', (['hide_streamlit_style'], {'unsafe_allow_html': '(True)'}), '(hide_streamlit_style, unsafe_allow_html=True)\n', (901, 947), True, 'import streamlit as st\n'), ((1043, 1075), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/jobs.csv"""'], {}), "('datasets/jobs.csv')\n", (1054, 1075), True, 'import pandas as pd\n'), ((1089, 1123), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/social.csv"""'], {}), "('datasets/social.csv')\n", (1100, 1123), True, 'import pandas as pd\n'), ((1137, 1171), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/movies.csv"""'], {}), "('datasets/movies.csv')\n", (1148, 1171), True, 'import pandas as pd\n'), ((1180, 1215), 'duckdb.connect', 'duckdb.connect', ([], {'database': '""":memory:"""'}), "(database=':memory:')\n", (1194, 1215), False, 'import duckdb\n'), ((1861, 1938), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['question', 'context']"}), "(template=qa_template, input_variables=['question', 'context'])\n", (1875, 1938), False, 'from langchain import PromptTemplate\n'), ((2426, 2503), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'sql_template', 'input_variables': "['question', 'schema']"}), "(template=sql_template, input_variables=['question', 'schema'])\n", (2440, 2503), False, 'from langchain import PromptTemplate\n'), ((2672, 2697), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['name'], {}), '(name)\n', (2691, 2697), False, 'from sentence_transformers import SentenceTransformer\n'), ((3239, 3286), 're.search', 're.search', (['"""(SELECT.*?);"""', 'sql_query', 're.DOTALL'], {}), "('(SELECT.*?);', sql_query, re.DOTALL)\n", (3248, 3286), False, 'import re\n'), ((5846, 5877), 'streamlit.chat_input', 'st.chat_input', (['"""Ask a question"""'], {}), "('Ask a question')\n", (5859, 5877), True, 'import streamlit as st\n'), ((5883, 5952), 'streamlit.session_state.messages.append', 'st.session_state.messages.append', (["{'role': 'user', 'content': prompt}"], {}), "({'role': 'user', 'content': prompt})\n", (5915, 5952), True, 'import streamlit as st\n'), ((8226, 8311), 'streamlit.session_state.messages.append', 'st.session_state.messages.append', (["{'role': 'assistant', 'content': full_response}"], {}), "({'role': 'assistant', 'content':\n full_response})\n", (8258, 8311), True, 'import streamlit as st\n'), ((2856, 2974), 'predictionguard.Completion.create', 'pg.Completion.create', ([], {'model': '"""deepseek-coder-6.7b-instruct"""', 'prompt': 'prompt_filled', 'max_tokens': '(300)', 'temperature': '(0.1)'}), "(model='deepseek-coder-6.7b-instruct', prompt=\n prompt_filled, max_tokens=300, temperature=0.1)\n", (2876, 2974), True, 'import predictionguard as pg\n'), ((5195, 5298), 'predictionguard.Completion.create', 'pg.Completion.create', ([], {'model': '"""Neural-Chat-7B"""', 'prompt': 'prompt_filled', 'max_tokens': '(200)', 'temperature': '(0.1)'}), "(model='Neural-Chat-7B', prompt=prompt_filled,\n max_tokens=200, temperature=0.1)\n", (5215, 5298), True, 'import predictionguard as pg\n'), ((5758, 5790), 'streamlit.chat_message', 'st.chat_message', (["message['role']"], {}), "(message['role'])\n", (5773, 5790), True, 'import streamlit as st\n'), ((5800, 5831), 'streamlit.markdown', 'st.markdown', (["message['content']"], {}), "(message['content'])\n", (5811, 5831), True, 'import streamlit as st\n'), ((5962, 5985), 'streamlit.chat_message', 'st.chat_message', (['"""user"""'], {}), "('user')\n", (5977, 5985), True, 'import streamlit as st\n'), ((5995, 6014), 'streamlit.markdown', 'st.markdown', (['prompt'], {}), '(prompt)\n', (6006, 6014), True, 'import streamlit as st\n'), ((6025, 6053), 'streamlit.chat_message', 'st.chat_message', (['"""assistant"""'], {}), "('assistant')\n", (6040, 6053), True, 'import streamlit as st\n'), ((6085, 6095), 'streamlit.empty', 'st.empty', ([], {}), '()\n', (6093, 6095), True, 'import streamlit as st\n'), ((7748, 7785), 'streamlit.spinner', 'st.spinner', (['"""Generating an answer..."""'], {}), "('Generating an answer...')\n", (7758, 7785), True, 'import streamlit as st\n'), ((4413, 4440), 'streamlit.sidebar.info', 'st.sidebar.info', (['table_name'], {}), '(table_name)\n', (4428, 4440), True, 'import streamlit as st\n'), ((4457, 4480), 'streamlit.sidebar.code', 'st.sidebar.code', (['schema'], {}), '(schema)\n', (4472, 4480), True, 'import streamlit as st\n'), ((8147, 8164), 'time.sleep', 'time.sleep', (['(0.075)'], {}), '(0.075)\n', (8157, 8164), False, 'import time\n')] |
from FlagEmbedding import LLMEmbedder, FlagReranker
import os
import lancedb
import re
import pandas as pd
import random
from datasets import load_dataset
import torch
import gc
import lance
from lancedb.embeddings import with_embeddings
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
embed_model = LLMEmbedder('BAAI/llm-embedder', use_fp16=False) # Load model (automatically use GPUs)
reranker_model = FlagReranker('BAAI/bge-reranker-base', use_fp16=True) # use_fp16 speeds up computation with a slight performance degradation
"""# Load `Chunks` of data from [BeIR Dataset](https://huggingface.co/datasets/BeIR/scidocs)
Note: This is a dataset built specially for retrieval tasks to see how good your search is working
"""
data=pd.read_csv("Kcc_subset.csv")
# just random samples for faster embed demo
data['documents'] = 'query:' + data['QueryText'] + ', answer:' + data['KccAns']
data = data.dropna()
def embed_documents(batch):
"""
Function to embed the whole text data
"""
return embed_model.encode_keys(batch, task=task) # Encode data or 'keys'
db = lancedb.connect("./db") # Connect Local DB
if "doc_embed" in db.table_names():
table = db.open_table("doc_embed") # Open Table
else:
# Use the train text chunk data to save embed in the DB
data1 = with_embeddings(embed_documents, data, column = 'documents',show_progress = True, batch_size = 512)
table = db.create_table("doc_embed", data=data1) # create Table
"""# Search from a random Text"""
def search(query, top_k = 10):
"""
Search a query from the table
"""
query_vector = embed_model.encode_queries(query, task=task) # Encode the QUERY (it is done differently than the 'key')
search_results = table.search(query_vector).limit(top_k)
return ",".join(search_results.to_pandas().dropna(subset = "QueryText").reset_index(drop = True)["documents"].to_list())
# query = "how to control flower drop in bottelgourd?"
# print("QUERY:-> ", query)
# # get top_k search results
# search_results = search(query, top_k = 10).to_pandas().dropna(subset = "Query").reset_index(drop = True)["documents"]
# print(",".join(search_results.to_list))
# def rerank(query, search_results):
# search_results["old_similarity_rank"] = search_results.index+1 # Old ranks
# torch.cuda.empty_cache()
# gc.collect()
# search_results["new_scores"] = reranker_model.compute_score([[query,chunk] for chunk in search_results["text"]]) # Re compute ranks
# return search_results.sort_values(by = "new_scores", ascending = False).reset_index(drop = True)
# print("QUERY:-> ", query) | [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((356, 404), 'FlagEmbedding.LLMEmbedder', 'LLMEmbedder', (['"""BAAI/llm-embedder"""'], {'use_fp16': '(False)'}), "('BAAI/llm-embedder', use_fp16=False)\n", (367, 404), False, 'from FlagEmbedding import LLMEmbedder, FlagReranker\n'), ((463, 516), 'FlagEmbedding.FlagReranker', 'FlagReranker', (['"""BAAI/bge-reranker-base"""'], {'use_fp16': '(True)'}), "('BAAI/bge-reranker-base', use_fp16=True)\n", (475, 516), False, 'from FlagEmbedding import LLMEmbedder, FlagReranker\n'), ((803, 832), 'pandas.read_csv', 'pd.read_csv', (['"""Kcc_subset.csv"""'], {}), "('Kcc_subset.csv')\n", (814, 832), True, 'import pandas as pd\n'), ((1162, 1185), 'lancedb.connect', 'lancedb.connect', (['"""./db"""'], {}), "('./db')\n", (1177, 1185), False, 'import lancedb\n'), ((1370, 1469), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embed_documents', 'data'], {'column': '"""documents"""', 'show_progress': '(True)', 'batch_size': '(512)'}), "(embed_documents, data, column='documents', show_progress=\n True, batch_size=512)\n", (1385, 1469), False, 'from lancedb.embeddings import with_embeddings\n')] |
import time
import re
import shutil
import os
import urllib
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import PredictionGuard
import streamlit as st
from sentence_transformers import SentenceTransformer
import lancedb
from lancedb.embeddings import with_embeddings
import pandas as pd
#--------------------------#
# Prompt templates #
#--------------------------#
demo_formatter_template = """\nUser: {user}
Assistant: {assistant}\n"""
demo_prompt = PromptTemplate(
input_variables=["user", "assistant"],
template=demo_formatter_template,
)
category_template = """### Instruction:
Read the below input and determine if it is a request to generate computer code? Respond "yes" or "no".
### Input:
{query}
### Response:
"""
category_prompt = PromptTemplate(
input_variables=["query"],
template=category_template
)
qa_template = """### Instruction:
Read the context below and respond with an answer to the question. If the question cannot be answered based on the context alone or the context does not explicitly say the answer to the question, write "Sorry I had trouble answering this question, based on the information I found."
### Input:
Context: {context}
Question: {query}
### Response:
"""
qa_prompt = PromptTemplate(
input_variables=["context", "query"],
template=qa_template
)
chat_template = """### Instruction:
You are a friendly and clever AI assistant. Respond to the latest human message in the input conversation below.
### Input:
{context}
Human: {query}
AI:
### Response:
"""
chat_prompt = PromptTemplate(
input_variables=["context", "query"],
template=chat_template
)
code_template = """### Instruction:
You are a code generation assistant. Respond with a code snippet and any explanation requested in the below input.
### Input:
{query}
### Response:
"""
code_prompt = PromptTemplate(
input_variables=["query"],
template=code_template
)
#-------------------------#
# Vector search #
#-------------------------#
# Embeddings setup
name="all-MiniLM-L12-v2"
model = SentenceTransformer(name)
def embed_batch(batch):
return [model.encode(sentence) for sentence in batch]
def embed(sentence):
return model.encode(sentence)
# LanceDB setup
if os.path.exists(".lancedb"):
shutil.rmtree(".lancedb")
os.mkdir(".lancedb")
uri = ".lancedb"
db = lancedb.connect(uri)
def vector_search_urls(urls, query, sessionid):
for url in urls:
# Let's get the html off of a website.
fp = urllib.request.urlopen(url)
mybytes = fp.read()
html = mybytes.decode("utf8")
fp.close()
# And convert it to text.
h = html2text.HTML2Text()
h.ignore_links = True
text = h.handle(html)
# Chunk the text into smaller pieces for injection into LLM prompts.
text_splitter = CharacterTextSplitter(chunk_size=700, chunk_overlap=50)
docs = text_splitter.split_text(text)
docs = [x.replace('#', '-') for x in docs]
# Create a dataframe with the chunk ids and chunks
metadata = []
for i in range(len(docs)):
metadata.append([
i,
docs[i],
url
])
doc_df = pd.DataFrame(metadata, columns=["chunk", "text", "url"])
# Embed the documents
data = with_embeddings(embed_batch, doc_df)
# Create the table if there isn't one.
if sessionid not in db.table_names():
db.create_table(sessionid, data=data)
else:
table = db.open_table(sessionid)
table.add(data=data)
# Perform the query
table = db.open_table(sessionid)
results = table.search(embed(query)).limit(1).to_df()
results = results[results['_distance'] < 1.0]
if len(results) == 0:
doc_use = ""
else:
doc_use = results['text'].values[0]
# Clean up
db.drop_table(sessionid)
return doc_use
#-------------------------#
# Info Agent #
#-------------------------#
tools = load_tools(["serpapi"], llm=PredictionGuard(model="Nous-Hermes-Llama2-13B"))
agent = initialize_agent(
tools,
PredictionGuard(model="Nous-Hermes-Llama2-13B"),
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
verbose=True,
max_execution_time=30)
#-------------------------#
# Helper functions #
#-------------------------#
def find_urls(text):
return re.findall(r'(https?://[^\s]+)', text)
# QuestionID provides some help in determining if a sentence is a question.
class QuestionID:
"""
QuestionID has the actual logic used to determine if sentence is a question
"""
def padCharacter(self, character: str, sentence: str):
if character in sentence:
position = sentence.index(character)
if position > 0 and position < len(sentence):
# Check for existing white space before the special character.
if (sentence[position - 1]) != " ":
sentence = sentence.replace(character, (" " + character))
return sentence
def predict(self, sentence: str):
questionStarters = [
"which", "wont", "cant", "isnt", "arent", "is", "do", "does",
"will", "can"
]
questionElements = [
"who", "what", "when", "where", "why", "how", "sup", "?"
]
sentence = sentence.lower()
sentence = sentence.replace("\'", "")
sentence = self.padCharacter('?', sentence)
splitWords = sentence.split()
if any(word == splitWords[0] for word in questionStarters) or any(
word in splitWords for word in questionElements):
return True
else:
return False
#---------------------#
# Streamlit config #
#---------------------#
#st.set_page_config(layout="wide")
# Hide the hamburger menu
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
#--------------------------#
# Streamlit sidebar #
#--------------------------#
st.sidebar.title("Super Chat 🚀")
st.sidebar.markdown(
"This app provides a chat interface driven by various generative AI models and "
"augmented (via information retrieval and agentic processing)."
)
url_text = st.sidebar.text_area(
"Enter one or more urls for reference information (separated by a comma):",
"", height=100)
if len(url_text) > 0:
urls = url_text.split(",")
else:
urls = []
#--------------------------#
# Streamlit app #
#--------------------------#
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Hello?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
# process the context
examples = []
turn = "user"
example = {}
for m in st.session_state.messages:
latest_message = m["content"]
example[turn] = m["content"]
if turn == "user":
turn = "assistant"
else:
turn = "user"
examples.append(example)
example = {}
if len(example) > 4:
examples = examples[-4:]
# Determine what kind of message this is.
with st.spinner("Trying to figure out what you are wanting..."):
result = pg.Completion.create(
model="WizardCoder",
prompt=category_prompt.format(query=latest_message),
output={
"type": "categorical",
"categories": ["yes", "no"]
}
)
# configure out chain
code = result['choices'][0]['output']
qIDModel = QuestionID()
question = qIDModel.predict(latest_message)
if code == "no" and question:
# if there are urls, let's embed them as a primary data source.
if len(urls) > 0:
with st.spinner("Performing vector search..."):
info_context = vector_search_urls(urls, latest_message, "assistant")
else:
info_context = ""
# Handle the informational request.
if info_context != "":
with st.spinner("Generating a RAG result..."):
result = pg.Completion.create(
model="Nous-Hermes-Llama2-13B",
prompt=qa_prompt.format(context=info_context, query=latest_message)
)
completion = result['choices'][0]['text'].split('#')[0].strip()
# Otherwise try an agentic approach.
else:
with st.spinner("Trying to find an answer with an agent..."):
try:
completion = agent.run(latest_message)
except:
completion = "Sorry, I didn't find an answer. Could you rephrase the question?"
if "Agent stopped" in completion:
completion = "Sorry, I didn't find an answer. Could you rephrase the question?"
elif code == "yes":
# Handle the code generation request.
with st.spinner("Generating code..."):
result = pg.Completion.create(
model="WizardCoder",
prompt=code_prompt.format(query=latest_message),
max_tokens=500
)
completion = result['choices'][0]['text']
else:
# contruct prompt
few_shot_prompt = FewShotPromptTemplate(
examples=examples,
example_prompt=demo_prompt,
example_separator="",
prefix="The following is a conversation between an AI assistant and a human user. The assistant is helpful, creative, clever, and very friendly.\n",
suffix="\nHuman: {human}\nAssistant: ",
input_variables=["human"],
)
prompt = few_shot_prompt.format(human=latest_message)
# generate response
with st.spinner("Generating chat response..."):
result = pg.Completion.create(
model="Nous-Hermes-Llama2-13B",
prompt=prompt,
)
completion = result['choices'][0]['text']
# Print out the response.
completion = completion.split("Human:")[0].strip()
completion = completion.split("H:")[0].strip()
completion = completion.split('#')[0].strip()
for token in completion.split(" "):
full_response += " " + token
message_placeholder.markdown(full_response + "▌")
time.sleep(0.075)
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response}) | [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((728, 820), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['user', 'assistant']", 'template': 'demo_formatter_template'}), "(input_variables=['user', 'assistant'], template=\n demo_formatter_template)\n", (742, 820), False, 'from langchain import PromptTemplate, FewShotPromptTemplate\n'), ((1030, 1099), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['query']", 'template': 'category_template'}), "(input_variables=['query'], template=category_template)\n", (1044, 1099), False, 'from langchain import PromptTemplate, FewShotPromptTemplate\n'), ((1510, 1584), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['context', 'query']", 'template': 'qa_template'}), "(input_variables=['context', 'query'], template=qa_template)\n", (1524, 1584), False, 'from langchain import PromptTemplate, FewShotPromptTemplate\n'), ((1820, 1896), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['context', 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import logging
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Union
logger = logging.getLogger(__name__)
from hamilton import contrib
with contrib.catch_import_errors(__name__, __file__, logger):
import pyarrow as pa
import lancedb
import numpy as np
import pandas as pd
from lancedb.pydantic import LanceModel
from hamilton.function_modifiers import tag
VectorType = Union[list, np.ndarray, pa.Array, pa.ChunkedArray]
DataType = Union[Dict, List[Dict], pd.DataFrame, pa.Table, Iterable[pa.RecordBatch]]
TableSchema = Union[pa.Schema, LanceModel]
def client(uri: Union[str, Path] = "./.lancedb") -> lancedb.DBConnection:
"""Create a LanceDB connection.
:param uri: path to local LanceDB
:return: connection to LanceDB instance.
"""
return lancedb.connect(uri=uri)
def _create_table(
client: lancedb.DBConnection,
table_name: str,
schema: Optional[TableSchema] = None,
overwrite_table: bool = False,
) -> lancedb.db.LanceTable:
"""Create a new table based on schema."""
mode = "overwrite" if overwrite_table else "create"
table = client.create_table(name=table_name, schema=schema, mode=mode)
return table
@tag(side_effect="True")
def table_ref(
client: lancedb.DBConnection,
table_name: str,
schema: Optional[TableSchema] = None,
overwrite_table: bool = False,
) -> lancedb.db.LanceTable:
"""Create or reference a LanceDB table
:param vdb_client: LanceDB connection.
:param table_name: Name of the table.
:param schema: Pyarrow schema defining the table schema.
:param overwrite_table: If True, overwrite existing table
:return: Reference to existing or newly created table.
"""
try:
table = client.open_table(table_name)
except FileNotFoundError:
if schema is None:
raise ValueError("`schema` must be provided to create table.")
table = _create_table(
client=client,
table_name=table_name,
schema=schema,
overwrite_table=overwrite_table,
)
return table
@tag(side_effect="True")
def reset(client: lancedb.DBConnection) -> Dict[str, List[str]]:
"""Drop all existing tables.
:param vdb_client: LanceDB connection.
:return: dictionary containing all the dropped tables.
"""
tables_dropped = []
for table_name in client.table_names():
client.drop_table(table_name)
tables_dropped.append(table_name)
return dict(tables_dropped=tables_dropped)
@tag(side_effect="True")
def insert(table_ref: lancedb.db.LanceTable, data: DataType) -> Dict:
"""Push new data to the specified table.
:param table_ref: Reference to the LanceDB table.
:param data: Data to add to the table. Ref: https://lancedb.github.io/lancedb/guides/tables/#adding-to-a-table
:return: Reference to the table and number of rows added
"""
n_rows_before = table_ref.to_arrow().shape[0]
table_ref.add(data)
n_rows_after = table_ref.to_arrow().shape[0]
n_rows_added = n_rows_after - n_rows_before
return dict(table=table_ref, n_rows_added=n_rows_added)
@tag(side_effect="True")
def delete(table_ref: lancedb.db.LanceTable, delete_expression: str) -> Dict:
"""Delete existing data using an SQL expression.
:param table_ref: Reference to the LanceDB table.
:param data: Expression to select data. Ref: https://lancedb.github.io/lancedb/sql/
:return: Reference to the table and number of rows deleted
"""
n_rows_before = table_ref.to_arrow().shape[0]
table_ref.delete(delete_expression)
n_rows_after = table_ref.to_arrow().shape[0]
n_rows_deleted = n_rows_before - n_rows_after
return dict(table=table_ref, n_rows_deleted=n_rows_deleted)
def vector_search(
table_ref: lancedb.db.LanceTable,
vector_query: VectorType,
columns: Optional[List[str]] = None,
where: Optional[str] = None,
prefilter_where: bool = False,
limit: int = 10,
) -> pd.DataFrame:
"""Search database using an embedding vector.
:param table_ref: table to search
:param vector_query: embedding of the query
:param columns: columns to include in the results
:param where: SQL where clause to pre- or post-filter results
:param prefilter_where: If True filter rows before search else filter after search
:param limit: number of rows to return
:return: A dataframe of results
"""
query_ = (
table_ref.search(
query=vector_query,
query_type="vector",
vector_column_name="vector",
)
.select(columns=columns)
.where(where, prefilter=prefilter_where)
.limit(limit=limit)
)
return query_.to_pandas()
def full_text_search(
table_ref: lancedb.db.LanceTable,
full_text_query: str,
full_text_index: Union[str, List[str]],
where: Optional[str] = None,
limit: int = 10,
rebuild_index: bool = True,
) -> pd.DataFrame:
"""Search database using an embedding vector.
:param table_ref: table to search
:param full_text_query: text query
:param full_text_index: one or more text columns to search
:param where: SQL where clause to pre- or post-filter results
:param limit: number of rows to return
:param rebuild_index: If True rebuild the index
:return: A dataframe of results
"""
# NOTE. Currently, the index needs to be recreated whenever data is added
# ref: https://lancedb.github.io/lancedb/fts/#installation
if rebuild_index:
table_ref.create_fts_index(full_text_index)
query_ = (
table_ref.search(query=full_text_query, query_type="fts")
.select(full_text_index)
.where(where)
.limit(limit)
)
return query_.to_pandas()
| [
"lancedb.connect"
] | [((107, 134), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (124, 134), False, 'import logging\n'), ((1219, 1242), 'hamilton.function_modifiers.tag', 'tag', ([], {'side_effect': '"""True"""'}), "(side_effect='True')\n", (1222, 1242), False, 'from hamilton.function_modifiers import tag\n'), ((2122, 2145), 'hamilton.function_modifiers.tag', 'tag', ([], {'side_effect': '"""True"""'}), "(side_effect='True')\n", (2125, 2145), False, 'from hamilton.function_modifiers import tag\n'), ((2554, 2577), 'hamilton.function_modifiers.tag', 'tag', ([], {'side_effect': '"""True"""'}), "(side_effect='True')\n", (2557, 2577), False, 'from hamilton.function_modifiers import tag\n'), ((3166, 3189), 'hamilton.function_modifiers.tag', 'tag', ([], {'side_effect': '"""True"""'}), "(side_effect='True')\n", (3169, 3189), False, 'from hamilton.function_modifiers import tag\n'), ((171, 226), 'hamilton.contrib.catch_import_errors', 'contrib.catch_import_errors', (['__name__', '__file__', 'logger'], {}), '(__name__, __file__, logger)\n', (198, 226), False, 'from hamilton import contrib\n'), ((816, 840), 'lancedb.connect', 'lancedb.connect', ([], {'uri': 'uri'}), '(uri=uri)\n', (831, 840), False, 'import lancedb\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import importlib.metadata
import platform
import random
import sys
import time
from lancedb.utils import CONFIG
from lancedb.utils.general import TryExcept
from .general import (
PLATFORMS,
get_git_origin_url,
is_git_dir,
is_github_actions_ci,
is_online,
is_pip_package,
is_pytest_running,
threaded_request,
)
class _Events:
"""
A class for collecting anonymous event analytics. Event analytics are enabled when ``diagnostics=True`` in config and
disabled when ``diagnostics=False``.
You can enable or disable diagnostics by running ``lancedb diagnostics --enabled`` or ``lancedb diagnostics --disabled``.
Attributes
----------
url : str
The URL to send anonymous events.
rate_limit : float
The rate limit in seconds for sending events.
metadata : dict
A dictionary containing metadata about the environment.
enabled : bool
A flag to enable or disable Events based on certain conditions.
"""
_instance = None
url = "https://app.posthog.com/capture/"
headers = {"Content-Type": "application/json"}
api_key = "phc_oENDjGgHtmIDrV6puUiFem2RB4JA8gGWulfdulmMdZP"
# This api-key is write only and is safe to expose in the codebase.
def __init__(self):
"""
Initializes the Events object with default values for events, rate_limit, and metadata.
"""
self.events = [] # events list
self.throttled_event_names = ["search_table"]
self.throttled_events = set()
self.max_events = 5 # max events to store in memory
self.rate_limit = 60.0 * 5 # rate limit (seconds)
self.time = 0.0
if is_git_dir():
install = "git"
elif is_pip_package():
install = "pip"
else:
install = "other"
self.metadata = {
"cli": sys.argv[0],
"install": install,
"python": ".".join(platform.python_version_tuple()[:2]),
"version": importlib.metadata.version("lancedb"),
"platforms": PLATFORMS,
"session_id": round(random.random() * 1e15),
# 'engagement_time_msec': 1000 # TODO: In future we might be interested in this metric
}
TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
ONLINE = is_online()
self.enabled = (
CONFIG["diagnostics"]
and not TESTS_RUNNING
and ONLINE
and (
is_pip_package()
or get_git_origin_url() == "https://github.com/lancedb/lancedb.git"
)
)
def __call__(self, event_name, params={}):
"""
Attempts to add a new event to the events list and send events if the rate limit is reached.
Args
----
event_name : str
The name of the event to be logged.
params : dict, optional
A dictionary of additional parameters to be logged with the event.
"""
### NOTE: We might need a way to tag a session with a label to check usage from a source. Setting label should be exposed to the user.
if not self.enabled:
return
if (
len(self.events) < self.max_events
): # Events list limited to self.max_events (drop any events past this)
params.update(self.metadata)
event = {
"event": event_name,
"properties": params,
"timestamp": datetime.datetime.now(
tz=datetime.timezone.utc
).isoformat(),
"distinct_id": CONFIG["uuid"],
}
if event_name not in self.throttled_event_names:
self.events.append(event)
elif event_name not in self.throttled_events:
self.throttled_events.add(event_name)
self.events.append(event)
# Check rate limit
t = time.time()
if (t - self.time) < self.rate_limit:
return
# Time is over rate limiter, send now
data = {
"api_key": self.api_key,
"distinct_id": CONFIG["uuid"], # posthog needs this to accepts the event
"batch": self.events,
}
# POST equivalent to requests.post(self.url, json=data).
# threaded request is used to avoid blocking, retries are disabled, and verbose is disabled
# to avoid any possible disruption in the console.
threaded_request(
method="post",
url=self.url,
headers=self.headers,
json=data,
retry=0,
verbose=False,
)
# Flush & Reset
self.events = []
self.throttled_events = set()
self.time = t
@TryExcept(verbose=False)
def register_event(name: str, **kwargs):
if _Events._instance is None:
_Events._instance = _Events()
_Events._instance(name, **kwargs)
| [
"lancedb.utils.general.TryExcept"
] | [((5422, 5446), 'lancedb.utils.general.TryExcept', 'TryExcept', ([], {'verbose': '(False)'}), '(verbose=False)\n', (5431, 5446), False, 'from lancedb.utils.general import TryExcept\n'), ((4584, 4595), 'time.time', 'time.time', ([], {}), '()\n', (4593, 4595), False, 'import time\n'), ((2567, 2598), 'platform.python_version_tuple', 'platform.python_version_tuple', ([], {}), '()\n', (2596, 2598), False, 'import platform\n'), ((2735, 2750), 'random.random', 'random.random', ([], {}), '()\n', (2748, 2750), False, 'import random\n'), ((4127, 4174), 'datetime.datetime.now', 'datetime.datetime.now', ([], {'tz': 'datetime.timezone.utc'}), '(tz=datetime.timezone.utc)\n', (4148, 4174), False, 'import datetime\n')] |
import argparse
import os
import sys
from concurrent.futures import ProcessPoolExecutor, as_completed
from functools import lru_cache
from pathlib import Path
from typing import Any, Iterator
import lancedb
import pandas as pd
import srsly
from codetiming import Timer
from dotenv import load_dotenv
from lancedb.pydantic import pydantic_to_schema
from sentence_transformers import SentenceTransformer
from tqdm import tqdm
sys.path.insert(1, os.path.realpath(Path(__file__).resolve().parents[1]))
from api.config import Settings
from schemas.wine import LanceModelWine, Wine
load_dotenv()
# Custom types
JsonBlob = dict[str, Any]
class FileNotFoundError(Exception):
pass
@lru_cache()
def get_settings():
# Use lru_cache to avoid loading .env file for every request
return Settings()
def chunk_iterable(item_list: list[JsonBlob], chunksize: int) -> Iterator[list[JsonBlob]]:
"""
Break a large iterable into an iterable of smaller iterables of size `chunksize`
"""
for i in range(0, len(item_list), chunksize):
yield item_list[i : i + chunksize]
def get_json_data(data_dir: Path, filename: str) -> list[JsonBlob]:
"""Get all line-delimited json files (.jsonl) from a directory with a given prefix"""
file_path = data_dir / filename
if not file_path.is_file():
# File may not have been uncompressed yet so try to do that first
data = srsly.read_gzip_jsonl(file_path)
# This time if it isn't there it really doesn't exist
if not file_path.is_file():
raise FileNotFoundError(f"No valid .jsonl file found in `{data_dir}`")
else:
data = srsly.read_gzip_jsonl(file_path)
return data
def validate(
data: list[JsonBlob],
exclude_none: bool = False,
) -> list[JsonBlob]:
validated_data = [Wine(**item).model_dump(exclude_none=exclude_none) for item in data]
return validated_data
def embed_func(batch: list[str], model) -> list[list[float]]:
return [model.encode(sentence.lower()) for sentence in batch]
def vectorize_text(data: list[JsonBlob]) -> list[LanceModelWine] | None:
# Load a sentence transformer model for semantic similarity from a specified checkpoint
model_id = get_settings().embedding_model_checkpoint
assert model_id, "Invalid embedding model checkpoint specified in .env file"
MODEL = SentenceTransformer(model_id)
ids = [item["id"] for item in data]
to_vectorize = [text.get("to_vectorize") for text in data]
vectors = embed_func(to_vectorize, MODEL)
try:
data_batch = [{**d, "vector": vector} for d, vector in zip(data, vectors)]
except Exception as e:
print(f"{e}: Failed to add ID range {min(ids)}-{max(ids)}")
return None
return data_batch
def embed_batches(tbl: str, validated_data: list[JsonBlob]) -> pd.DataFrame:
with ProcessPoolExecutor(max_workers=WORKERS) as executor:
chunked_data = chunk_iterable(validated_data, CHUNKSIZE)
embed_data = []
for chunk in tqdm(chunked_data, total=len(validated_data) // CHUNKSIZE):
futures = [executor.submit(vectorize_text, chunk)]
embed_data = [f.result() for f in as_completed(futures) if f.result()][0]
df = pd.DataFrame.from_dict(embed_data)
tbl.add(df, mode="overwrite")
def main(data: list[JsonBlob]) -> None:
DB_NAME = f"../{get_settings().lancedb_dir}"
TABLE = "wines"
db = lancedb.connect(DB_NAME)
tbl = db.create_table(TABLE, schema=pydantic_to_schema(LanceModelWine), mode="overwrite")
print(f"Created table `{TABLE}`, with length {len(tbl)}")
with Timer(name="Bulk Index", text="Validated data using Pydantic in {:.4f} sec"):
validated_data = validate(data, exclude_none=False)
with Timer(name="Embed batches", text="Created sentence embeddings in {:.4f} sec"):
embed_batches(tbl, validated_data)
print(f"Finished inserting {len(tbl)} items into LanceDB table")
with Timer(name="Create index", text="Created IVF-PQ index in {:.4f} sec"):
# Creating index (choose num partitions as a power of 2 that's closest to len(dataset) // 5000)
# In this case, we have 130k datapoints, so the nearest power of 2 is 130000//5000 ~ 32)
tbl.create_index(metric="cosine", num_partitions=4, num_sub_vectors=32)
if __name__ == "__main__":
# fmt: off
parser = argparse.ArgumentParser("Bulk index database from the wine reviews JSONL data")
parser.add_argument("--limit", type=int, default=0, help="Limit the size of the dataset to load for testing purposes")
parser.add_argument("--chunksize", type=int, default=1000, help="Size of each chunk to break the dataset into before processing")
parser.add_argument("--filename", type=str, default="winemag-data-130k-v2.jsonl.gz", help="Name of the JSONL zip file to use")
parser.add_argument("--workers", type=int, default=4, help="Number of workers to use for vectorization")
args = vars(parser.parse_args())
# fmt: on
LIMIT = args["limit"]
DATA_DIR = Path(__file__).parents[3] / "data"
FILENAME = args["filename"]
CHUNKSIZE = args["chunksize"]
WORKERS = args["workers"]
data = list(get_json_data(DATA_DIR, FILENAME))
assert data, "No data found in the specified file"
data = data[:LIMIT] if LIMIT > 0 else data
main(data)
print("Finished execution!")
| [
"lancedb.connect",
"lancedb.pydantic.pydantic_to_schema"
] | [((580, 593), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (591, 593), False, 'from dotenv import load_dotenv\n'), ((685, 696), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (694, 696), False, 'from functools import lru_cache\n'), ((793, 803), 'api.config.Settings', 'Settings', ([], {}), '()\n', (801, 803), False, 'from api.config import Settings\n'), ((2355, 2384), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['model_id'], {}), '(model_id)\n', (2374, 2384), False, 'from sentence_transformers import SentenceTransformer\n'), ((3439, 3463), 'lancedb.connect', 'lancedb.connect', (['DB_NAME'], {}), '(DB_NAME)\n', (3454, 3463), False, 'import lancedb\n'), ((4390, 4469), 'argparse.ArgumentParser', 'argparse.ArgumentParser', (['"""Bulk index database from the wine reviews JSONL data"""'], {}), "('Bulk index database from the wine reviews JSONL data')\n", (4413, 4469), False, 'import argparse\n'), ((1408, 1440), 'srsly.read_gzip_jsonl', 'srsly.read_gzip_jsonl', (['file_path'], {}), '(file_path)\n', (1429, 1440), False, 'import srsly\n'), ((1647, 1679), 'srsly.read_gzip_jsonl', 'srsly.read_gzip_jsonl', (['file_path'], {}), '(file_path)\n', (1668, 1679), False, 'import srsly\n'), ((2852, 2892), 'concurrent.futures.ProcessPoolExecutor', 'ProcessPoolExecutor', ([], {'max_workers': 'WORKERS'}), '(max_workers=WORKERS)\n', (2871, 2892), False, 'from concurrent.futures import ProcessPoolExecutor, as_completed\n'), ((3631, 3707), 'codetiming.Timer', 'Timer', ([], {'name': '"""Bulk Index"""', 'text': '"""Validated data using Pydantic in {:.4f} sec"""'}), "(name='Bulk Index', text='Validated data using Pydantic in {:.4f} sec')\n", (3636, 3707), False, 'from codetiming import Timer\n'), ((3779, 3856), 'codetiming.Timer', 'Timer', ([], {'name': '"""Embed batches"""', 'text': '"""Created sentence embeddings in {:.4f} sec"""'}), "(name='Embed batches', text='Created sentence embeddings in {:.4f} sec')\n", (3784, 3856), False, 'from codetiming import Timer\n'), ((3981, 4050), 'codetiming.Timer', 'Timer', ([], {'name': '"""Create index"""', 'text': '"""Created IVF-PQ index in {:.4f} sec"""'}), "(name='Create index', text='Created IVF-PQ index in {:.4f} sec')\n", (3986, 4050), False, 'from codetiming import Timer\n'), ((3242, 3276), 'pandas.DataFrame.from_dict', 'pd.DataFrame.from_dict', (['embed_data'], {}), '(embed_data)\n', (3264, 3276), True, 'import pandas as pd\n'), ((3505, 3539), 'lancedb.pydantic.pydantic_to_schema', 'pydantic_to_schema', (['LanceModelWine'], {}), '(LanceModelWine)\n', (3523, 3539), False, 'from lancedb.pydantic import pydantic_to_schema\n'), ((1813, 1825), 'schemas.wine.Wine', 'Wine', ([], {}), '(**item)\n', (1817, 1825), False, 'from schemas.wine import LanceModelWine, Wine\n'), ((5060, 5074), 'pathlib.Path', 'Path', (['__file__'], {}), '(__file__)\n', (5064, 5074), False, 'from pathlib import Path\n'), ((462, 476), 'pathlib.Path', 'Path', (['__file__'], {}), '(__file__)\n', (466, 476), False, 'from pathlib import Path\n'), ((3185, 3206), 'concurrent.futures.as_completed', 'as_completed', (['futures'], {}), '(futures)\n', (3197, 3206), False, 'from concurrent.futures import ProcessPoolExecutor, as_completed\n')] |
import os
import time
import shutil
import pandas as pd
import lancedb
from lancedb.embeddings import with_embeddings
from langchain import PromptTemplate
import predictionguard as pg
import numpy as np
from sentence_transformers import SentenceTransformer
#---------------------#
# Lance DB Setup #
#---------------------#
#Import datasets
#JOBS
df1=pd.read_csv('datasets/jobs.csv')
df1_table_name = "jobs"
#SOCIAL
df2=pd.read_csv('datasets/social.csv')
df2_table_name = "social"
#movies
df3=pd.read_csv('datasets/movies.csv')
df3_table_name = "movies"
# local path of the vector db
uri = "schema.lancedb"
db = lancedb.connect(uri)
# Embeddings setup
name="all-MiniLM-L12-v2"
# Load model
def load_model():
return SentenceTransformer(name)
def embed(query, embModel):
return embModel.encode(query)
#---------------------#
# SQL Schema Creation #
#---------------------#
def create_schema(df,table_name):
# Here we will create an example SQL schema based on the data in this dataset.
# In a real use case, you likely already have this sort of CREATE TABLE statement.
# Performance can be improved by manually curating the descriptions.
columns_info = []
# Iterate through each column in the DataFrame
for col in df.columns:
# Determine the SQL data type based on the first non-null value in the column
first_non_null = df[col].dropna().iloc[0]
if isinstance(first_non_null, np.int64):
kind = "INTEGER"
elif isinstance(first_non_null, np.float64):
kind = "DECIMAL(10,2)"
elif isinstance(first_non_null, str):
kind = "VARCHAR(255)" # Assuming a default max length of 255
else:
kind = "VARCHAR(255)" # Default to VARCHAR for other types or customize as needed
# Sample a few example values
example_values = ', '.join([str(x) for x in df[col].dropna().unique()[0:4]])
# Append column info to the list
columns_info.append(f"{col} {kind}, -- Example values are {example_values}")
# Construct the CREATE TABLE statement
create_table_statement = "CREATE TABLE" + " " + table_name + " (\n " + ",\n ".join(columns_info) + "\n);"
# Adjust the statement to handle the final comma, primary keys, or other specifics
create_table_statement = create_table_statement.replace(",\n);", "\n);")
return create_table_statement
# SQL Schema for Table Jobs
df1_schema=create_schema(df1,df1_table_name)
# SQL Schema for Table Social
df2_schema=create_schema(df2,df2_table_name)
# SQL Schema for Table Movies
df3_schema=create_schema(df3,df3_table_name)
#---------------------#
# Prompt Templates #
#---------------------#
template="""
###System:
Generate a brief description of the below data. Be as detailed as possible.
###User:
{schema}
###Assistant:
"""
prompt=PromptTemplate(template=template,input_variables=["schema"])
#---------------------#
# Generate Description #
#---------------------#
def generate_description(schema):
prompt_filled=prompt.format(schema=schema)
result=pg.Completion.create(
model="Neural-Chat-7B",
prompt=prompt_filled,
temperature=0.1,
max_tokens=300
)
return result['choices'][0]['text']
df1_desc=generate_description(df1_schema)
df2_desc=generate_description(df2_schema)
df3_desc=generate_description(df3_schema)
# Create Pandas DataFrame
df = pd.DataFrame({
'text': [df1_desc, df2_desc, df3_desc],
'table_name': [df1_table_name, df2_table_name, df3_table_name],
'schema': [df1_schema, df2_schema, df3_schema],
})
print(df)
def load_data():
if os.path.exists("schema.lancedb"):
shutil.rmtree("schema.lancedb")
os.mkdir("schema.lancedb")
db = lancedb.connect(uri)
batchModel = SentenceTransformer(name)
def batch_embed_func(batch):
return [batchModel.encode(sentence) for sentence in batch]
vecData = with_embeddings(batch_embed_func, df)
if "schema" not in db.table_names():
db.create_table("schema", data=vecData)
else:
table = db.open_table("schema")
table.add(data=vecData)
return
load_data()
print("Done") | [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((359, 391), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/jobs.csv"""'], {}), "('datasets/jobs.csv')\n", (370, 391), True, 'import pandas as pd\n'), ((429, 463), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/social.csv"""'], {}), "('datasets/social.csv')\n", (440, 463), True, 'import pandas as pd\n'), ((503, 537), 'pandas.read_csv', 'pd.read_csv', (['"""datasets/movies.csv"""'], {}), "('datasets/movies.csv')\n", (514, 537), True, 'import pandas as pd\n'), ((623, 643), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (638, 643), False, 'import lancedb\n'), ((2866, 2927), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'template', 'input_variables': "['schema']"}), "(template=template, input_variables=['schema'])\n", (2880, 2927), False, 'from langchain import PromptTemplate\n'), ((3432, 3607), 'pandas.DataFrame', 'pd.DataFrame', (["{'text': [df1_desc, df2_desc, df3_desc], 'table_name': [df1_table_name,\n df2_table_name, df3_table_name], 'schema': [df1_schema, df2_schema,\n df3_schema]}"], {}), "({'text': [df1_desc, df2_desc, df3_desc], 'table_name': [\n df1_table_name, df2_table_name, df3_table_name], 'schema': [df1_schema,\n df2_schema, df3_schema]})\n", (3444, 3607), True, 'import pandas as pd\n'), ((732, 757), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['name'], {}), '(name)\n', (751, 757), False, 'from sentence_transformers import SentenceTransformer\n'), ((3095, 3198), 'predictionguard.Completion.create', 'pg.Completion.create', ([], {'model': '"""Neural-Chat-7B"""', 'prompt': 'prompt_filled', 'temperature': '(0.1)', 'max_tokens': '(300)'}), "(model='Neural-Chat-7B', prompt=prompt_filled,\n temperature=0.1, max_tokens=300)\n", (3115, 3198), True, 'import predictionguard as pg\n'), ((3651, 3683), 'os.path.exists', 'os.path.exists', (['"""schema.lancedb"""'], {}), "('schema.lancedb')\n", (3665, 3683), False, 'import os\n'), ((3729, 3755), 'os.mkdir', 'os.mkdir', (['"""schema.lancedb"""'], {}), "('schema.lancedb')\n", (3737, 3755), False, 'import os\n'), ((3765, 3785), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3780, 3785), False, 'import lancedb\n'), ((3808, 3833), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['name'], {}), '(name)\n', (3827, 3833), False, 'from sentence_transformers import SentenceTransformer\n'), ((3953, 3990), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['batch_embed_func', 'df'], {}), '(batch_embed_func, df)\n', (3968, 3990), False, 'from lancedb.embeddings import with_embeddings\n'), ((3693, 3724), 'shutil.rmtree', 'shutil.rmtree', (['"""schema.lancedb"""'], {}), "('schema.lancedb')\n", (3706, 3724), False, 'import shutil\n')] |
import typer
import openai
from rag_app.models import TextChunk
from lancedb import connect
from typing import List
from pathlib import Path
from rich.console import Console
from rich.table import Table
from rich import box
import duckdb
app = typer.Typer()
@app.command(help="Query LanceDB for some results")
def db(
db_path: str = typer.Option(help="Your LanceDB path"),
table_name: str = typer.Option(help="Table to ingest data into"),
query: str = typer.Option(help="Text to query against existing vector db chunks"),
n: int = typer.Option(default=3, help="Maximum number of chunks to return"),
):
if not Path(db_path).exists():
raise ValueError(f"Database path {db_path} does not exist.")
db = connect(db_path)
db_table = db.open_table(table_name)
client = openai.OpenAI()
query_vector = (
client.embeddings.create(
input=query, model="text-embedding-3-large", dimensions=256
)
.data[0]
.embedding
)
results: List[TextChunk] = (
db_table.search(query_vector).limit(n).to_pydantic(TextChunk)
)
sql_table = db_table.to_lance()
df = duckdb.query(
"SELECT doc_id, count(chunk_id) as count FROM sql_table GROUP BY doc_id"
).to_df()
doc_ids = df["doc_id"].to_list()
counts = df["count"].to_list()
doc_id_to_count = {id: chunk_count for id, chunk_count in zip(doc_ids, counts)}
table = Table(title="Results", box=box.HEAVY, padding=(1, 2), show_lines=True)
table.add_column("Post Title", style="green", max_width=30)
table.add_column("Content", style="magenta", max_width=120)
table.add_column("Chunk Number", style="yellow")
table.add_column("Publish Date", style="blue")
for result in results:
chunk_number = f"{result.chunk_id}"
table.add_row(
f"{result.post_title}({result.source})",
result.text,
f"{chunk_number}/{doc_id_to_count[result.doc_id]}",
result.publish_date.strftime("%Y-%m"),
)
Console().print(table)
| [
"lancedb.connect"
] | [((245, 258), 'typer.Typer', 'typer.Typer', ([], {}), '()\n', (256, 258), False, 'import typer\n'), ((340, 378), 'typer.Option', 'typer.Option', ([], {'help': '"""Your LanceDB path"""'}), "(help='Your LanceDB path')\n", (352, 378), False, 'import typer\n'), ((402, 448), 'typer.Option', 'typer.Option', ([], {'help': '"""Table to ingest data into"""'}), "(help='Table to ingest data into')\n", (414, 448), False, 'import typer\n'), ((467, 535), 'typer.Option', 'typer.Option', ([], {'help': '"""Text to query against existing vector db chunks"""'}), "(help='Text to query against existing vector db chunks')\n", (479, 535), False, 'import typer\n'), ((550, 616), 'typer.Option', 'typer.Option', ([], {'default': '(3)', 'help': '"""Maximum number of chunks to return"""'}), "(default=3, help='Maximum number of chunks to return')\n", (562, 616), False, 'import typer\n'), ((734, 750), 'lancedb.connect', 'connect', (['db_path'], {}), '(db_path)\n', (741, 750), False, 'from lancedb import connect\n'), ((806, 821), 'openai.OpenAI', 'openai.OpenAI', ([], {}), '()\n', (819, 821), False, 'import openai\n'), ((1437, 1507), 'rich.table.Table', 'Table', ([], {'title': '"""Results"""', 'box': 'box.HEAVY', 'padding': '(1, 2)', 'show_lines': '(True)'}), "(title='Results', box=box.HEAVY, padding=(1, 2), show_lines=True)\n", (1442, 1507), False, 'from rich.table import Table\n'), ((1157, 1248), 'duckdb.query', 'duckdb.query', (['"""SELECT doc_id, count(chunk_id) as count FROM sql_table GROUP BY doc_id"""'], {}), "(\n 'SELECT doc_id, count(chunk_id) as count FROM sql_table GROUP BY doc_id')\n", (1169, 1248), False, 'import duckdb\n'), ((2042, 2051), 'rich.console.Console', 'Console', ([], {}), '()\n', (2049, 2051), False, 'from rich.console import Console\n'), ((632, 645), 'pathlib.Path', 'Path', (['db_path'], {}), '(db_path)\n', (636, 645), False, 'from pathlib import Path\n')] |
import typer
from lancedb import connect
from rag_app.models import TextChunk, Document
from pathlib import Path
from typing import Iterable
from tqdm import tqdm
from rich import print
import frontmatter
import hashlib
from datetime import datetime
from unstructured.partition.text import partition_text
app = typer.Typer()
def read_files(path: Path, file_suffix: str) -> Iterable[Document]:
for i, file in enumerate(path.iterdir()):
if file.suffix != file_suffix:
continue
post = frontmatter.load(file)
yield Document(
id=hashlib.md5(post.content.encode("utf-8")).hexdigest(),
content=post.content,
filename=file.name,
metadata=post.metadata,
)
def batch_chunks(chunks, batch_size=20):
batch = []
for chunk in chunks:
batch.append(chunk)
if len(batch) == batch_size:
yield batch
batch = []
if batch:
yield batch
def chunk_text(
documents: Iterable[Document], window_size: int = 1024, overlap: int = 0
):
for doc in documents:
for chunk_num, chunk in enumerate(partition_text(text=doc.content)):
yield {
"doc_id": doc.id,
"chunk_id": chunk_num + 1,
"text": chunk.text,
"post_title": doc.metadata["title"],
"publish_date": datetime.strptime(doc.metadata["date"], "%Y-%m"),
"source": doc.metadata["url"],
}
@app.command(help="Ingest data into a given lancedb")
def from_folder(
db_path: str = typer.Option(help="Your LanceDB path"),
table_name: str = typer.Option(help="Table to ingest data into"),
folder_path: str = typer.Option(help="Folder to read data from"),
file_suffix: str = typer.Option(default=".md", help="File suffix to filter by"),
):
db = connect(db_path)
if table_name not in db.table_names():
db.create_table(table_name, schema=TextChunk, mode="overwrite")
table = db.open_table(table_name)
path = Path(folder_path)
if not path.exists():
raise ValueError(f"Ingestion folder of {folder_path} does not exist")
files = read_files(path, file_suffix)
chunks = chunk_text(files)
batched_chunks = batch_chunks(chunks)
ttl = 0
for chunk_batch in tqdm(batched_chunks):
table.add(chunk_batch)
ttl += len(chunk_batch)
print(f"Added {ttl} chunks to {table_name}")
| [
"lancedb.connect"
] | [((312, 325), 'typer.Typer', 'typer.Typer', ([], {}), '()\n', (323, 325), False, 'import typer\n'), ((1598, 1636), 'typer.Option', 'typer.Option', ([], {'help': '"""Your LanceDB path"""'}), "(help='Your LanceDB path')\n", (1610, 1636), False, 'import typer\n'), ((1660, 1706), 'typer.Option', 'typer.Option', ([], {'help': '"""Table to ingest data into"""'}), "(help='Table to ingest data into')\n", (1672, 1706), False, 'import typer\n'), ((1731, 1776), 'typer.Option', 'typer.Option', ([], {'help': '"""Folder to read data from"""'}), "(help='Folder to read data from')\n", (1743, 1776), False, 'import typer\n'), ((1801, 1861), 'typer.Option', 'typer.Option', ([], {'default': '""".md"""', 'help': '"""File suffix to filter by"""'}), "(default='.md', help='File suffix to filter by')\n", (1813, 1861), False, 'import typer\n'), ((1875, 1891), 'lancedb.connect', 'connect', (['db_path'], {}), '(db_path)\n', (1882, 1891), False, 'from lancedb import connect\n'), ((2058, 2075), 'pathlib.Path', 'Path', (['folder_path'], {}), '(folder_path)\n', (2062, 2075), False, 'from pathlib import Path\n'), ((2333, 2353), 'tqdm.tqdm', 'tqdm', (['batched_chunks'], {}), '(batched_chunks)\n', (2337, 2353), False, 'from tqdm import tqdm\n'), ((2423, 2467), 'rich.print', 'print', (['f"""Added {ttl} chunks to {table_name}"""'], {}), "(f'Added {ttl} chunks to {table_name}')\n", (2428, 2467), False, 'from rich import print\n'), ((517, 539), 'frontmatter.load', 'frontmatter.load', (['file'], {}), '(file)\n', (533, 539), False, 'import frontmatter\n'), ((1142, 1174), 'unstructured.partition.text.partition_text', 'partition_text', ([], {'text': 'doc.content'}), '(text=doc.content)\n', (1156, 1174), False, 'from unstructured.partition.text import partition_text\n'), ((1395, 1443), 'datetime.datetime.strptime', 'datetime.strptime', (["doc.metadata['date']", '"""%Y-%m"""'], {}), "(doc.metadata['date'], '%Y-%m')\n", (1412, 1443), False, 'from datetime import datetime\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import click
from lancedb.utils import CONFIG
@click.group()
@click.version_option(help="LanceDB command line interface entry point")
def cli():
"LanceDB command line interface"
diagnostics_help = """
Enable or disable LanceDB diagnostics. When enabled, LanceDB will send anonymous events to help us improve LanceDB.
These diagnostics are used only for error reporting and no data is collected. You can find more about diagnosis on
our docs: https://lancedb.github.io/lancedb/cli_config/
"""
@cli.command(help=diagnostics_help)
@click.option("--enabled/--disabled", default=True)
def diagnostics(enabled):
CONFIG.update({"diagnostics": True if enabled else False})
click.echo("LanceDB diagnostics is %s" % ("enabled" if enabled else "disabled"))
@cli.command(help="Show current LanceDB configuration")
def config():
# TODO: pretty print as table with colors and formatting
click.echo("Current LanceDB configuration:")
cfg = CONFIG.copy()
cfg.pop("uuid") # Don't show uuid as it is not configurable
for item, amount in cfg.items():
click.echo("{} ({})".format(item, amount))
| [
"lancedb.utils.CONFIG.copy",
"lancedb.utils.CONFIG.update"
] | [((641, 654), 'click.group', 'click.group', ([], {}), '()\n', (652, 654), False, 'import click\n'), ((656, 727), 'click.version_option', 'click.version_option', ([], {'help': '"""LanceDB command line interface entry point"""'}), "(help='LanceDB command line interface entry point')\n", (676, 727), False, 'import click\n'), ((1131, 1181), 'click.option', 'click.option', (['"""--enabled/--disabled"""'], {'default': '(True)'}), "('--enabled/--disabled', default=True)\n", (1143, 1181), False, 'import click\n'), ((1212, 1270), 'lancedb.utils.CONFIG.update', 'CONFIG.update', (["{'diagnostics': True if enabled else False}"], {}), "({'diagnostics': True if enabled else False})\n", (1225, 1270), False, 'from lancedb.utils import CONFIG\n'), ((1275, 1360), 'click.echo', 'click.echo', (["('LanceDB diagnostics is %s' % ('enabled' if enabled else 'disabled'))"], {}), "('LanceDB diagnostics is %s' % ('enabled' if enabled else 'disabled')\n )\n", (1285, 1360), False, 'import click\n'), ((1493, 1537), 'click.echo', 'click.echo', (['"""Current LanceDB configuration:"""'], {}), "('Current LanceDB configuration:')\n", (1503, 1537), False, 'import click\n'), ((1548, 1561), 'lancedb.utils.CONFIG.copy', 'CONFIG.copy', ([], {}), '()\n', (1559, 1561), False, 'from lancedb.utils import CONFIG\n')] |
import json
from sentence_transformers import SentenceTransformer
from pydantic.main import ModelMetaclass
from pathlib import Path
import pandas as pd
import sqlite3
from uuid import uuid4
import lancedb
encoder = SentenceTransformer('all-MiniLM-L6-v2')
data_folder = Path('data/collections')
config_file = Path('data/config/indexes.yaml')
index_folder = Path('indexes')
lance_folder = Path('indexes')
lance_folder.mkdir(parents=True, exist_ok=True)
sqlite_folder = Path('data/indexes/')
with sqlite3.connect(sqlite_folder.joinpath('documents.sqlite')) as conn:
cursor = conn.cursor()
cursor.execute('SELECT SQLITE_VERSION()')
data = cursor.fetchone()
print(f"Sqlite version: {data}")
class LanceDBDocument():
def __init__(self, document:dict, title:str, text:str, fields, tags=None, date=None, file_path=None):
self.document = self.fill_missing_fields(document, text, title, tags, date)
# self.text = document[text]
# self.tags = document[tags] if tags is not None else list()
# self.date = document[date] if date is not None else None
self.file_path = file_path
self.metadata = {k:document[k] for k in fields if k not in [title, text, tags, date]}
self.uuid = str(uuid4()) if 'uuid' not in document else document['uuid']
self.save_uuids = list()
self.sqlite_fields = list()
self.lance_exclude = list()
def fill_missing_fields(self, document, text, title, tags, date):
if title not in document:
self.title = ''
else:
self.title = document[title]
if text not in document:
self.text = ''
else:
self.text = document[text]
if date not in document:
self.date = ''
else:
self.date = document[date]
if tags not in document:
self.tags = list()
else:
self.tags = document[tags]
def create_json_document(self, text, uuids=None):
"""Creates a custom dictionary object that can be used for both sqlite and lancedb
The full document is always stored in sqlite where fixed fields are:
title
text
date
filepath
document_uuid - used for retrieval from lancedb results
Json field contains the whole document for retrieval and display
Lancedb only gets searching text, vectorization of that, and filter fields
"""
_document = {'title':self.title,
'text':text,
'tags':self.tags,
'date':self.date,
'file_path':str(self.file_path),
'uuid':self.uuid,
'metadata': self.metadata}
self._enforce_tags_schema()
for field in ['title','date','file_path']:
self.enforce_string_schema(field, _document)
return _document
def enforce_string_schema(self, field, test_document):
if not isinstance(test_document[field], str):
self.lance_exclude.append(field)
def _enforce_tags_schema(self):
# This enforces a simple List[str] format for the tags to match what lancedb can use for filtering
# If they are of type List[Dict] as a nested field, they are stored in sqlite for retrieval
if isinstance(self.tags, list):
tags_are_list = True
for _tag in self.tags:
if not isinstance(_tag, str):
tags_are_list = False
break
if not tags_are_list:
self.lance_exclude.append('tags')
def return_document(self):
document = self.create_json_document(self.text)
return document
class SqlLiteIngestNotes():
def __init__(self, documents, source_file, db_location, index_name, overwrite):
self.documents = documents
self.source_file = source_file
self.db_location = db_location
self.index_name = index_name
self.overwrite = overwrite
def initialize(self):
self.connection = sqlite3.connect(self.db_location)
if self.overwrite:
self.connection.execute(f"""DROP TABLE IF EXISTS {self.index_name};""")
self.connection.commit()
table_exists = self.connection.execute(f"SELECT name FROM sqlite_master WHERE type='table' AND name='{self.index_name}';").fetchall()
if len(table_exists) == 0:
self.connection.execute(f"""
CREATE TABLE {self.index_name}(
id INTEGER PRIMARY KEY NOT NULL,
uuid STRING NOT NULL UNIQUE,
text STRING NOT NULL,
title STRING,
date STRING,
source_file STRING,
metadata JSONB);""")
self.connection.commit()
def insert(self, document):
self.connection.execute(f"""INSERT OR IGNORE INTO
{self.index_name} (uuid, text, title, date, source_file, metadata)
VALUES ('{document.uuid.replace("'","''")}', '{document.text.replace("'","''")}',
'{document.title.replace("'","''")}', '{document.date.replace("'","''")}',
'{self.index_name.replace("'","''")}', '{json.dumps(document.metadata).replace("'","''")}');""")
def bulk_insert(self):
for document in self.documents:
self.insert(document)
self.connection.commit()
self.connection.close()
from lancedb.pydantic import LanceModel, Vector, List
class LanceDBSchema384(LanceModel):
uuid: str
text: str
title: str
tags: List[str]
vector: Vector(384)
class LanceDBSchema512(LanceModel):
uuid: str
text: str
title: str
tags: List[str]
vector: Vector(512)
class LanceDBIngestNotes():
def __init__(self, documents, lance_location, index_name, overwrite, encoder, schema):
self.documents = documents
self.lance_location = lance_location
self.index_name = index_name
self.overwrite = overwrite
self.encoder = encoder
self.schema = schema
def initialize(self):
self.db = lancedb.connect(self.lance_location)
existing_tables = self.db.table_names()
self.documents = [self.prep_documents(document) for document in self.documents]
if self.overwrite:
self.table = self.db.create_table(self.index_name, data=self.documents, mode='overwrite', schema=self.schema.to_arrow_schema())
else:
if self.index_name in existing_tables:
self.table = self.db.open_table(self.index_name)
self.table.add(self.documents)
else:
self.table = self.db.create_table(self.index_name, data=self.documents, schema=self.schema.to_arrow_schema())
def prep_documents(self, document):
lance_document = dict()
lance_document['text'] = document.text
lance_document['vector'] = self.encoder.encode(document.text)
lance_document['uuid'] = document.uuid
lance_document['title'] = document.title
lance_document['tags'] = document.tags
return lance_document
def insert(self, document):
document['vector'] = self.encoder.encode(document.text)
self.table.add(document)
def bulk_insert(self, create_vectors=False):
if create_vectors:
self.table.create_index(vector_column_name='vector', metric='cosine')
self.table.create_fts_index(field_names=['title','text'], replace=True)
return self.table
class IndexDocumentsNotes():
def __init__(self,field_mapping, source_file, index_name, overwrite):
self.field_mapping = field_mapping
self.source_file = source_file
self.index_name = index_name
self.overwrite = overwrite
def open_json(self):
with open(self.source_file, 'r') as f:
self.data = json.load(f)
print(self.data)
def open_csv(self):
self.data = pd.read_csv(self.source_file)
def create_document(self, document):
document = LanceDBDocument(document,
text=self.field_mapping['text'],
title=self.field_mapping['title'],
tags=self.field_mapping['tags'],
date=self.field_mapping['date'],
fields=list(document.keys()),
file_path=self.source_file
)
return document
def create_documents(self):
self.documents = [self.create_document(document) for document in self.data]
def ingest(self, overwrite=False):
# lance_path = Path(f'../indexes/lance')
lance_folder.mkdir(parents=True, exist_ok=True)
lance_ingest = LanceDBIngestNotes(documents=self.documents,
lance_location=lance_folder,
# field_mapping=self.field_mapping,
index_name=self.index_name,
overwrite=self.overwrite,
encoder=encoder,
schema=LanceDBSchema384)
lance_ingest.initialize()
if len(self.documents) <= 256:
_table = lance_ingest.bulk_insert(create_vectors=False)
else:
_table = lance_ingest.bulk_insert(create_vectors=True)
sql_path = sqlite_folder.joinpath('documents.sqlite')
sqlite_ingest = SqlLiteIngestNotes(documents=self.documents,
source_file=self.source_file,
db_location=sql_path,
index_name=self.index_name,
overwrite=self.overwrite)
sqlite_ingest.initialize()
sqlite_ingest.bulk_insert()
| [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((216, 255), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (235, 255), False, 'from sentence_transformers import SentenceTransformer\n'), ((271, 295), 'pathlib.Path', 'Path', (['"""data/collections"""'], {}), "('data/collections')\n", (275, 295), False, 'from pathlib import Path\n'), ((310, 342), 'pathlib.Path', 'Path', (['"""data/config/indexes.yaml"""'], {}), "('data/config/indexes.yaml')\n", (314, 342), False, 'from pathlib import Path\n'), ((358, 373), 'pathlib.Path', 'Path', (['"""indexes"""'], {}), "('indexes')\n", (362, 373), False, 'from pathlib import Path\n'), ((390, 405), 'pathlib.Path', 'Path', (['"""indexes"""'], {}), "('indexes')\n", (394, 405), False, 'from pathlib import Path\n'), ((471, 492), 'pathlib.Path', 'Path', (['"""data/indexes/"""'], {}), "('data/indexes/')\n", (475, 492), False, 'from pathlib import Path\n'), ((5615, 5626), 'lancedb.pydantic.Vector', 'Vector', (['(384)'], {}), '(384)\n', (5621, 5626), False, 'from lancedb.pydantic import LanceModel, Vector, List\n'), ((5739, 5750), 'lancedb.pydantic.Vector', 'Vector', (['(512)'], {}), '(512)\n', (5745, 5750), False, 'from lancedb.pydantic import LanceModel, Vector, List\n'), ((4114, 4147), 'sqlite3.connect', 'sqlite3.connect', (['self.db_location'], {}), '(self.db_location)\n', (4129, 4147), False, 'import sqlite3\n'), ((6128, 6164), 'lancedb.connect', 'lancedb.connect', (['self.lance_location'], {}), '(self.lance_location)\n', (6143, 6164), False, 'import lancedb\n'), ((7989, 8018), 'pandas.read_csv', 'pd.read_csv', (['self.source_file'], {}), '(self.source_file)\n', (8000, 8018), True, 'import pandas as pd\n'), ((7902, 7914), 'json.load', 'json.load', (['f'], {}), '(f)\n', (7911, 7914), False, 'import json\n'), ((1248, 1255), 'uuid.uuid4', 'uuid4', ([], {}), '()\n', (1253, 1255), False, 'from uuid import uuid4\n'), ((5257, 5286), 'json.dumps', 'json.dumps', (['document.metadata'], {}), '(document.metadata)\n', (5267, 5286), False, 'import json\n')] |
import argparse
import pandas as pd
from unstructured.partition.pdf import partition_pdf
import lancedb.embeddings.gte
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
def split_text_into_chunks(text, chunk_size, overlap):
"""
Split text into chunks with a specified size and overlap.
Parameters:
- text (str): The input text to be split into chunks.
- chunk_size (int): The size of each chunk.
- overlap (int): The number of characters to overlap between consecutive chunks.
Returns:
- List of chunks (str).
"""
if chunk_size <= 0 or overlap < 0:
raise ValueError("Invalid chunk size or overlap value.")
chunks = []
start = 0
while start < len(text):
end = start + chunk_size
chunk = text[start:end]
chunks.append(chunk)
start += chunk_size - overlap
return chunks
def pdf_to_lancedb(pdf_file: str, path: str = "/tmp/lancedb"):
"""
create lancedb table from a pdf file
Parameters:
- pdf_file (str): The path to the input PDF file.
- path (str): The path to store the vector DB.
default: /tmp/lancedb
Returns:
- None
"""
elements = partition_pdf(pdf_file)
content = "\n\n".join([e.text for e in elements])
chunks = split_text_into_chunks(text=content, chunk_size=1000, overlap=200)
model = (
get_registry().get("gte-text").create(mlx=True)
) # mlx=True for Apple silicon only.
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
df = pd.DataFrame({"text": chunks})
db = lancedb.connect(path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
return None
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Create a Vector DB from a PDF file")
# Input
parser.add_argument(
"--pdf",
help="The path to the input PDF file",
default="flash_attention.pdf",
)
# Output
parser.add_argument(
"--db_path",
type=str,
default="/tmp/lancedb",
help="The path to store the vector DB",
)
args = parser.parse_args()
pdf_to_lancedb(args.pdf, args.db_path)
print("ingestion done , move to query!")
| [
"lancedb.embeddings.get_registry"
] | [((1242, 1265), 'unstructured.partition.pdf.partition_pdf', 'partition_pdf', (['pdf_file'], {}), '(pdf_file)\n', (1255, 1265), False, 'from unstructured.partition.pdf import partition_pdf\n'), ((1658, 1688), 'pandas.DataFrame', 'pd.DataFrame', (["{'text': chunks}"], {}), "({'text': chunks})\n", (1670, 1688), True, 'import pandas as pd\n'), ((1864, 1937), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Create a Vector DB from a PDF file"""'}), "(description='Create a Vector DB from a PDF file')\n", (1887, 1937), False, 'import argparse\n'), ((1424, 1438), 'lancedb.embeddings.get_registry', 'get_registry', ([], {}), '()\n', (1436, 1438), False, 'from lancedb.embeddings import get_registry\n')] |
from datetime import datetime
from typing import List, Union
from pydantic import field_validator
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
from pydantic import BaseModel
openai = get_registry().get("openai").create(name="text-embedding-3-large", dim=256)
class TextChunk(LanceModel):
doc_id: str
text: str = openai.SourceField()
vector: Vector(openai.ndims()) = openai.VectorField(default=None)
post_title: str
publish_date: datetime
chunk_id: int
source: str
class Document(BaseModel):
id: str
content: str
filename: str
metadata: dict[str, Union[str, List[str]]]
@field_validator('metadata')
@classmethod
def metadata_must_contain_a_valid_datestring(cls, v: dict[str, Union[str, List[str]]]):
try:
datetime.strptime(v["date"], "%Y-%m")
except Exception as e:
raise ValueError(
f"Date format must be YYYY-MM (Eg. 2024-10). Unable to parse provided date of {v['date']} "
)
return v
@field_validator('metadata')
@classmethod
def metadata_must_contain_required_keys(cls,v:dict[str, Union[str, List[str]]]):
required_keys = [
"url","date","title"
]
for k in required_keys:
if k not in v:
raise ValueError(f"Required Property {k} is not present in metadata")
return v
| [
"lancedb.embeddings.get_registry"
] | [((671, 698), 'pydantic.field_validator', 'field_validator', (['"""metadata"""'], {}), "('metadata')\n", (686, 698), False, 'from pydantic import field_validator\n'), ((1082, 1109), 'pydantic.field_validator', 'field_validator', (['"""metadata"""'], {}), "('metadata')\n", (1097, 1109), False, 'from pydantic import field_validator\n'), ((833, 870), 'datetime.datetime.strptime', 'datetime.strptime', (["v['date']", '"""%Y-%m"""'], {}), "(v['date'], '%Y-%m')\n", (850, 870), False, 'from datetime import datetime\n'), ((231, 245), 'lancedb.embeddings.get_registry', 'get_registry', ([], {}), '()\n', (243, 245), False, 'from lancedb.embeddings import get_registry\n')] |
import os
import shutil
from pathlib import Path
import lancedb
from lancedb.pydantic import LanceModel, Vector, pydantic_to_schema
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import LanceDB
# LanceDB pydantic schema
class Content(LanceModel):
text: str
vector: Vector(384)
def get_files() -> list[str]:
# Get a list of files from the data directory
data_dir = Path("../data")
txt_files = list(data_dir.glob("*.txt"))
# Return string of paths or else lancedb/pydantic will complain
txt_files = [str(f) for f in txt_files]
return txt_files
def get_docs(txt_files: list[str]):
loaders = [TextLoader(f) for f in txt_files]
docs = [loader.load() for loader in loaders]
return docs
def create_lance_table(table_name: str) -> lancedb.table.LanceTable:
try:
# Create empty table if it does not exist
tbl = db.create_table(table_name, schema=pydantic_to_schema(Content), mode="overwrite")
except OSError:
# If table exists, open it
tbl = db.open_table(table_name, mode="append")
return tbl
async def search_lancedb(query: str, retriever: LanceDB) -> list[Content]:
"Perform async retrieval from LanceDB"
search_result = await retriever.asimilarity_search(query, k=5)
if len(search_result) > 0:
print(search_result[0].page_content)
else:
print("Failed to find similar result")
return search_result
def main() -> None:
txt_files = get_files()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
)
tbl = create_lance_table("countries")
docs = get_docs(txt_files)
chunked_docs = []
for doc in docs:
chunked_docs.extend(text_splitter.split_documents(doc))
# Ingest docs in append mode
retriever = LanceDB.from_documents(chunked_docs, embeddings, connection=tbl)
return retriever
if __name__ == "__main__":
DB_NAME = "./db"
TABLE = "countries"
if os.path.exists(DB_NAME):
# Clear DB if it exists
shutil.rmtree(DB_NAME)
db = lancedb.connect(DB_NAME)
retriever = main()
print("Finished loading documents to LanceDB")
query = "Is Tonga a monarchy or a democracy"
docsearch = retriever.as_retriever(
search_kwargs={"k": 3, "threshold": 0.8, "return_vector": False}
)
search_result = docsearch.get_relevant_documents(query)
if len(search_result) > 0:
print(f"Found {len(search_result)} relevant results")
print([r.page_content for r in search_result])
else:
print("Failed to find relevant result") | [
"lancedb.connect",
"lancedb.pydantic.Vector",
"lancedb.pydantic.pydantic_to_schema"
] | [((429, 440), 'lancedb.pydantic.Vector', 'Vector', (['(384)'], {}), '(384)\n', (435, 440), False, 'from lancedb.pydantic import LanceModel, Vector, pydantic_to_schema\n'), ((538, 553), 'pathlib.Path', 'Path', (['"""../data"""'], {}), "('../data')\n", (542, 553), False, 'from pathlib import Path\n'), ((1650, 1714), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(512)', 'chunk_overlap': '(50)'}), '(chunk_size=512, chunk_overlap=50)\n', (1680, 1714), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((1732, 1842), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': '"""sentence-transformers/all-MiniLM-L6-v2"""', 'model_kwargs': "{'device': 'cpu'}"}), "(model_name='sentence-transformers/all-MiniLM-L6-v2',\n model_kwargs={'device': 'cpu'})\n", (1753, 1842), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((2082, 2146), 'langchain.vectorstores.LanceDB.from_documents', 'LanceDB.from_documents', (['chunked_docs', 'embeddings'], {'connection': 'tbl'}), '(chunked_docs, embeddings, connection=tbl)\n', (2104, 2146), False, 'from langchain.vectorstores import LanceDB\n'), ((2249, 2272), 'os.path.exists', 'os.path.exists', (['DB_NAME'], {}), '(DB_NAME)\n', (2263, 2272), False, 'import os\n'), ((2347, 2371), 'lancedb.connect', 'lancedb.connect', (['DB_NAME'], {}), '(DB_NAME)\n', (2362, 2371), False, 'import lancedb\n'), ((785, 798), 'langchain.document_loaders.TextLoader', 'TextLoader', (['f'], {}), '(f)\n', (795, 798), False, 'from langchain.document_loaders import TextLoader\n'), ((2314, 2336), 'shutil.rmtree', 'shutil.rmtree', (['DB_NAME'], {}), '(DB_NAME)\n', (2327, 2336), False, 'import shutil\n'), ((1063, 1090), 'lancedb.pydantic.pydantic_to_schema', 'pydantic_to_schema', (['Content'], {}), '(Content)\n', (1081, 1090), False, 'from lancedb.pydantic import LanceModel, Vector, pydantic_to_schema\n')] |
import lancedb
import uuid
from datetime import datetime
from tqdm import tqdm
from typing import Optional, List, Iterator, Dict
from memgpt.config import MemGPTConfig
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.config import AgentConfig, MemGPTConfig
from memgpt.constants import MEMGPT_DIR
from memgpt.utils import printd
from memgpt.data_types import Record, Message, Passage, Source
from datetime import datetime
from lancedb.pydantic import Vector, LanceModel
""" Initial implementation - not complete """
def get_db_model(table_name: str, table_type: TableType):
config = MemGPTConfig.load()
if table_type == TableType.ARCHIVAL_MEMORY or table_type == TableType.PASSAGES:
# create schema for archival memory
class PassageModel(LanceModel):
"""Defines data model for storing Passages (consisting of text, embedding)"""
id: uuid.UUID
user_id: str
text: str
doc_id: str
agent_id: str
data_source: str
embedding: Vector(config.embedding_dim)
metadata_: Dict
def __repr__(self):
return f"<Passage(passage_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
def to_record(self):
return Passage(
text=self.text,
embedding=self.embedding,
doc_id=self.doc_id,
user_id=self.user_id,
id=self.id,
data_source=self.data_source,
agent_id=self.agent_id,
metadata=self.metadata_,
)
return PassageModel
elif table_type == TableType.RECALL_MEMORY:
class MessageModel(LanceModel):
"""Defines data model for storing Message objects"""
__abstract__ = True # this line is necessary
# Assuming message_id is the primary key
id: uuid.UUID
user_id: str
agent_id: str
# openai info
role: str
text: str
model: str
user: str
# function info
function_name: str
function_args: str
function_response: str
embedding = Vector(config.embedding_dim)
# Add a datetime column, with default value as the current time
created_at = datetime
def __repr__(self):
return f"<Message(message_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
def to_record(self):
return Message(
user_id=self.user_id,
agent_id=self.agent_id,
role=self.role,
name=self.name,
text=self.text,
model=self.model,
function_name=self.function_name,
function_args=self.function_args,
function_response=self.function_response,
embedding=self.embedding,
created_at=self.created_at,
id=self.id,
)
"""Create database model for table_name"""
return MessageModel
elif table_type == TableType.DATA_SOURCES:
class SourceModel(LanceModel):
"""Defines data model for storing Passages (consisting of text, embedding)"""
# Assuming passage_id is the primary key
id: uuid.UUID
user_id: str
name: str
created_at: datetime
def __repr__(self):
return f"<Source(passage_id='{self.id}', name='{self.name}')>"
def to_record(self):
return Source(id=self.id, user_id=self.user_id, name=self.name, created_at=self.created_at)
"""Create database model for table_name"""
return SourceModel
else:
raise ValueError(f"Table type {table_type} not implemented")
class LanceDBConnector(StorageConnector):
"""Storage via LanceDB"""
# TODO: this should probably eventually be moved into a parent DB class
def __init__(self, name: Optional[str] = None, agent_config: Optional[AgentConfig] = None):
# TODO
pass
def generate_where_filter(self, filters: Dict) -> str:
where_filters = []
for key, value in filters.items():
where_filters.append(f"{key}={value}")
return where_filters.join(" AND ")
@abstractmethod
def get_all_paginated(self, filters: Optional[Dict] = {}, page_size: Optional[int] = 1000) -> Iterator[List[Record]]:
# TODO
pass
@abstractmethod
def get_all(self, filters: Optional[Dict] = {}, limit=10) -> List[Record]:
# TODO
pass
@abstractmethod
def get(self, id: str) -> Optional[Record]:
# TODO
pass
@abstractmethod
def size(self, filters: Optional[Dict] = {}) -> int:
# TODO
pass
@abstractmethod
def insert(self, record: Record):
# TODO
pass
@abstractmethod
def insert_many(self, records: List[Record], show_progress=False):
# TODO
pass
@abstractmethod
def query(self, query: str, query_vec: List[float], top_k: int = 10, filters: Optional[Dict] = {}) -> List[Record]:
# TODO
pass
@abstractmethod
def query_date(self, start_date, end_date):
# TODO
pass
@abstractmethod
def query_text(self, query):
# TODO
pass
@abstractmethod
def delete_table(self):
# TODO
pass
@abstractmethod
def delete(self, filters: Optional[Dict] = {}):
# TODO
pass
@abstractmethod
def save(self):
# TODO
pass
| [
"lancedb.pydantic.Vector"
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import os, time
import pandas as pd
import numpy as np
from collections import Counter
from .utils import abbreviate_book_name_in_full_reference, get_train_test_split_from_verse_list, embed_batch
from .types import TranslationTriplet, ChatResponse, VerseMap, AIResponse
from pydantic import BaseModel, Field
from typing import Any, List, Optional, Callable
from random import shuffle
import requests
import guidance
import lancedb
from lancedb.embeddings import with_embeddings
from nltk.util import ngrams
from nltk import FreqDist
import logging
logger = logging.getLogger('uvicorn')
machine = 'http://192.168.1.76:8081'
def get_dataframes(target_language_code=None, file_suffix=None):
"""Get source data dataframes (literalistic english Bible and macula Greek/Hebrew)"""
bsb_bible_df = pd.read_csv('data/bsb-utf8.txt', sep='\t', names=['vref', 'content'], header=0)
bsb_bible_df['vref'] = bsb_bible_df['vref'].apply(abbreviate_book_name_in_full_reference)
macula_df = pd.read_csv('data/combined_greek_hebrew_vref.csv') # Note: csv wrangled in notebook: `create-combined-macula-df.ipynb`
if target_language_code:
target_tsv = get_target_vref_df(target_language_code, file_suffix=file_suffix)
target_df = get_target_vref_df(target_language_code, file_suffix=file_suffix)
return bsb_bible_df, macula_df, target_df
else:
return bsb_bible_df, macula_df
def get_vref_list(book_abbreviation=None):
vref_url = 'https://raw.githubusercontent.com/BibleNLP/ebible/main/metadata/vref.txt'
if not os.path.exists('data/vref.txt'):
os.system(f'wget {vref_url} -O data/vref.txt')
with open('data/vref.txt', 'r', encoding="utf8") as f:
if book_abbreviation:
return [i.strip() for i in f.readlines() if i.startswith(book_abbreviation)]
else:
return list(set([i.strip().split(' ')[0] for i in f.readlines()]))
def get_target_vref_df(language_code, file_suffix=None, drop_empty_verses=False):
"""Get target language data by language code"""
if not len(language_code) == 3:
return 'Invalid language code. Please use 3-letter ISO 639-3 language code.'
language_code = language_code.lower().strip()
language_code = f'{language_code}-{language_code}'
# if file_suffix:
# print('adding file suffix', file_suffix)
language_code = f'{language_code}{file_suffix if file_suffix else ""}'
target_data_url = f'https://raw.githubusercontent.com/BibleNLP/ebible/main/corpus/{language_code}.txt'
path = f'data/{language_code}.txt'
if not os.path.exists(path):
try:
os.system(f'wget {target_data_url} -O {path}')
except:
return 'No data found for language code. Please check the eBible repo for available data.'
with open(path, 'r', encoding="utf8") as f:
target_text = f.readlines()
target_text = [i.strip() for i in target_text]
vref_url = 'https://raw.githubusercontent.com/BibleNLP/ebible/main/metadata/vref.txt'
if not os.path.exists('data/vref.txt'):
os.system(f'wget {vref_url} -O data/vref.txt')
with open('data/vref.txt', 'r', encoding="utf8") as f:
target_vref = f.readlines()
target_vref = [i.strip() for i in target_vref]
target_tsv = [i for i in list(zip(target_vref, target_text))]
if drop_empty_verses:
target_tsv = [i for i in target_tsv if i[1] != '']
target_df = pd.DataFrame(target_tsv, columns=['vref', 'content'])
return target_df
from pandas import DataFrame as DataFrameClass
def create_lancedb_table_from_df(df: DataFrameClass, table_name, content_column_name='content'):
"""Turn a pandas dataframe into a LanceDB table."""
start_time = time.time()
logger.info('Creating LanceDB table...')
import lancedb
from lancedb.embeddings import with_embeddings
logger.error(f'Creating LanceDB table: {table_name}, {df.head}')
# rename 'content' field as 'text' as lancedb expects
try:
df = df.rename(columns={content_column_name: 'text'})
except:
assert 'text' in df.columns, 'Please rename the content column to "text" or specify the column name in the function call.'
# Add target_language_code to the dataframe
df['language_code'] = table_name
# mkdir lancedb if it doesn't exist
if not os.path.exists('./lancedb'):
os.mkdir('./lancedb')
# Connect to LanceDB
db = lancedb.connect("./lancedb")
table = get_table_from_database(table_name)
if not table:
# If it doesn't exist, create it
df_filtered = df[df['text'].str.strip() != '']
# data = with_embeddings(embed_batch, df_filtered.sample(1000)) # FIXME: I can't process the entirety of the bsb bible for some reason. Something is corrupt or malformed in the data perhaps
data = with_embeddings(embed_batch, df_filtered)
# data = with_embeddings(embed_batch, df)
table = db.create_table(
table_name,
data=data,
mode="create",
)
else:
# If it exists, append to it
df_filtered = df[df['text'].str.strip() != '']
data = with_embeddings(embed_batch, df_filtered.sample(10000))
data = data.fillna(0) # Fill missing values with 0
table.append(data)
print('LanceDB table created. Time elapsed: ', time.time() - start_time, 'seconds.')
return table
def load_database(target_language_code=None, file_suffix=None):
print('Loading dataframes...')
if target_language_code:
print(f'Loading target language data for {target_language_code} (suffix: {file_suffix})...')
bsb_bible_df, macula_df, target_df = get_dataframes(target_language_code, file_suffix=file_suffix)
else:
print('No target language code specified. Loading English and Greek/Hebrew data only.')
bsb_bible_df, macula_df = get_dataframes()
target_df = None
print('Creating tables...')
# table_name = 'verses'
# create_lancedb_table_from_df(bsb_bible_df, table_name)
# create_lancedb_table_from_df(macula_df, table_name)
create_lancedb_table_from_df(bsb_bible_df, 'bsb_bible')
create_lancedb_table_from_df(macula_df, 'macula')
if target_df is not None:
print('Creating target language tables...')
# create_lancedb_table_from_df(target_df, table_name)
target_table_name = target_language_code if not file_suffix else f'{target_language_code}{file_suffix}'
create_lancedb_table_from_df(target_df, target_table_name)
print('Database populated.')
return True
def get_table_from_database(table_name):
"""
Returns a table by name.
Use '/api/db_info' endpoint to see available tables.
"""
import lancedb
db = lancedb.connect("./lancedb")
table_names = db.table_names()
if table_name not in table_names:
logger.error(f'''Table {table_name} not found. Please check the table name and try again.
Available tables: {table_names}''')
return None
table = db.open_table(table_name)
return table
def get_verse_triplet(full_verse_ref: str, language_code: str, bsb_bible_df, macula_df):
"""
Get verse from bsb_bible_df,
AND macula_df (greek and hebrew)
AND target_vref_data (target language)
e.g., http://localhost:3000/api/verse/GEN%202:19&aai
or NT: http://localhost:3000/api/verse/ROM%202:19&aai
"""
bsb_row = bsb_bible_df[bsb_bible_df['vref'] == full_verse_ref]
macula_row = macula_df[macula_df['vref'] == full_verse_ref]
target_df = get_target_vref_df(language_code)
target_row = target_df[target_df['vref'] == full_verse_ref]
if not bsb_row.empty and not macula_row.empty:
return {
'bsb': {
'verse_number': int(bsb_row.index[0]),
'vref': bsb_row['vref'][bsb_row.index[0]],
'content': bsb_row['content'][bsb_row.index[0]]
},
'macula': {
'verse_number': int(macula_row.index[0]),
'vref': macula_row['vref'][macula_row.index[0]],
'content': macula_row['content'][macula_row.index[0]]
},
'target': {
'verse_number': int(target_row.index[0]),
'vref': target_row['vref'][target_row.index[0]],
'content': target_row['content'][target_row.index[0]]
}
}
else:
return None
def query_lancedb_table(language_code: str, query: str, limit: str='50'):
"""Get similar sentences from a LanceDB table."""
# limit = int(limit) # I don't know if this is necessary. The FastAPI endpoint might infer an int from the query param if I typed it that way
table = get_table_from_database(language_code)
query_vector = embed_batch([query])[0]
if not table:
return {'error':'table not found'}
result = table.search(query_vector).limit(limit).to_df().to_dict()
if not result.values():
return []
texts = result['text']
# scores = result['_distance']
vrefs = result['vref']
output = []
for i in range(len(texts)):
output.append({
'text': texts[i],
# 'score': scores[i],
'vref': vrefs[i]
})
return output
def get_unique_tokens_for_language(language_code):
"""Get unique tokens for a language"""
tokens_to_ignore = ['']
if language_code == 'bsb' or language_code =='bsb_bible':
df, _, _ = get_dataframes()
elif language_code =='macula':
_, df, _ = get_dataframes()
else:
_, _, df = get_dataframes(target_language_code=language_code)
target_tokens = df['content'].apply(lambda x: x.split(' ')).explode().tolist()
target_tokens = [token for token in target_tokens if token not in tokens_to_ignore]
unique_tokens = Counter(target_tokens)
return unique_tokens
def get_ngrams(language_code: str, size: int=2, n=100, string_filter: list[str]=[]):
"""Get ngrams with frequencies for a language
Params:
- language_code (str): language code
- size (int): ngram size
- n (int): max number of ngrams to return
- string_filter (list[str]): if passed, only return ngrams where all ngram tokens are contained in string_filter
A string_filter might be, for example, a tokenized sentence where you want to detect ngrams relative to the entire corpus.
NOTE: calculating these is not slow, and it is assumed that the corpus itself will change during iterative translation
If it winds up being slow, we can cache the results and only recalculate when the corpus changes. # ?FIXME
"""
tokens_to_ignore = ['']
# TODO: use a real character filter. I'm sure NLTK has something built in
if language_code == 'bsb' or language_code =='bsb_bible':
df, _, _ = get_dataframes()
elif language_code =='macula':
_, df, _ = get_dataframes()
else:
_, _, df = get_dataframes(target_language_code=language_code)
target_tokens = df['content'].apply(lambda x: x.split(' ')).explode().tolist()
target_tokens = [token for token in target_tokens if token not in tokens_to_ignore]
n_grams = [tuple(gram) for gram in ngrams(target_tokens, size)]
print('ngrams before string_filter:', len(n_grams))
if string_filter:
print('filtering with string_filter')
n_grams = [gram for gram in n_grams if all(token in string_filter for token in gram)]
freq_dist = FreqDist(n_grams)
print('ngrams after string_filter:', len(n_grams))
return list(freq_dist.most_common(n))
def build_translation_prompt(
vref,
target_language_code,
source_language_code=None,
bsb_bible_df=None,
macula_df=None,
number_of_examples=3,
backtranslate=False) -> dict[str, TranslationTriplet]:
"""Build a prompt for translation"""
if bsb_bible_df is None or bsb_bible_df.empty or macula_df is None or macula_df.empty: # build bsb_bible_df and macula_df only if not supplied (saves overhead)
bsb_bible_df, macula_df, target_df = get_dataframes(target_language_code=target_language_code)
if source_language_code:
_, _, source_df = get_dataframes(target_language_code=source_language_code)
else:
source_df = bsb_bible_df
# Query the LanceDB table for the most similar verses to the source text (or bsb if source_language_code is None)
table_name = source_language_code if source_language_code else 'bsb_bible'
query = source_df[source_df['vref']==vref]['content'].values[0]
original_language_source = macula_df[macula_df['vref']==vref]['content'].values[0]
print(f'Query result: {query}')
similar_verses = query_lancedb_table(table_name, query, limit=number_of_examples) # FIXME: query 50 and then filter to first n that have target content?
triplets = [get_verse_triplet(similar_verse['vref'], target_language_code, bsb_bible_df, macula_df) for similar_verse in similar_verses]
target_verse = target_df[target_df['vref']==vref]['content'].values[0]
# Initialize an empty dictionary to store the JSON objects
json_objects: dict[str, TranslationTriplet] = dict()
for triplet in triplets:
# Create a JSON object for each triplet with top-level keys being the VREFs
json_objects[triplet["bsb"]["vref"]] = TranslationTriplet(
source=triplet["macula"]["content"],
bridge_translation=triplet["bsb"]["content"],
target=triplet["target"]["content"] # FIXME: validate that content exists here?
).to_dict()
# Add the source verse Greek/Hebrew and English reference to the JSON objects
json_objects[vref] = TranslationTriplet(
source=original_language_source,
bridge_translation=query,
target=target_verse
).to_dict()
return json_objects
def execute_discriminator_evaluation(verse_triplets: dict[str, TranslationTriplet], hypothesis_vref: str, hypothesis_key='target') -> ChatResponse:
"""
Accepts an array of verses as verse_triplets.
The final triplet is assumed to be the hypothesis.
The hypothesis string is assumed to be the target language rendering.
This simple discriminator type of evaluation scrambles the input verse_triplets
and prompts the LLM to detect which is the hypothesis.
The return value is:
{
'y_index': index_of_hypothesis,
'y_hat_index': llm_predicted_index,
'rationale': rationale_string,
}
If you introduce any intermediate translation steps (e.g., leaving unknown tokens untranslated),
then this type of evaluation is not recommended.
"""
hypothesis_triplet = verse_triplets[hypothesis_vref]
print(f'Hypothesis: {hypothesis_triplet}')
verse_triplets_list: list[tuple] = list(verse_triplets.items())
print('Verse triplets keys:', [k for k, v in verse_triplets_list])
# # Shuffle the verse_triplets
shuffle(verse_triplets_list)
print(f'Shuffled verse triplets keys: {[k for k, v in verse_triplets_list]}')
# # Build the prompt
prompt = ''
for i, triplet in enumerate(verse_triplets_list):
print(f'Verse triplet {i}: {triplet}')
prompt += f'\n{triplet[0]}. Target: {triplet[1]["target"]}'
url = f"{machine}/v1/chat/completions"
headers = {
"Content-Type": "application/json",
}
payload = {
"messages": [
# FIXME: I think I should just ask the model to designate which verse stands out as the least likely to be correct.
{"role": "user", "content": f"### Instruction: One of these translations is incorrect, and you can only try to determine by comparing the examples given:\n{prompt}\nWhich one of these is incorrect? (show only '[put verse ref here] -- rationale as to why you picked this one relative only to the other options')\n###Response:"}
],
"temperature": 0.7,
"max_tokens": -1,
"stream": False,
}
response = requests.post(url, json=payload, headers=headers)
return response.json()
def execute_fewshot_translation(vref, target_language_code, source_language_code=None, bsb_bible_df=None, macula_df=None, number_of_examples=3, backtranslate=False) -> ChatResponse:
prompt = build_translation_prompt(vref, target_language_code, source_language_code, bsb_bible_df, macula_df, number_of_examples, backtranslate)
url = f"{machine}/v1/chat/completions"
headers = {
"Content-Type": "application/json",
}
payload = {
"messages": [
{"role": "user", "content": prompt}
],
"temperature": 0.7,
"max_tokens": -1,
"stream": False,
}
response = requests.post(url, json=payload, headers=headers)
return response.json()
class RevisionLoop(BaseModel):
# FIXME: this loop should only work for (revise-evaluate)*n, where you start with a translation draft.
# TODO: implement a revision function whose output could be evaluated
iterations: int
function_a: Optional[Callable] = None
function_b: Optional[Callable] = None
function_a_output: Optional[Any] = Field(None, description="Output of function A")
function_b_output: Optional[Any] = Field(None, description="Output of function B")
loop_data: Optional[List[Any]] = Field(None, description="List to store data generated in the loop")
current_iteration: int = Field(0, description="Current iteration of the loop")
def __init__(self, iterations: int, function_a=execute_fewshot_translation, function_b=execute_discriminator_evaluation):
super().__init__(iterations=iterations)
self.function_a = function_a
self.function_b = function_b
self.loop_data = ['test item']
def __iter__(self):
self.current_iteration = 0
return self
def __next__(self):
if self.current_iteration < self.iterations:
print("Executing function A...")
self.function_a_output: VerseMap = self.function_a()
print("Executing function B...")
# inputs for function b: (verse_triplets: dict[str, TranslationTriplet], hypothesis_vref: str, hypothesis_key='target') -> ChatResponse:
function_b_input = {
"verse_triplets": self.function_a_output,
"hypothesis_vref": list(self.function_a_output.keys())[-1],
"hypothesis_key": "target"
}
self.function_b_output = self.function_b(**function_b_input)
self.loop_data.append((self.function_a_output, self.function_b_output))
self.current_iteration += 1
return self.function_a_output, self.function_b_output
else:
print("Reached maximum iterations, stopping loop...")
raise StopIteration
def get_loop_data(self):
return self.loop_data
class Translation():
"""Translations differ from revisions insofar as revisions require an existing draft of the target"""
def __init__(self, vref: str, target_language_code: str, number_of_examples=3, should_backtranslate=False):
self.vref = vref
self.target_language_code = target_language_code
self.number_of_examples = number_of_examples
self.should_backtranslate = should_backtranslate
bsb_bible_df, macula_df = get_dataframes()
self.verse = get_verse_triplet(full_verse_ref=self.vref, language_code=self.target_language_code, bsb_bible_df=bsb_bible_df, macula_df=macula_df)
self.vref_triplets = build_translation_prompt(vref, target_language_code)
# Predict translation
self.hypothesis: ChatResponse = execute_fewshot_translation(vref, target_language_code, source_language_code=None, bsb_bible_df=bsb_bible_df, macula_df=macula_df, number_of_examples=3, backtranslate=False)
# Get feedback on the translation
# NOTE: here is where various evaluation functions could be swapped out
self.feedback: ChatResponse = execute_discriminator_evaluation(self.vref_triplets, self.vref)
def get_hypothesis(self):
return self.hypothesis
def get_feedback(self):
return self.feedback
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
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import logging
import pyarrow as pa
import pyarrow.compute as pc
from tabulate import tabulate
from llama_cpp import Llama
from dryg.settings import DEFAULT_MODEL
from dryg.db import open_table, create_table
from lancedb.embeddings import with_embeddings
MODEL = None
def get_code_blocks(body: pa.ChunkedArray):
"""
Get code blocks from the body of an issue
Args:
body (str): Body of the issue
Returns:
list: List of code blocks
"""
code_blocks = []
for body_chunk in body:
if body_chunk is None:
continue
code_blocks += str(body_chunk).split("```")[1::2]
return code_blocks
def setup_model(model_name:str = None):
"""
Set the model to be used for embedding
"""
global MODEL
if model_name is None:
model_name = DEFAULT_MODEL
if model_name.endswith(".bin"):
MODEL = Llama(model_name, embedding=True, n_threads=8) # workers=8 hardcoded for now
else:
raise ValueError("Invalid model format")
def embedding_func(batch):
"""
Embedding function for the model
"""
if MODEL is None:
setup_model()
return [MODEL.embed(x) for x in batch]
def save_embeddings(issue_table: str, force: bool = False):
"""
Create an index for the issue table
"""
issues = open_table(issue_table).to_arrow()
if "vector" in issues.column_names and not force:
logging.info("Embeddings already exist. Use `force=True` to overwrite")
return
issues = with_embeddings(embedding_func, issues, "title") # Turn this into a Toy problem
create_table(issue_table, issues, mode="overwrite")
def search_table(table: str, query: str):
"""
Search issues in the issue table
Args:
issue_table (str): Name of the issue table
query (str): Query to search for
Returns:
list: List of issues
"""
issues = open_table(table)
query_embedding = embedding_func([query])[0]
results = issues.search(query_embedding).limit(4).to_df()
table = [["Title", "Link"]]
for title, link in zip(results["title"], results["html_url"]):
table.append([title, link])
print(tabulate(table))
| [
"lancedb.embeddings.with_embeddings"
] | [((1527, 1575), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embedding_func', 'issues', '"""title"""'], {}), "(embedding_func, issues, 'title')\n", (1542, 1575), False, 'from lancedb.embeddings import with_embeddings\n'), ((1611, 1662), 'dryg.db.create_table', 'create_table', (['issue_table', 'issues'], {'mode': '"""overwrite"""'}), "(issue_table, issues, mode='overwrite')\n", (1623, 1662), False, 'from dryg.db import open_table, create_table\n'), ((1918, 1935), 'dryg.db.open_table', 'open_table', (['table'], {}), '(table)\n', (1928, 1935), False, 'from dryg.db import open_table, create_table\n'), ((895, 941), 'llama_cpp.Llama', 'Llama', (['model_name'], {'embedding': '(True)', 'n_threads': '(8)'}), '(model_name, embedding=True, n_threads=8)\n', (900, 941), False, 'from llama_cpp import Llama\n'), ((1426, 1497), 'logging.info', 'logging.info', (['"""Embeddings already exist. Use `force=True` to overwrite"""'], {}), "('Embeddings already exist. Use `force=True` to overwrite')\n", (1438, 1497), False, 'import logging\n'), ((2198, 2213), 'tabulate.tabulate', 'tabulate', (['table'], {}), '(table)\n', (2206, 2213), False, 'from tabulate import tabulate\n'), ((1329, 1352), 'dryg.db.open_table', 'open_table', (['issue_table'], {}), '(issue_table)\n', (1339, 1352), False, 'from dryg.db import open_table, create_table\n')] |
from pathlib import Path
from collections import defaultdict
import math
import json
import pandas as pd
import cv2
import duckdb
import matplotlib.pyplot as plt
import numpy as np
import yaml
from tqdm import tqdm
from ultralytics.utils import LOGGER, colorstr
from ultralytics.utils.plotting import Annotator, colors
from torch import Tensor
import lancedb
import pyarrow as pa
from lancedb.embeddings import with_embeddings
from sklearn.decomposition import PCA
from yoloexplorer.dataset import get_dataset_info, Dataset
from yoloexplorer.frontend import launch
from yoloexplorer.config import TEMP_CONFIG_PATH
import torch
import torchvision.models as models
from torchvision import datasets, transforms
from PIL import Image
import sys
SCHEMA = [
"id",
# "img", # Make this optional; disabled by default. Not feasible unless we can have row_id/primary key to index
"path",
"cls",
"labels",
"bboxes",
"segments",
"keypoints",
"meta",
] # + "vector" with embeddings
def encode(img_path):
img = cv2.imread(img_path)
ext = Path(img_path).suffix
img_encoded = cv2.imencode(ext, img)[1].tobytes()
return img_encoded
def decode(img_encoded):
nparr = np.frombuffer(img_encoded, np.byte)
img = cv2.imdecode(nparr, cv2.IMREAD_ANYCOLOR)
return img
class Explorer:
"""
Dataset explorer
"""
def __init__(self, data, device="", model="resnet18", batch_size=64, project="run") -> None:
"""
Args:
data (str, optional): path to dataset file
table (str, optional): path to LanceDB table to load embeddings Table from.
model (str, optional): path to model. Defaults to None.
device (str, optional): device to use. Defaults to ''. If empty, uses the default device.
project (str, optional): path to project. Defaults to "runs/dataset".
"""
self.data = data
self.table = None
self.model = model
self.device = device
self.batch_size = batch_size
self.project = project
self.dataset_info = None
self.predictor = None
self.trainset = None
self.removed_img_count = 0
self.verbose = False # For embedding function
self._sim_index = None
self.version = None
self.table_name = Path(data).name
self.temp_table_name = self.table_name + "_temp"
self.model_arch_supported = [
"resnet18",
"resnet50",
"efficientnet_b0",
"efficientnet_v2_s",
"googlenet",
"mobilenet_v3_small",
]
if model:
self.predictor = self._setup_predictor(model, device)
if data:
self.dataset_info = get_dataset_info(self.data)
self.transform = transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
]
)
def build_embeddings(self, verbose=False, force=False, store_imgs=False):
"""
Builds the dataset in LanceDB table format
Args:
batch (int, optional): batch size. Defaults to 1000.
verbose (bool, optional): verbose. Defaults to False.
force (bool, optional): force rebuild. Defaults to False.
"""
trainset = self.dataset_info["train"]
trainset = trainset if isinstance(trainset, list) else [trainset]
self.trainset = trainset
self.verbose = verbose
dataset = Dataset(img_path=trainset, data=self.dataset_info, augment=False, cache=False)
batch_size = self.batch_size # TODO: fix this hardcoding
db = self._connect()
if not force and self.table_name in db.table_names():
LOGGER.info("LanceDB embedding space already exists. Attempting to reuse it. Use force=True to overwrite.")
self.table = self._open_table(self.table_name)
self.version = self.table.version
if len(self.table) == dataset.ni:
return
else:
self.table = None
LOGGER.info("Table length does not match the number of images in the dataset. Building embeddings...")
table_data = defaultdict(list)
for idx, batch in enumerate(dataset):
batch["id"] = idx
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [self.dataset_info["names"][i] for i in batch["cls"]]
batch["path"] = batch["im_file"]
# batch["cls"] = batch["cls"].tolist()
keys = (key for key in SCHEMA if key in batch)
for key in keys:
val = batch[key]
if isinstance(val, Tensor):
val = val.tolist()
table_data[key].append(val)
table_data["img"].append(encode(batch["im_file"])) if store_imgs else None
if len(table_data[key]) == batch_size or idx == dataset.ni - 1:
df = pd.DataFrame(table_data)
df = with_embeddings(self._embedding_func, df, "path", batch_size=batch_size)
if self.table:
self.table.add(df)
else:
self.table = self._create_table(self.table_name, data=df, mode="overwrite")
self.version = self.table.version
table_data = defaultdict(list)
LOGGER.info(f'{colorstr("LanceDB:")} Embedding space built successfully.')
def plot_embeddings(self):
"""
Projects the embedding space to 2D using PCA
Args:
n_components (int, optional): number of components. Defaults to 2.
"""
if self.table is None:
LOGGER.error("No embedding space found. Please build the embedding space first.")
return None
pca = PCA(n_components=2)
embeddings = np.array(self.table.to_arrow()["vector"].to_pylist())
embeddings = pca.fit_transform(embeddings)
plt.scatter(embeddings[:, 0], embeddings[:, 1])
plt.show()
def get_similar_imgs(self, img, n=10):
"""
Returns the n most similar images to the given image
Args:
img (int, str, Path): index of image in the table, or path to image
n (int, optional): number of similar images to return. Defaults to 10.
Returns:
tuple: (list of paths, list of ids)
"""
embeddings = None
if self.table is None:
LOGGER.error("No embedding space found. Please build the embedding space first.")
return None
if isinstance(img, int):
embeddings = self.table.to_pandas()["vector"][img]
elif isinstance(img, (str, Path)):
img = img
elif isinstance(img, bytes):
img = decode(img)
elif isinstance(img, list): # exceptional case for batch search from dash
df = self.table.to_pandas().set_index("path")
array = None
try:
array = df.loc[img]["vector"].to_list()
embeddings = np.array(array)
except KeyError:
pass
else:
LOGGER.error("img should be index from the table(int), path of an image (str or Path), or bytes")
return
if embeddings is None:
if isinstance(img, list):
embeddings = np.array(
[self.predictor(self._image_encode(i)).squeeze().cpu().detach().numpy() for i in img]
)
else:
embeddings = self.predictor(self._image_encode(img)).squeeze().cpu().detach().numpy()
if len(embeddings.shape) > 1:
embeddings = np.mean(embeddings, axis=0)
sim = self.table.search(embeddings).limit(n).to_df()
return sim["path"].to_list(), sim["id"].to_list()
def plot_similar_imgs(self, img, n=10):
"""
Plots the n most similar images to the given image
Args:
img (int, str, Path): index of image in the table, or path to image.
n (int, optional): number of similar images to return. Defaults to 10.
"""
_, ids = self.get_similar_imgs(img, n)
self.plot_imgs(ids)
def plot_imgs(self, ids=None, query=None, labels=True):
if ids is None and query is None:
ValueError("ids or query must be provided")
# Resize the images to the minimum and maximum width and height
resized_images = []
df = self.sql(query) if query else self.table.to_pandas().iloc[ids]
for _, row in df.iterrows():
img = cv2.imread(row["path"])
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
if labels:
ann = Annotator(img)
for box, label, cls in zip(row["bboxes"], row["labels"], row["cls"]):
ann.box_label(box, label, color=colors(cls, True))
img = ann.result()
resized_images.append(img)
if not resized_images:
LOGGER.error("No images found")
return
# Create a grid of the images
cols = 10 if len(resized_images) > 10 else max(2, len(resized_images))
rows = max(1, math.ceil(len(resized_images) / cols))
fig, axes = plt.subplots(nrows=rows, ncols=cols)
fig.subplots_adjust(hspace=0, wspace=0)
for i, ax in enumerate(axes.ravel()):
if i < len(resized_images):
ax.imshow(resized_images[i])
ax.axis("off")
# Display the grid of images
plt.show()
def get_similarity_index(self, top_k=0.01, sim_thres=0.90, reduce=False, sorted=False):
"""
Args:
sim_thres (float, optional): Similarity threshold to set the minimum similarity. Defaults to 0.9.
top_k (float, optional): Top k fraction of the similar embeddings to apply the threshold on. Default 0.1.
dim (int, optional): Dimension of the reduced embedding space. Defaults to 256.
sorted (bool, optional): Sort the embeddings by similarity. Defaults to False.
Returns:
np.array: Similarity index
"""
if self.table is None:
LOGGER.error("No embedding space found. Please build the embedding space first.")
return None
if top_k > 1.0:
LOGGER.warning("top_k should be between 0 and 1. Setting top_k to 1.0")
top_k = 1.0
if top_k < 0.0:
LOGGER.warning("top_k should be between 0 and 1. Setting top_k to 0.0")
top_k = 0.0
if sim_thres is not None:
if sim_thres > 1.0:
LOGGER.warning("sim_thres should be between 0 and 1. Setting sim_thres to 1.0")
sim_thres = 1.0
if sim_thres < 0.0:
LOGGER.warning("sim_thres should be between 0 and 1. Setting sim_thres to 0.0")
sim_thres = 0.0
embs = np.array(self.table.to_arrow()["vector"].to_pylist())
self._sim_index = np.zeros(len(embs))
limit = max(int(len(embs) * top_k), 1)
# create a new table with reduced dimensionality to speedup the search
self._search_table = self.table
if reduce:
dim = min(256, embs.shape[1]) # TODO: make this configurable
pca = PCA(n_components=min(dim, len(embs)))
embs = pca.fit_transform(embs)
dim = embs.shape[1]
values = pa.array(embs.reshape(-1), type=pa.float32())
table_data = pa.FixedSizeListArray.from_arrays(values, dim)
table = pa.table([table_data, self.table.to_arrow()["id"]], names=["vector", "id"])
self._search_table = self._create_table("reduced_embs", data=table, mode="overwrite")
# with multiprocessing.Pool() as pool: # multiprocessing doesn't do much. Need to revisit
# list(tqdm(pool.imap(build_index, iterable)))
for _, emb in enumerate(tqdm(embs)):
df = self._search_table.search(emb).metric("cosine").limit(limit).to_df()
if sim_thres is not None:
df = df.query(f"_distance >= {1.0 - sim_thres}")
for idx in df["id"][1:]:
self._sim_index[idx] += 1
self._drop_table("reduced_embs") if reduce else None
return self._sim_index if not sorted else np.sort(self._sim_index)
def plot_similarity_index(self, sim_thres=0.90, top_k=0.01, reduce=False, sorted=False):
"""
Plots the similarity index
Args:
threshold (float, optional): Similarity threshold to set the minimum similarity. Defaults to 0.9.
top_k (float, optional): Top k fraction of the similar embeddings to apply the threshold on. Default 0.1.
dim (int, optional): Dimension of the reduced embedding space. Defaults to 256.
sorted (bool, optional): Whether to sort the index or not. Defaults to False.
"""
index = self.get_similarity_index(top_k, sim_thres, reduce)
if sorted:
index = np.sort(index)
plt.bar([i for i in range(len(index))], index)
plt.xlabel("idx")
plt.ylabel("similarity count")
plt.show()
def remove_imgs(self, idxs):
"""
Works on temporary table. To apply the changes to the main table, call `persist()`
Args:
idxs (int or list): Index of the image to remove from the dataset.
"""
if isinstance(idxs, int):
idxs = [idxs]
pa_table = self.table.to_arrow()
mask = [True for _ in range(len(pa_table))]
for idx in idxs:
mask[idx] = False
self.removed_img_count += len(idxs)
table = pa_table.filter(mask)
ids = [i for i in range(len(table))]
table = table.set_column(0, "id", [ids]) # TODO: Revisit this. This is a hack to fix the ids==dix
self.table = self._create_table(self.temp_table_name, data=table, mode="overwrite") # work on a temporary table
self.log_status()
def add_imgs(self, exp, idxs):
"""
Works on temporary table. To apply the changes to the main table, call `persist()`
Args:
data (pd.DataFrame or pa.Table): Table rows to add to the dataset.
"""
table_df = self.table.to_pandas()
data = exp.table.to_pandas().iloc[idxs]
assert len(table_df["vector"].iloc[0]) == len(data["vector"].iloc[0]), "Vector dimension mismatch"
table_df = pd.concat([table_df, data], ignore_index=True)
ids = [i for i in range(len(table_df))]
table_df["id"] = ids
self.table = self._create_table(
self.temp_table_name, data=table_df, mode="overwrite"
) # work on a temporary table
self.log_status()
def reset(self):
"""
Resets the dataset table to its original state or to the last persisted state.
"""
if self.table is None:
LOGGER.info("No changes made to the dataset.")
return
db = self._connect()
if self.temp_table_name in db.table_names():
self._drop_table(self.temp_table_name)
self.table = self._open_table(self.table_name)
self.removed_img_count = 0
# self._sim_index = None # Not sure if we should reset this as computing the index is expensive
LOGGER.info("Dataset reset to original state.")
def persist(self, name=None):
"""
Persists the changes made to the dataset. Available only if data is provided in the constructor.
Args:
name (str, optional): Name of the new dataset. Defaults to `data_updated.yaml`.
"""
db = self._connect()
if self.table is None or self.temp_table_name not in db.table_names():
LOGGER.info("No changes made to the dataset.")
return
LOGGER.info("Persisting changes to the dataset...")
self.log_status()
if not name:
name = self.data.split(".")[0] + "_updated"
datafile_name = name + ".yaml"
train_txt = "train_updated.txt"
path = Path(name).resolve() # add new train.txt file in the dataset parent path
path.mkdir(parents=True, exist_ok=True)
if (path / train_txt).exists():
(path / train_txt).unlink() # remove existing
for img in tqdm(self.table.to_pandas()["path"].to_list()):
with open(path / train_txt, "a") as f:
f.write(f"{img}" + "\n") # add image to txt file
new_dataset_info = self.dataset_info.copy()
new_dataset_info.pop("yaml_file")
new_dataset_info.pop("path") # relative paths will get messed up when merging datasets
new_dataset_info.pop("download") # Assume all files are present offline, there is no way to store metadata yet
new_dataset_info["train"] = (path / train_txt).resolve().as_posix()
for key, value in new_dataset_info.items():
if isinstance(value, Path):
new_dataset_info[key] = value.as_posix()
yaml.dump(new_dataset_info, open(path / datafile_name, "w")) # update dataset.yaml file
# TODO: not sure if this should be called data_final to prevent overwriting the original data?
self.table = self._create_table(datafile_name, data=self.table.to_arrow(), mode="overwrite")
db.drop_table(self.temp_table_name)
LOGGER.info("Changes persisted to the dataset.")
log = self._log_training_cmd(Path(path / datafile_name).relative_to(Path.cwd()).as_posix())
return log
def log_status(self):
# TODO: Pretty print log status
LOGGER.info("\n|-----------------------------------------------|")
LOGGER.info(f"\t Number of images: {len(self.table.to_arrow())}")
LOGGER.info("|------------------------------------------------|")
def sql(self, query: str):
"""
Executes a SQL query on the dataset table.
Args:
query (str): SQL query to execute.
"""
if self.table is None:
LOGGER.info("No table found. Please provide a dataset to work on.")
return
table = self.table.to_arrow() # noqa
result = duckdb.sql(query).to_df()
return result
def dash(self, exps=None, analysis=False):
"""
Launches a dashboard to visualize the dataset.
"""
config = {}
Path(TEMP_CONFIG_PATH).parent.mkdir(exist_ok=True, parents=True)
with open(TEMP_CONFIG_PATH, "w+") as file:
config_exp = [self.config]
if exps:
for exp in exps:
config_exp.append(exp.config)
config["exps"] = config_exp
config["analysis"] = analysis
json.dump(config, file)
launch()
@property
def config(self):
return {"project": self.project, "model": self.model, "device": self.device, "data": self.data}
def _log_training_cmd(self, data_path):
success_log = (
f'{colorstr("LanceDB: ") }New dataset created successfully! Run the following command to train a model:'
)
train_cmd = f"yolo train model={self.model} data={data_path} epochs=10"
success_log = success_log + "\n" + train_cmd
LOGGER.info(success_log)
return train_cmd
def _connect(self):
db = lancedb.connect(self.project)
return db
def _create_table(self, name, data=None, mode="overwrite"):
db = lancedb.connect(self.project)
table = db.create_table(name, data=data, mode=mode)
return table
def _open_table(self, name):
db = lancedb.connect(self.project)
table = db.open_table(name) if name in db.table_names() else None
if table is None:
raise ValueError(f'{colorstr("LanceDB: ") }Table not found.')
return table
def _drop_table(self, name):
db = lancedb.connect(self.project)
if name in db.table_names():
db.drop_table(name)
return True
return False
def _copy_table_to_project(self, table_path):
if not table_path.endswith(".lance"):
raise ValueError(f"{colorstr('LanceDB: ')} Table must be a .lance file")
LOGGER.info(f"Copying table from {table_path}")
path = Path(table_path).parent
name = Path(table_path).stem # lancedb doesn't need .lance extension
db = lancedb.connect(path)
table = db.open_table(name)
return self._create_table(self.table_name, data=table.to_arrow(), mode="overwrite")
def _image_encode(self, img):
image = Image.open(img)
n_channels = np.array(image).ndim
if n_channels == 2:
image = image.convert(mode="RGB")
img_tensor = self.transform(image)
trans_img = img_tensor.unsqueeze(0)
return trans_img
def _embedding_func(self, imgs):
embeddings = []
for img in tqdm(imgs):
encod_img = self._image_encode(img)
embeddings.append(self.predictor(encod_img).squeeze().cpu().detach().numpy())
return embeddings
def _setup_predictor(self, model_arch, device=""):
if model_arch in self.model_arch_supported:
load_model = getattr(models, model_arch)
model = load_model(pretrained=True)
predictor = torch.nn.Sequential(*list(model.children())[:-1])
return predictor
else:
LOGGER.error(f"Supported for {model_arch} is not added yet")
sys.exit(1)
def create_index(self):
# TODO: create index
pass
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
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"""
Run this script to benchmark the serial search performance of FTS and vector search
"""
import argparse
import random
from functools import lru_cache
from pathlib import Path
from typing import Any
from codetiming import Timer
from config import Settings
from rich import progress
from schemas.wine import SearchResult
from sentence_transformers import SentenceTransformer
import lancedb
from lancedb.table import Table
# Custom types
JsonBlob = dict[str, Any]
@lru_cache()
def get_settings():
# Use lru_cache to avoid loading .env file for every request
return Settings()
def get_query_terms(filename: str) -> list[str]:
assert filename.endswith(".txt")
query_terms_file = Path("./benchmark_queries") / filename
with open(query_terms_file, "r") as f:
queries = f.readlines()
assert queries
result = [query.strip() for query in queries]
return result
def fts_search(table: Table, query: str) -> list[SearchResult] | None:
search_result = (
table.search(query, vector_column_name="description")
.select(["id", "title", "description", "country", "variety", "price", "points"])
.limit(10)
).to_pydantic(SearchResult)
if not search_result:
return None
return search_result
def vector_search(model, table: Table, query: str) -> list[SearchResult] | None:
query_vector = model.encode(query.lower())
search_result = (
table.search(query_vector)
.metric("cosine")
.nprobes(20)
.select(["id", "title", "description", "country", "variety", "price", "points"])
.limit(10)
).to_pydantic(SearchResult)
if not search_result:
return None
return search_result
def main():
if args.search == "fts":
URL = "http://localhost:8000/fts_search"
queries = get_query_terms("keyword_terms.txt")
else:
URL = "http://localhost:8000/vector_search"
queries = get_query_terms("vector_terms.txt")
random_choice_queries = [random.choice(queries) for _ in range(LIMIT)]
# Run the search directly on the lancedb table
with Timer(name="Serial search", text="Finished search in {:.4f} sec"):
# Add rich progress bar
with progress.Progress(
"[progress.description]{task.description}",
progress.BarColumn(),
"[progress.percentage]{task.percentage:>3.0f}%",
progress.TimeElapsedColumn(),
) as prog:
overall_progress_task = prog.add_task(
f"Performing {args.search} search", total=len(random_choice_queries)
)
for query in random_choice_queries:
if args.search == "fts":
_ = fts_search(tbl, query)
else:
_ = vector_search(MODEL, tbl, query)
prog.update(overall_progress_task, advance=1)
if __name__ == "__main__":
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--seed", type=int, default=37, help="Seed for random number generator")
parser.add_argument("--limit", "-l", type=int, default=10, help="Number of search terms to randomly generate")
parser.add_argument("--search", type=str, default="fts", help="Specify whether to do FTS or vector search")
args = parser.parse_args()
# fmt: on
LIMIT = args.limit
SEED = args.seed
# Assert that the search type is only one of "fts" or "vector"
assert args.search in ["fts", "vector"], "Please specify a valid search type: 'fts' or 'vector'"
# Assumes that the table in the DB has already been created
DB_NAME = "./winemag"
TABLE = "wines"
db = lancedb.connect(DB_NAME)
tbl = db.open_table(TABLE)
# Load a sentence transformer model for semantic similarity from a specified checkpoint
model_id = get_settings().embedding_model_checkpoint
assert model_id, "Invalid embedding model checkpoint specified in .env file"
MODEL = SentenceTransformer(model_id)
main()
| [
"lancedb.connect"
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from neumai.Shared.NeumSinkInfo import NeumSinkInfo
from neumai.Shared.NeumVector import NeumVector
from neumai.Shared.NeumSearch import NeumSearchResult
from neumai.Shared.Exceptions import(
LanceDBInsertionException,
LanceDBIndexInfoException,
LanceDBIndexCreationException,
LanceDBQueryException
)
from neumai.SinkConnectors.SinkConnector import SinkConnector
from typing import List, Optional
from neumai.SinkConnectors.filter_utils import FilterCondition
from pydantic import Field
import lancedb
from lancedb import DBConnection
class LanceDBSink(SinkConnector):
"""
LanceDB sink
A sink connector for LanceDB, designed to facilitate data output into a
LanceDB storage system. For details about LanceDB, refer to
https://github.com/lancedb/lancedb.
LanceDB supports flat search as well as ANN search.
For indexing, read here - https://lancedb.github.io/lancedb/ann_indexes/#creating-an-ivf_pq-index
Attributes:
-----------
uri: str
URI for LanceDB database.
api_key: str
If presented, connect to LanceDB cloud.
Otherwise, connect to a database on file system or cloud storage.
region: str
Region for use of LanceDB cloud.
table_name: str
Name of LanceDB table to use
create_index: bool
LanceDB offers flat search as well as ANN search. If set to True,
a vector index would be created for searching instead of a
brute-force knn search.
metric: str
The distance metric to use. By default it uses euclidean distance 'L2'.
It also supports 'cosine' and 'dot' distance as well. Needs to be set if create_index is True.
num_partitions: int
The number of partitions of the index.
Needs to be set if create_index is True. And needs to be altered as per data size.
num_sub_vectors: int
The number of sub-vectors (M) that will be created during
Product Quantization (PQ). For D dimensional vector, it will be divided into
M of D/M sub-vectors, each of which is presented by a single PQ code.
accelerator: str
The accelerator to use for the index creation process. Supports GPU and MPS.
Example usage:
ldb = LanceDBSink(uri="data/test_ldb_sink", table_name="demo_ldb_table")
ldb.store(neum_vectors)
ldb.search(query)
"""
uri: str = Field(..., description="URI for LanceDB database")
api_key: Optional[str] = Field(default=None, description="API key for LanceDB cloud")
region: Optional[str] = Field(default=None, description="Region for use of LanceDB cloud")
table_name: str = Field(..., description="Name of LanceDB table to use")
create_index: bool = Field(default=False, description="Boolean to create index or use flat search")
metric: str = Field(default="cosine", description="The distance metric to use in the index")
num_partitions: int = Field(default=256, description="The number of partitions of the index")
num_sub_vectors: int = Field(default=96, description="The number of sub-vectors (M) that will be created during Product Quantization (PQ)")
accelerator: str = Field(default=None, description="Specify to cuda or mps (on Apple Silicon) to enable GPU training.")
# Check API reference for more details
# - https://lancedb.github.io/lancedb/python/python/#lancedb.connect
# db: DBConnection = lancedb.connect(uri=uri, api_key=api_key, region=region)
@property
def sink_name(self) -> str:
return "LanceDBSink"
@property
def required_properties(self) -> List[str]:
return ['uri', 'api_key', 'table_name']
@property
def optional_properties(self) -> List[str]:
return []
def validation(self) -> bool:
"""config_validation connector setup"""
db = lancedb.connect(uri=self.uri, api_key=self.api_key, region=self.region)
return True
def _get_db_connection(self) -> DBConnection:
return lancedb.connect(uri=self.uri, api_key=self.api_key, region=self.region)
def store(self, vectors_to_store: List[NeumVector]) -> int:
db = self._get_db_connection()
table_name = self.table_name
data = []
for vec in vectors_to_store:
dic = {
'id': vec.id,
'vector': vec.vector,
}
for k,v in vec.metadata.items():
dic[k] = v
data.append(dic)
tbl = db.create_table(table_name, data=data, mode="overwrite")
if tbl:
return len(tbl.to_pandas())
raise LanceDBInsertionException("LanceDB storing failed. Try later")
def search(self, vector: List[float], number_of_results: int, filters: List[FilterCondition] = []) -> List[NeumSearchResult]:
db = self._get_db_connection()
tbl = db.open_table(self.table_name)
if self.create_index:
# For more details, refer to docs
# - https://lancedb.github.io/lancedb/python/python/#lancedb.table.Table.create_index
try:
tbl.create_index(
metric=self.metric,
num_partitions=self.num_partitions,
num_sub_vectors=self.num_sub_vectors,
accelerator=self.accelerator,
replace=True)
except Exception as e:
raise LanceDBIndexCreationException(f"LanceDB index creation failed. \nException - {e}")
try:
search_results = tbl.search(query=vector)
for filter in filters:
search_results = search_results.where(f"{filter.field} {filter.operator.value} {filter.value}")
search_results = search_results.limit(number_of_results).to_pandas()
except Exception as e:
raise LanceDBQueryException(f"Failed to query LanceDB. Exception - {e}")
matches = []
cols = search_results.columns
for i in range(len(search_results)):
_id = search_results.iloc[i]['id']
_vec = list(search_results.iloc[i]['vector'])
matches.append(
NeumSearchResult(
id=_id,
vector=_vec,
metadata={k:search_results.iloc[i][k] for k in cols if k not in ['id', 'vector', '_distance']},
score=1-search_results.iloc[i]['_distance']
)
)
return matches
def get_representative_vector(self) -> list:
db = self._get_db_connection()
tbl = db.open_table(self.table_name)
return list(tbl.to_pandas()['vector'].mean())
def info(self) -> NeumSinkInfo:
try:
db = self._get_db_connection()
tbl = db.open_table(self.table_name)
return(NeumSinkInfo(number_vectors_stored=len(tbl)))
except Exception as e:
raise LanceDBIndexInfoException(f"Failed to get information from LanceDB. Exception - {e}")
def delete_vectors_with_file_id(self, file_id: str) -> bool:
db = self._get_db_connection()
table_name = self.table_name
tbl = db.open_table(table_name)
try:
tbl.delete(where=f"id = '{file_id}'")
except:
raise Exception("LanceDB deletion by file id failed.")
return True | [
"lancedb.connect"
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from FlagEmbedding import LLMEmbedder, FlagReranker
import lancedb
import re
import pandas as pd
import random
from datasets import load_dataset
import torch
import gc
from lancedb.embeddings import with_embeddings
embed_model = LLMEmbedder(
"BAAI/llm-embedder", use_fp16=False
) # Load model (automatically use GPUs)
reranker_model = FlagReranker(
"BAAI/bge-reranker-base", use_fp16=True
) # use_fp16 speeds up computation with a slight performance degradation
task = "qa" # Encode for a specific task (qa, icl, chat, lrlm, tool, convsearch)
# get embedding using LLM embedder
def embed_documents(batch):
"""
Function to embed the whole text data
"""
return embed_model.encode_keys(batch, task=task) # Encode data or 'keys'
def search(table, query, top_k=10):
"""
Search a query from the table
"""
query_vector = embed_model.encode_queries(
query, task=task
) # Encode the QUERY (it is done differently than the 'key')
search_results = table.search(query_vector).limit(top_k)
return search_results
def rerank(query, search_results):
search_results["old_similarity_rank"] = search_results.index + 1 # Old ranks
torch.cuda.empty_cache()
gc.collect()
search_results["new_scores"] = reranker_model.compute_score(
[[query, chunk] for chunk in search_results["text"]]
) # Re compute ranks
return search_results.sort_values(by="new_scores", ascending=False).reset_index(
drop=True
)
def main():
queries = load_dataset("BeIR/scidocs", "queries")["queries"].to_pandas()
docs = (
load_dataset("BeIR/scidocs", "corpus")["corpus"]
.to_pandas()
.dropna(subset="text")
.sample(10000)
) # just random samples for faster embed demo
# create Database using LanceDB Cloud
uri = "db://your-project-slug"
api_key = "sk_..."
db = lancedb.connect(uri, api_key=api_key, region="us-east-1")
table_name = "doc_embed"
try:
# Use the train text chunk data to save embed in the DB
data = with_embeddings(
embed_documents, docs, column="text", show_progress=True, batch_size=128
)
table = db.create_table(table_name, data=data) # create Table
except:
table = db.open_table(table_name) # Open Table
query = random.choice(queries["text"])
print("QUERY:-> ", query)
# get top_k search results
search_results = (
search(table, "what is mitochondria?", top_k=10)
.to_pandas()
.dropna(subset="text")
.reset_index(drop=True)
)
print("SEARCH RESULTS:-> ", search_results)
# Rerank search results using Reranker from BGE Reranker
print("QUERY:-> ", query)
search_results_reranked = rerank(query, search_results)
print("SEARCH RESULTS RERANKED:-> ", search_results_reranked)
if __name__ == "__main__":
main()
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"lancedb.connect",
"lancedb.embeddings.with_embeddings"
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import os
import urllib.request
import shutil
import html2text
import predictionguard as pg
from langchain import PromptTemplate, FewShotPromptTemplate
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
import numpy as np
import lancedb
from lancedb.embeddings import with_embeddings
import pandas as pd
import json
os.environ['PREDICTIONGUARD_TOKEN'] = "q1VuOjnffJ3NO2oFN8Q9m8vghYc84ld13jaqdF7E"
# get the ruleset from a local file
fp = urllib.request.urlopen("file:///home/ubuntu/insuranceagent.html")
mybytes = fp.read()
html = mybytes.decode("utf8")
fp.close()
# and convert it to text
h = html2text.HTML2Text()
h.ignore_links = True
text = h.handle(html)
text = text.split("Introduction")[1]
# Chunk the text into smaller pieces for injection into LLM prompts.
text_splitter = CharacterTextSplitter(chunk_size=700, chunk_overlap=50)
docs = text_splitter.split_text(text)
docs = [x.replace('#', '-') for x in docs]
# Now we need to embed these documents and put them into a "vector store" or
# "vector db" that we will use for semantic search and retrieval.
# Embeddings setup
name="all-MiniLM-L12-v2"
model = SentenceTransformer(name)
def embed_batch(batch):
return [model.encode(sentence) for sentence in batch]
def embed(sentence):
return model.encode(sentence)
# LanceDB setup
if os.path.exists(".lancedb"):
shutil.rmtree(".lancedb")
os.mkdir(".lancedb")
uri = ".lancedb"
db = lancedb.connect(uri)
# Create a dataframe with the chunk ids and chunks
metadata = []
for i in range(len(docs)):
metadata.append([i,docs[i]])
doc_df = pd.DataFrame(metadata, columns=["chunk", "text"])
# Embed the documents
data = with_embeddings(embed_batch, doc_df)
# Create the DB table and add the records.
db.create_table("linux", data=data)
table = db.open_table("linux")
table.add(data=data)
# Now let's augment our Q&A prompt with this external knowledge on-the-fly!!!
template = """### Instruction:
Read the below input context and respond with a short answer to the given question. Use only the information in the below input to answer the question. If you cannot answer the question, respond with "Sorry, I can't find an answer, but you might try looking in the following resource."
### Input:
Context: {context}
Question: {question}
### Response:
"""
qa_prompt = PromptTemplate(
input_variables=["context", "question"],
template=template,
)
#define the pre-prompt in order to give the LLM a little bit of expertise
pre_prompt="You are an expert insurance agent. You are getting information about a property. The information is a mixture of the state of the house and the homeowner's complaints. The state of the house will be just a few words describing the condition (for example, water damage). You will analyze the input and produce exactly three insights. These insights should constitute maintenance and protection recommendations for homeowners tailored to their home's condition. All the insights are at most 20 words long. Generate the insights in this form: Insight 1: (text), then on a new line, Insight 2: (text), then on a new line, Insight 3: (text). Only generate the insights and nothing else. Keep a professional tone. Do not make quote anyone. Do not add unrelated information. Do not add any code. Here is the home's condition: "
def rag_answer(message):
# Search the for relevant context
results = table.search(embed(message)).limit(10).to_pandas()
results.sort_values(by=['_distance'], inplace=True, ascending=True)
doc_use = results['text'].values[0]
# Augment the prompt with the context
prompt = qa_prompt.format(context=doc_use, question=message)
# Get a response
result = pg.Completion.create(
model="Nous-Hermes-Llama2-13B",
prompt=prompt
)
return result['choices'][0]['text']
with open('vision_output.json','r') as json_file:
data=json.load(json_file)
visionoutput=data['vision_output']
with open('data.json','r') as json_file:
data=json.load(json_file)
ownercomplaint=data['text']
house_condition=visionoutput+". "+ownercomplaint
#house_condition="Water damage. The gas lines don't work. The kitchen is spotless. The building is in good condition and the walls do not have any cracks in them. There is a termite infestation in the basement."
response=rag_answer(pre_prompt+house_condition)
#response = rag_answer("A house has been destroyed by a tornado and also has been set on fire. The water doesn't work but the gas lines are fine. The area the house is in is notorious for crime. It is built in an earthquake prone zone. There are cracks in the walls and it is quite old.")
print('')
print("3 insights that we've generated based on your report are:\n", response)
with open('insights.json', 'w') as json_file:
json.dump(response,json_file)
with open('stats_output.json','r') as json_file:
data=json.load(json_file)
predicted_claim=str(data['stats'])
#predicted_claim=0.5 #input from statistical model
full_report_pre_prompt="You are an expert insurance agent. You have been given a list of personalized insights about a home that has been surveyed, along with a probability that the homeowner files a claim in the next 3 to 6 months. Based on this, give the property a rating from 1 to 5, where 5 means that the property is healthy, and also explain why the rating was given in not more than 180 words, based on the input insights. A rating of 1 means that the property is not healthy at all. In this scenario, a healthy property is one that has mostly positive or neutral insights and a low probability of having a claim filed. An unhealthy probability is one that has mostly negative insights and a high probability of having a claim filed. Remember that even if the homeowner has a high chance of filing a claim, the property may have positive insights and therefore you should give it a higher score. The rating should be at the beginning of your response. Ensure that you do not have any incomplete sentences. Do not quote anyone. Do not quote any insights verbatim. Keep the tone professional. You are permitted to expand upon the insights but do not stray. Ensure that you complete each sentence. Keep the report to only one continuous paragraph. The insights are: "
#full_report_temp_prompt=full_report_pre_prompt+response
full_report_final_prompt=full_report_pre_prompt+" .The probability of filing a claim is: "+str(predicted_claim)
full_report=rag_answer(full_report_final_prompt)
#full_report_temp_2=rag_answer(full_report_final_prompt)
#full_report_second_prompt="You are an insurance agent that was given an incomplete report. You have psychic powers and can complete missing reports, with perfect extrapolation. Complete the given incomplete report: "
#full_report=rag_answer(full_report_second_prompt+full_report_temp_2)
print("The full report is: ")
print(full_report)
with open('fullreport.json','w') as json_file:
json.dump(full_report,json_file)
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
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import json
import logging
from typing import Any, Dict, Generator, List, Optional, Sequence, Set, Tuple, Type
import lancedb
import pandas as pd
from dotenv import load_dotenv
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import LanceVectorQueryBuilder
from pydantic import BaseModel, ValidationError, create_model
from src.embedding_models.base import (
EmbeddingModel,
EmbeddingModelsConfig,
)
from src.embedding_models.models import OpenAIEmbeddingsConfig
from src.types import Document, EmbeddingFunction
from src.utils.configuration import settings
from src.utils.pydantic_utils import (
clean_schema,
dataframe_to_document_model,
dataframe_to_documents,
extract_fields,
flatten_pydantic_instance,
flatten_pydantic_model,
nested_dict_from_flat,
)
from src.db.base import VectorStore, VectorStoreConfig
logger = logging.getLogger(__name__)
class LanceDBConfig(VectorStoreConfig):
collection_name: str | None = "temp"
storage_path: str = ".lancedb/data"
embedding: EmbeddingModelsConfig = OpenAIEmbeddingsConfig()
distance: str = "cosine"
document_class: Type[Document] = Document
flatten: bool = False # flatten Document class into LanceSchema ?
filter_fields: List[str] = [] # fields usable in filter
filter: str | None = None # filter condition for lexical/semantic search
class LanceDB(VectorStore):
def __init__(self, config: LanceDBConfig = LanceDBConfig()):
super().__init__(config)
self.config: LanceDBConfig = config
emb_model = EmbeddingModel.create(config.embedding)
self.embedding_fn: EmbeddingFunction = emb_model.embedding_fn()
self.embedding_dim = emb_model.embedding_dims
self.host = None
self.port = None
self.is_from_dataframe = False # were docs ingested from a dataframe?
self.df_metadata_columns: List[str] = [] # metadata columns from dataframe
self._setup_schemas(config.document_class)
load_dotenv()
try:
self.client = lancedb.connect(
uri=config.storage_path,
)
except Exception as e:
new_storage_path = config.storage_path + ".new"
logger.warning(
f"""
Error connecting to local LanceDB at {config.storage_path}:
{e}
Switching to {new_storage_path}
"""
)
self.client = lancedb.connect(
uri=new_storage_path,
)
# Note: Only create collection if a non-null collection name is provided.
# This is useful to delay creation of vecdb until we have a suitable
# collection name (e.g. we could get it from the url or folder path).
if config.collection_name is not None:
self.create_collection(
config.collection_name, replace=config.replace_collection
)
def _setup_schemas(self, doc_cls: Type[Document] | None) -> None:
doc_cls = doc_cls or self.config.document_class
self.unflattened_schema = self._create_lance_schema(doc_cls)
self.schema = (
self._create_flat_lance_schema(doc_cls)
if self.config.flatten
else self.unflattened_schema
)
def clear_empty_collections(self) -> int:
coll_names = self.list_collections()
n_deletes = 0
for name in coll_names:
nr = self.client.open_table(name).head(1).shape[0]
if nr == 0:
n_deletes += 1
self.client.drop_table(name)
return n_deletes
def clear_all_collections(self, really: bool = False, prefix: str = "") -> int:
"""Clear all collections with the given prefix."""
if not really:
logger.warning("Not deleting all collections, set really=True to confirm")
return 0
coll_names = [
c for c in self.list_collections(empty=True) if c.startswith(prefix)
]
if len(coll_names) == 0:
logger.warning(f"No collections found with prefix {prefix}")
return 0
n_empty_deletes = 0
n_non_empty_deletes = 0
for name in coll_names:
nr = self.client.open_table(name).head(1).shape[0]
n_empty_deletes += nr == 0
n_non_empty_deletes += nr > 0
self.client.drop_table(name)
logger.warning(
f"""
Deleted {n_empty_deletes} empty collections and
{n_non_empty_deletes} non-empty collections.
"""
)
return n_empty_deletes + n_non_empty_deletes
def list_collections(self, empty: bool = False) -> List[str]:
"""
Returns:
List of collection names that have at least one vector.
Args:
empty (bool, optional): Whether to include empty collections.
"""
colls = self.client.table_names()
if len(colls) == 0:
return []
if empty: # include empty tbls
return colls # type: ignore
counts = [self.client.open_table(coll).head(1).shape[0] for coll in colls]
return [coll for coll, count in zip(colls, counts) if count > 0]
def _create_lance_schema(self, doc_cls: Type[Document]) -> Type[BaseModel]:
"""
Create a subclass of LanceModel with fields:
- id (str)
- Vector field that has dims equal to
the embedding dimension of the embedding model, and a data field of type
DocClass.
- other fields from doc_cls
Args:
doc_cls (Type[Document]): A Pydantic model which should be a subclass of
Document, to be used as the type for the data field.
Returns:
Type[BaseModel]: A new Pydantic model subclassing from LanceModel.
Raises:
ValueError: If `n` is not a non-negative integer or if `DocClass` is not a
subclass of Document.
"""
if not issubclass(doc_cls, Document):
raise ValueError("DocClass must be a subclass of Document")
n = self.embedding_dim
# Prepare fields for the new model
fields = {"id": (str, ...), "vector": (Vector(n), ...)}
# Add both statically and dynamically defined fields from doc_cls
for field_name, field in doc_cls.model_fields.items():
fields[field_name] = (field.annotation, field.default)
# Create the new model with dynamic fields
NewModel = create_model(
"NewModel", __base__=LanceModel, **fields
) # type: ignore
return NewModel # type: ignore
def _create_flat_lance_schema(self, doc_cls: Type[Document]) -> Type[BaseModel]:
"""
Flat version of the lance_schema, as nested Pydantic schemas are not yet
supported by LanceDB.
"""
lance_model = self._create_lance_schema(doc_cls)
FlatModel = flatten_pydantic_model(lance_model, base_model=LanceModel)
return FlatModel
def create_collection(self, collection_name: str, replace: bool = False) -> None:
"""
Create a collection with the given name, optionally replacing an existing
collection if `replace` is True.
Args:
collection_name (str): Name of the collection to create.
replace (bool): Whether to replace an existing collection
with the same name. Defaults to False.
"""
self.config.collection_name = collection_name
collections = self.list_collections()
if collection_name in collections:
coll = self.client.open_table(collection_name)
if coll.head().shape[0] > 0:
logger.warning(f"Non-empty Collection {collection_name} already exists")
if not replace:
logger.warning("Not replacing collection")
return
else:
logger.warning("Recreating fresh collection")
self.client.create_table(
collection_name, schema=self.schema, mode="overwrite", on_bad_vectors="drop"
)
tbl = self.client.open_table(self.config.collection_name)
# We assume "content" is available as top-level field
if "content" in tbl.schema.names:
tbl.create_fts_index("content", replace=True)
if settings.debug:
level = logger.getEffectiveLevel()
logger.setLevel(logging.INFO)
logger.setLevel(level)
def add_documents(self, documents: Sequence[Document]) -> None:
super().maybe_add_ids(documents)
colls = self.list_collections(empty=True)
if len(documents) == 0:
return
embedding_vecs = self.embedding_fn([doc.content for doc in documents])
coll_name = self.config.collection_name
if coll_name is None:
raise ValueError("No collection name set, cannot ingest docs")
if (
coll_name not in colls
or self.client.open_table(coll_name).head(1).shape[0] == 0
):
# collection either doesn't exist or is empty, so replace it,
# possibly with a new schema
doc_cls = type(documents[0])
self.config.document_class = doc_cls
self._setup_schemas(doc_cls)
self.create_collection(coll_name, replace=True)
ids = [str(d.id()) for d in documents]
# don't insert all at once, batch in chunks of b,
# else we get an API error
b = self.config.batch_size
def make_batches() -> Generator[List[BaseModel], None, None]:
for i in range(0, len(ids), b):
batch = [
self.unflattened_schema(
id=ids[i],
vector=embedding_vecs[i],
**doc.model_dump(),
)
for i, doc in enumerate(documents[i : i + b])
]
if self.config.flatten:
batch = [
flatten_pydantic_instance(instance) # type: ignore
for instance in batch
]
yield batch
tbl = self.client.open_table(self.config.collection_name)
try:
tbl.add(make_batches())
if "content" in tbl.schema.names:
tbl.create_fts_index("content", replace=True)
except Exception as e:
logger.error(
f"""
Error adding documents to LanceDB: {e}
POSSIBLE REMEDY: Delete the LancdDB storage directory
{self.config.storage_path} and try again.
"""
)
def add_dataframe(
self,
df: pd.DataFrame,
content: str = "content",
metadata: List[str] = [],
) -> None:
"""
Add a dataframe to the collection.
Args:
df (pd.DataFrame): A dataframe
content (str): The name of the column in the dataframe that contains the
text content to be embedded using the embedding model.
metadata (List[str]): A list of column names in the dataframe that contain
metadata to be stored in the database. Defaults to [].
"""
self.is_from_dataframe = True
actual_metadata = metadata.copy()
self.df_metadata_columns = actual_metadata # could be updated below
# get content column
content_values = df[content].values.tolist()
if "vector" not in df.columns:
embedding_vecs = self.embedding_fn(content_values)
df["vector"] = embedding_vecs
if content != "content":
# rename content column to "content", leave existing column intact
df = df.rename(columns={content: "content"}, inplace=False)
if "id" not in df.columns:
docs = dataframe_to_documents(df, content="content", metadata=metadata)
ids = [str(d.id()) for d in docs]
df["id"] = ids
if "id" not in actual_metadata:
actual_metadata += ["id"]
colls = self.list_collections(empty=True)
coll_name = self.config.collection_name
if (
coll_name not in colls
or self.client.open_table(coll_name).head(1).shape[0] == 0
):
# collection either doesn't exist or is empty, so replace it
# and set new schema from df
self.client.create_table(
self.config.collection_name,
data=df,
mode="overwrite",
on_bad_vectors="drop",
)
doc_cls = dataframe_to_document_model(
df,
content=content,
metadata=actual_metadata,
exclude=["vector"],
)
self.config.document_class = doc_cls # type: ignore
self._setup_schemas(doc_cls) # type: ignore
tbl = self.client.open_table(self.config.collection_name)
# We assume "content" is available as top-level field
if "content" in tbl.schema.names:
tbl.create_fts_index("content", replace=True)
else:
# collection exists and is not empty, so append to it
tbl = self.client.open_table(self.config.collection_name)
tbl.add(df)
if "content" in tbl.schema.names:
tbl.create_fts_index("content", replace=True)
def delete_collection(self, collection_name: str) -> None:
self.client.drop_table(collection_name)
def _lance_result_to_docs(self, result: LanceVectorQueryBuilder) -> List[Document]:
if self.is_from_dataframe:
df = result.to_pandas()
return dataframe_to_documents(
df,
content="content",
metadata=self.df_metadata_columns,
doc_cls=self.config.document_class,
)
else:
records = result.to_arrow().to_pylist()
return self._records_to_docs(records)
def _records_to_docs(self, records: List[Dict[str, Any]]) -> List[Document]:
if self.config.flatten:
docs = [
self.unflattened_schema(**nested_dict_from_flat(rec)) for rec in records
]
else:
try:
docs = [self.schema(**rec) for rec in records]
except ValidationError as e:
raise ValueError(
f"""
Error validating LanceDB result: {e}
HINT: This could happen when you're re-using an
existing LanceDB store with a different schema.
Try deleting your local lancedb storage at `{self.config.storage_path}`
re-ingesting your documents and/or replacing the collections.
"""
)
doc_cls = self.config.document_class
doc_cls_field_names = doc_cls.model_fields.keys()
return [
doc_cls(
**{
field_name: getattr(doc, field_name)
for field_name in doc_cls_field_names
}
)
for doc in docs
]
def get_all_documents(self, where: str = "") -> List[Document]:
if self.config.collection_name is None:
raise ValueError("No collection name set, cannot retrieve docs")
tbl = self.client.open_table(self.config.collection_name)
pre_result = tbl.search(None).where(where or None)
return self._lance_result_to_docs(pre_result)
def get_documents_by_ids(self, ids: List[str]) -> List[Document]:
if self.config.collection_name is None:
raise ValueError("No collection name set, cannot retrieve docs")
_ids = [str(id) for id in ids]
tbl = self.client.open_table(self.config.collection_name)
docs = [
self._lance_result_to_docs(tbl.search().where(f"id == '{_id}'"))
for _id in _ids
]
return docs
def similar_texts_with_scores(
self,
text: str,
k: int = 1,
where: Optional[str] = None,
) -> List[Tuple[Document, float]]:
embedding = self.embedding_fn([text])[0]
tbl = self.client.open_table(self.config.collection_name)
result = (
tbl.search(embedding).metric(self.config.distance).where(where).limit(k)
)
docs = self._lance_result_to_docs(result)
# note _distance is 1 - cosine
if self.is_from_dataframe:
scores = [
1 - rec["_distance"] for rec in result.to_pandas().to_dict("records")
]
else:
scores = [1 - rec["_distance"] for rec in result.to_arrow().to_pylist()]
if len(docs) == 0:
logger.warning(f"No matches found for {text}")
return []
if settings.debug:
logger.info(f"Found {len(docs)} matches, max score: {max(scores)}")
doc_score_pairs = list(zip(docs, scores))
self.show_if_debug(doc_score_pairs)
return doc_score_pairs
def get_fts_chunks(
self,
query: str,
k: int = 5,
where: Optional[str] = None,
) -> List[Tuple[Document, float]]:
"""
Uses LanceDB FTS (Full Text Search).
"""
# Clean up query: replace all newlines with spaces in query,
# force special search keywords to lower case, remove quotes,
# so it's not interpreted as code syntax
query_clean = (
query.replace("\n", " ")
.replace("AND", "and")
.replace("OR", "or")
.replace("NOT", "not")
.replace("'", "")
.replace('"', "")
)
tbl = self.client.open_table(self.config.collection_name)
tbl.create_fts_index(field_names="content", replace=True)
result = tbl.search(query_clean).where(where).limit(k).with_row_id(True)
docs = self._lance_result_to_docs(result)
scores = [r["score"] for r in result.to_list()]
return list(zip(docs, scores))
def _get_clean_vecdb_schema(self) -> str:
"""Get a cleaned schema of the vector-db, to pass to the LLM
as part of instructions on how to generate a SQL filter."""
if len(self.config.filter_fields) == 0:
filterable_fields = (
self.client.open_table(self.config.collection_name)
.search()
.limit(1)
.to_pandas(flatten=True)
.columns.tolist()
)
# drop id, vector, metadata.id, metadata.window_ids, metadata.is_chunk
for fields in [
"id",
"vector",
"metadata.id",
"metadata.window_ids",
"metadata.is_chunk",
]:
if fields in filterable_fields:
filterable_fields.remove(fields)
logger.warning(
f"""
No filter_fields set in config, so using these fields as filterable fields:
{filterable_fields}
"""
)
self.config.filter_fields = filterable_fields
if self.is_from_dataframe:
return self.is_from_dataframe
schema_dict = clean_schema(
self.schema,
excludes=["id", "vector"],
)
# intersect config.filter_fields with schema_dict.keys() in case
# there are extraneous fields in config.filter_fields
filter_fields_set = set(
self.config.filter_fields or schema_dict.keys()
).intersection(schema_dict.keys())
# remove 'content' from filter_fields_set, even if it's not in filter_fields_set
filter_fields_set.discard("content")
# possible values of filterable fields
filter_field_values = self.get_field_values(list(filter_fields_set))
# add field values to schema_dict as another field `values` for each field
for field, values in filter_field_values.items():
if field in schema_dict:
schema_dict[field]["values"] = values
# if self.config.filter_fields is set, restrict to these:
if len(self.config.filter_fields) > 0:
schema_dict = {
k: v for k, v in schema_dict.items() if k in self.config.filter_fields
}
schema = json.dumps(schema_dict, indent=2)
schema += f"""
NOTE when creating a filter for a query,
ONLY the following fields are allowed:
{",".join(self.config.filter_fields)}
"""
return schema
def get_field_values(self, fields: list[str]) -> Dict[str, str]:
"""Get string-listing of possible values of each filterable field,
e.g.
{
"genre": "crime, drama, mystery, ... (10 more)",
"certificate": "R, PG-13, PG, R",
}
"""
field_values: Dict[str, Set[str]] = {}
# make empty set for each field
for f in fields:
field_values[f] = set()
# get all documents and accumulate possible values of each field until 10
docs = self.get_all_documents()
for d in docs:
# extract fields from d
doc_field_vals = extract_fields(d, fields)
for field, val in doc_field_vals.items():
field_values[field].add(val)
# For each field make a string showing list of possible values,
# truncate to 20 values, and if there are more, indicate how many
# more there are, e.g. Genre: crime, drama, mystery, ... (20 more)
field_values_list = {}
for f in fields:
vals = list(field_values[f])
n = len(vals)
remaining = n - 20
vals = vals[:20]
if n > 20:
vals.append(f"(...{remaining} more)")
# make a string of the values, ensure they are strings
field_values_list[f] = ", ".join(str(v) for v in vals)
return field_values_list
| [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((877, 904), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (894, 904), False, 'import logging\n'), ((1067, 1091), 'src.embedding_models.models.OpenAIEmbeddingsConfig', 'OpenAIEmbeddingsConfig', ([], {}), '()\n', (1089, 1091), False, 'from src.embedding_models.models import OpenAIEmbeddingsConfig\n'), ((1569, 1608), 'src.embedding_models.base.EmbeddingModel.create', 'EmbeddingModel.create', (['config.embedding'], {}), '(config.embedding)\n', (1590, 1608), False, 'from src.embedding_models.base import EmbeddingModel, EmbeddingModelsConfig\n'), ((2008, 2021), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (2019, 2021), False, 'from dotenv import load_dotenv\n'), ((6584, 6639), 'pydantic.create_model', 'create_model', (['"""NewModel"""'], {'__base__': 'LanceModel'}), "('NewModel', __base__=LanceModel, **fields)\n", (6596, 6639), False, 'from pydantic import BaseModel, ValidationError, create_model\n'), ((7016, 7074), 'src.utils.pydantic_utils.flatten_pydantic_model', 'flatten_pydantic_model', (['lance_model'], {'base_model': 'LanceModel'}), '(lance_model, base_model=LanceModel)\n', (7038, 7074), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((19539, 19591), 'src.utils.pydantic_utils.clean_schema', 'clean_schema', (['self.schema'], {'excludes': "['id', 'vector']"}), "(self.schema, excludes=['id', 'vector'])\n", (19551, 19591), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((20650, 20683), 'json.dumps', 'json.dumps', (['schema_dict'], {'indent': '(2)'}), '(schema_dict, indent=2)\n', (20660, 20683), False, 'import json\n'), ((2062, 2102), 'lancedb.connect', 'lancedb.connect', ([], {'uri': 'config.storage_path'}), '(uri=config.storage_path)\n', (2077, 2102), False, 'import lancedb\n'), ((12047, 12111), 'src.utils.pydantic_utils.dataframe_to_documents', 'dataframe_to_documents', (['df'], {'content': '"""content"""', 'metadata': 'metadata'}), "(df, content='content', metadata=metadata)\n", (12069, 12111), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((12824, 12922), 'src.utils.pydantic_utils.dataframe_to_document_model', 'dataframe_to_document_model', (['df'], {'content': 'content', 'metadata': 'actual_metadata', 'exclude': "['vector']"}), "(df, content=content, metadata=actual_metadata,\n exclude=['vector'])\n", (12851, 12922), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((13937, 14058), 'src.utils.pydantic_utils.dataframe_to_documents', 'dataframe_to_documents', (['df'], {'content': '"""content"""', 'metadata': 'self.df_metadata_columns', 'doc_cls': 'self.config.document_class'}), "(df, content='content', metadata=self.\n df_metadata_columns, doc_cls=self.config.document_class)\n", (13959, 14058), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((21541, 21566), 'src.utils.pydantic_utils.extract_fields', 'extract_fields', (['d', 'fields'], {}), '(d, fields)\n', (21555, 21566), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((2478, 2515), 'lancedb.connect', 'lancedb.connect', ([], {'uri': 'new_storage_path'}), '(uri=new_storage_path)\n', (2493, 2515), False, 'import lancedb\n'), ((6291, 6300), 'lancedb.pydantic.Vector', 'Vector', (['n'], {}), '(n)\n', (6297, 6300), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((10171, 10206), 'src.utils.pydantic_utils.flatten_pydantic_instance', 'flatten_pydantic_instance', (['instance'], {}), '(instance)\n', (10196, 10206), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n'), ((14426, 14452), 'src.utils.pydantic_utils.nested_dict_from_flat', 'nested_dict_from_flat', (['rec'], {}), '(rec)\n', (14447, 14452), False, 'from src.utils.pydantic_utils import clean_schema, dataframe_to_document_model, dataframe_to_documents, extract_fields, flatten_pydantic_instance, flatten_pydantic_model, nested_dict_from_flat\n')] |
from datasets import load_dataset
import os
import lancedb
import getpass
import time
import argparse
from tqdm.auto import tqdm
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector
def main(query=None):
if "COHERE_API_KEY" not in os.environ:
os.environ["COHERE_API_KEY"] = getpass.getpass("Enter your Cohere API key: ")
en = dataset = load_dataset(
"wikipedia",
"20220301.en",
streaming=True,
)
fr = load_dataset("wikipedia", "20220301.fr", streaming=True)
datasets = {"english": iter(en["train"]), "french": iter(fr["train"])}
registry = EmbeddingFunctionRegistry().get_instance()
cohere = registry.get(
"cohere"
).create() # uses multi-lingual model by default (768 dim)
class Schema(LanceModel):
vector: Vector(cohere.ndims()) = cohere.VectorField()
text: str = cohere.SourceField()
url: str
title: str
id: str
lang: str
db = lancedb.connect("~/lancedb")
tbl = (
db.create_table("wikipedia-cohere", schema=Schema, mode="overwrite")
if "wikipedia-cohere" not in db
else db.open_table("wikipedia-cohere")
)
# let's use cohere embeddings. Use can also set it to openai version of the table
batch_size = 1000
num_records = 10000
data = []
for i in tqdm(range(0, num_records, batch_size)):
for lang, dataset in datasets.items():
batch = [next(dataset) for _ in range(batch_size)]
texts = [x["text"] for x in batch]
ids = [f"{x['id']}-{lang}" for x in batch]
data.extend(
{
"text": x["text"],
"title": x["title"],
"url": x["url"],
"lang": lang,
"id": f"{lang}-{x['id']}",
}
for x in batch
)
# add in batches to avoid token limit
tbl.add(data)
data = []
print("Added batch. Sleeping for 20 seconds to avoid rate limit")
time.sleep(20) # wait for 20 seconds to avoid rate limit
if not query:
it = iter(fr["train"])
for i in range(5):
next(it)
query = next(it)
rs = tbl.search(query["text"]).limit(3).to_list()
print("Query: ", query["text"])
print("Results: ", rs)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--query", type=str, default="", help="Query to search")
args = parser.parse_args()
main(query=args.query)
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"lancedb.connect",
"lancedb.embeddings.EmbeddingFunctionRegistry"
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from typing import Optional
from pydantic import BaseModel, ConfigDict, Field, model_validator
from lancedb.pydantic import LanceModel, Vector
class Wine(BaseModel):
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
extra="allow",
str_strip_whitespace=True,
json_schema_extra={
"example": {
"id": 45100,
"points": 85,
"title": "Balduzzi 2012 Reserva Merlot (Maule Valley)",
"description": "Ripe in color and aromas, this chunky wine delivers heavy baked-berry and raisin aromas in front of a jammy, extracted palate. Raisin and cooked berry flavors finish plump, with earthy notes.",
"price": 10.0,
"variety": "Merlot",
"winery": "Balduzzi",
"vineyard": "Reserva",
"country": "Chile",
"province": "Maule Valley",
"region_1": "null",
"region_2": "null",
"taster_name": "Michael Schachner",
"taster_twitter_handle": "@wineschach",
}
},
)
id: int
points: int
title: str
description: Optional[str]
price: Optional[float]
variety: Optional[str]
winery: Optional[str]
vineyard: Optional[str] = Field(..., alias="designation")
country: Optional[str]
province: Optional[str]
region_1: Optional[str]
region_2: Optional[str]
taster_name: Optional[str]
taster_twitter_handle: Optional[str]
@model_validator(mode="before")
def _fill_country_unknowns(cls, values):
"Fill in missing country values with 'Unknown', as we always want this field to be queryable"
country = values.get("country")
if not country:
values["country"] = "Unknown"
return values
@model_validator(mode="before")
def _add_to_vectorize_fields(cls, values):
"Add a field to_vectorize that will be used to create sentence embeddings"
variety = values.get("variety", "")
title = values.get("title", "")
description = values.get("description", "")
to_vectorize = list(filter(None, [variety, title, description]))
values["to_vectorize"] = " ".join(to_vectorize).strip()
return values
class LanceModelWine(BaseModel):
"""
Pydantic model for LanceDB, with a vector field added for sentence embeddings
"""
id: int
points: int
title: str
description: Optional[str]
price: Optional[float]
variety: Optional[str]
winery: Optional[str]
vineyard: Optional[str] = Field(..., alias="designation")
country: Optional[str]
province: Optional[str]
region_1: Optional[str]
region_2: Optional[str]
taster_name: Optional[str]
taster_twitter_handle: Optional[str]
to_vectorize: str
vector: Vector(384)
class SearchResult(LanceModel):
"Model to return search results"
model_config = ConfigDict(
extra="ignore",
json_schema_extra={
"example": {
"id": 374,
"title": "Borgo Conventi 2002 I Fiori del Borgo Sauvignon Blanc (Collio)",
"description": "Crisp, green, grassy wine with fresh acidity and herbeceous character. It is very New World with its tropical flavors and open, forward fruit.",
"country": "Italy",
"variety": "Sauvignon Blanc",
"price": 15,
"points": 88,
}
},
)
id: int
title: str
description: Optional[str]
country: Optional[str]
variety: Optional[str]
price: Optional[float]
points: Optional[int]
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"lancedb.pydantic.Vector"
] | [((189, 894), 'pydantic.ConfigDict', 'ConfigDict', ([], {'populate_by_name': '(True)', 'validate_assignment': '(True)', 'extra': '"""allow"""', 'str_strip_whitespace': '(True)', 'json_schema_extra': "{'example': {'id': 45100, 'points': 85, 'title':\n 'Balduzzi 2012 Reserva Merlot (Maule Valley)', 'description':\n 'Ripe in color and aromas, this chunky wine delivers heavy baked-berry and raisin aromas in front of a jammy, extracted palate. Raisin and cooked berry flavors finish plump, with earthy notes.'\n , 'price': 10.0, 'variety': 'Merlot', 'winery': 'Balduzzi', 'vineyard':\n 'Reserva', 'country': 'Chile', 'province': 'Maule Valley', 'region_1':\n 'null', 'region_2': 'null', 'taster_name': 'Michael Schachner',\n 'taster_twitter_handle': '@wineschach'}}"}), "(populate_by_name=True, validate_assignment=True, extra='allow',\n str_strip_whitespace=True, json_schema_extra={'example': {'id': 45100,\n 'points': 85, 'title': 'Balduzzi 2012 Reserva Merlot (Maule Valley)',\n 'description':\n 'Ripe in color and aromas, this chunky wine delivers heavy baked-berry and raisin aromas in front of a jammy, extracted palate. Raisin and cooked berry flavors finish plump, with earthy notes.'\n , 'price': 10.0, 'variety': 'Merlot', 'winery': 'Balduzzi', 'vineyard':\n 'Reserva', 'country': 'Chile', 'province': 'Maule Valley', 'region_1':\n 'null', 'region_2': 'null', 'taster_name': 'Michael Schachner',\n 'taster_twitter_handle': '@wineschach'}})\n", (199, 894), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n'), ((1355, 1386), 'pydantic.Field', 'Field', (['...'], {'alias': '"""designation"""'}), "(..., alias='designation')\n", (1360, 1386), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n'), ((1576, 1606), 'pydantic.model_validator', 'model_validator', ([], {'mode': '"""before"""'}), "(mode='before')\n", (1591, 1606), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n'), ((1888, 1918), 'pydantic.model_validator', 'model_validator', ([], {'mode': '"""before"""'}), "(mode='before')\n", (1903, 1918), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n'), ((2662, 2693), 'pydantic.Field', 'Field', (['...'], {'alias': '"""designation"""'}), "(..., alias='designation')\n", (2667, 2693), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n'), ((2911, 2922), 'lancedb.pydantic.Vector', 'Vector', (['(384)'], {}), '(384)\n', (2917, 2922), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((3014, 3422), 'pydantic.ConfigDict', 'ConfigDict', ([], {'extra': '"""ignore"""', 'json_schema_extra': "{'example': {'id': 374, 'title':\n 'Borgo Conventi 2002 I Fiori del Borgo Sauvignon Blanc (Collio)',\n 'description':\n 'Crisp, green, grassy wine with fresh acidity and herbeceous character. It is very New World with its tropical flavors and open, forward fruit.'\n , 'country': 'Italy', 'variety': 'Sauvignon Blanc', 'price': 15,\n 'points': 88}}"}), "(extra='ignore', json_schema_extra={'example': {'id': 374,\n 'title':\n 'Borgo Conventi 2002 I Fiori del Borgo Sauvignon Blanc (Collio)',\n 'description':\n 'Crisp, green, grassy wine with fresh acidity and herbeceous character. It is very New World with its tropical flavors and open, forward fruit.'\n , 'country': 'Italy', 'variety': 'Sauvignon Blanc', 'price': 15,\n 'points': 88}})\n", (3024, 3422), False, 'from pydantic import BaseModel, ConfigDict, Field, model_validator\n')] |
from typing import Any
from lancedb.embeddings import EmbeddingFunctionRegistry
def register_model(model_name: str) -> Any:
"""
Register a model with the given name using LanceDB's EmbeddingFunctionRegistry.
Args:
model_name (str): The name of the model to register.
Returns:
model: The registered model instance.
Usage:
>>> model = register_model("open-clip")
"""
registry = EmbeddingFunctionRegistry.get_instance()
model = registry.get(model_name).create()
return model
| [
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
] | [((430, 470), 'lancedb.embeddings.EmbeddingFunctionRegistry.get_instance', 'EmbeddingFunctionRegistry.get_instance', ([], {}), '()\n', (468, 470), False, 'from lancedb.embeddings import EmbeddingFunctionRegistry\n')] |
#!/usr/bin/env python
import os
import lancedb
from lancedb.embeddings import with_embeddings
import openai
import pandas as pd
from pydantic import BaseModel, Field
import requests
from aifunctools.openai_funcs import complete_with_functions
openai.api_key = os.getenv("OPENAI_API_KEY")
MODEL = "gpt-3.5-turbo-16k-0613"
db = lancedb.connect(".lancedb")
def embed_func(c):
rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
return [record["embedding"] for record in rs["data"]]
def to_lancedb_table(db, memes):
df = pd.DataFrame([m.model_dump() for m in memes])
data = with_embeddings(embed_func, df, column="name")
if "memes" in db.table_names():
tbl = db.open_table("memes")
tbl.add(data, mode="overwrite")
else:
tbl = db.create_table("memes", data)
return tbl
class Meme(BaseModel):
id: str = Field(description="The meme id")
name: str = Field(description="The meme name")
url: str = Field(description="The meme url")
width: int = Field(description="The meme image width")
height: int = Field(description="The meme image height")
box_count: int = Field(description="The number of text boxes in the meme")
def get_memes():
"""
Get a list of memes from the meme api
"""
resp = requests.get("https://api.imgflip.com/get_memes")
return [Meme(**m) for m in resp.json()["data"]["memes"]]
def search_memes(query: str):
"""
Get the most popular memes from imgflip and do a semantic search based on the user query
:param query: str, the search string
"""
memes = get_memes()
tbl = to_lancedb_table(db, memes)
df = tbl.search(embed_func(query)[0]).limit(1).to_df()
return Meme(**df.to_dict(orient="records")[0]).model_dump()
if __name__ == "__main__":
question = "Please find me the image link for that popular meme with Fry from Futurama"
print(complete_with_functions(question, search_memes)["choices"][0]["message"]["content"])
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((263, 290), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (272, 290), False, 'import os\n'), ((331, 358), 'lancedb.connect', 'lancedb.connect', (['""".lancedb"""'], {}), "('.lancedb')\n", (346, 358), False, 'import lancedb\n'), ((389, 454), 'openai.Embedding.create', 'openai.Embedding.create', ([], {'input': 'c', 'engine': '"""text-embedding-ada-002"""'}), "(input=c, engine='text-embedding-ada-002')\n", (412, 454), False, 'import openai\n'), ((614, 660), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embed_func', 'df'], {'column': '"""name"""'}), "(embed_func, df, column='name')\n", (629, 660), False, 'from lancedb.embeddings import with_embeddings\n'), ((883, 915), 'pydantic.Field', 'Field', ([], {'description': '"""The meme id"""'}), "(description='The meme id')\n", (888, 915), False, 'from pydantic import BaseModel, Field\n'), ((932, 966), 'pydantic.Field', 'Field', ([], {'description': '"""The meme name"""'}), "(description='The meme name')\n", (937, 966), False, 'from pydantic import BaseModel, Field\n'), ((982, 1015), 'pydantic.Field', 'Field', ([], {'description': '"""The meme url"""'}), "(description='The meme url')\n", (987, 1015), False, 'from pydantic import BaseModel, Field\n'), ((1033, 1074), 'pydantic.Field', 'Field', ([], {'description': '"""The meme image width"""'}), "(description='The meme image width')\n", (1038, 1074), False, 'from pydantic import BaseModel, Field\n'), ((1093, 1135), 'pydantic.Field', 'Field', ([], {'description': '"""The meme image height"""'}), "(description='The meme image height')\n", (1098, 1135), False, 'from pydantic import BaseModel, Field\n'), ((1157, 1214), 'pydantic.Field', 'Field', ([], {'description': '"""The number of text boxes in the meme"""'}), "(description='The number of text boxes in the meme')\n", (1162, 1214), False, 'from pydantic import BaseModel, Field\n'), ((1303, 1352), 'requests.get', 'requests.get', (['"""https://api.imgflip.com/get_memes"""'], {}), "('https://api.imgflip.com/get_memes')\n", (1315, 1352), False, 'import requests\n'), ((1913, 1960), 'aifunctools.openai_funcs.complete_with_functions', 'complete_with_functions', (['question', 'search_memes'], {}), '(question, search_memes)\n', (1936, 1960), False, 'from aifunctools.openai_funcs import complete_with_functions\n')] |
import lancedb
import uuid
from datetime import datetime
from tqdm import tqdm
from typing import Optional, List, Iterator, Dict
from memgpt.config import MemGPTConfig
from memgpt.connectors.storage import StorageConnector, TableType
from memgpt.config import AgentConfig, MemGPTConfig
from memgpt.constants import MEMGPT_DIR
from memgpt.utils import printd
from memgpt.data_types import Record, Message, Passage, Source
from datetime import datetime
from lancedb.pydantic import Vector, LanceModel
""" Initial implementation - not complete """
def get_db_model(table_name: str, table_type: TableType):
config = MemGPTConfig.load()
if table_type == TableType.ARCHIVAL_MEMORY or table_type == TableType.PASSAGES:
# create schema for archival memory
class PassageModel(LanceModel):
"""Defines data model for storing Passages (consisting of text, embedding)"""
id: uuid.UUID
user_id: str
text: str
doc_id: str
agent_id: str
data_source: str
embedding: Vector(config.embedding_dim)
metadata_: Dict
def __repr__(self):
return f"<Passage(passage_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
def to_record(self):
return Passage(
text=self.text,
embedding=self.embedding,
doc_id=self.doc_id,
user_id=self.user_id,
id=self.id,
data_source=self.data_source,
agent_id=self.agent_id,
metadata=self.metadata_,
)
return PassageModel
elif table_type == TableType.RECALL_MEMORY:
class MessageModel(LanceModel):
"""Defines data model for storing Message objects"""
__abstract__ = True # this line is necessary
# Assuming message_id is the primary key
id: uuid.UUID
user_id: str
agent_id: str
# openai info
role: str
text: str
model: str
user: str
# function info
function_name: str
function_args: str
function_response: str
embedding = Vector(config.embedding_dim)
# Add a datetime column, with default value as the current time
created_at = datetime
def __repr__(self):
return f"<Message(message_id='{self.id}', text='{self.text}', embedding='{self.embedding})>"
def to_record(self):
return Message(
user_id=self.user_id,
agent_id=self.agent_id,
role=self.role,
user=self.user,
text=self.text,
model=self.model,
function_name=self.function_name,
function_args=self.function_args,
function_response=self.function_response,
embedding=self.embedding,
created_at=self.created_at,
id=self.id,
)
"""Create database model for table_name"""
return MessageModel
elif table_type == TableType.DATA_SOURCES:
class SourceModel(LanceModel):
"""Defines data model for storing Passages (consisting of text, embedding)"""
# Assuming passage_id is the primary key
id: uuid.UUID
user_id: str
name: str
created_at: datetime
def __repr__(self):
return f"<Source(passage_id='{self.id}', name='{self.name}')>"
def to_record(self):
return Source(id=self.id, user_id=self.user_id, name=self.name, created_at=self.created_at)
"""Create database model for table_name"""
return SourceModel
else:
raise ValueError(f"Table type {table_type} not implemented")
class LanceDBConnector(StorageConnector):
"""Storage via LanceDB"""
# TODO: this should probably eventually be moved into a parent DB class
def __init__(self, name: Optional[str] = None, agent_config: Optional[AgentConfig] = None):
# TODO
pass
def generate_where_filter(self, filters: Dict) -> str:
where_filters = []
for key, value in filters.items():
where_filters.append(f"{key}={value}")
return where_filters.join(" AND ")
@abstractmethod
def get_all_paginated(self, filters: Optional[Dict] = {}, page_size: Optional[int] = 1000) -> Iterator[List[Record]]:
# TODO
pass
@abstractmethod
def get_all(self, filters: Optional[Dict] = {}, limit=10) -> List[Record]:
# TODO
pass
@abstractmethod
def get(self, id: str) -> Optional[Record]:
# TODO
pass
@abstractmethod
def size(self, filters: Optional[Dict] = {}) -> int:
# TODO
pass
@abstractmethod
def insert(self, record: Record):
# TODO
pass
@abstractmethod
def insert_many(self, records: List[Record], show_progress=False):
# TODO
pass
@abstractmethod
def query(self, query: str, query_vec: List[float], top_k: int = 10, filters: Optional[Dict] = {}) -> List[Record]:
# TODO
pass
@abstractmethod
def query_date(self, start_date, end_date):
# TODO
pass
@abstractmethod
def query_text(self, query):
# TODO
pass
@abstractmethod
def delete_table(self):
# TODO
pass
@abstractmethod
def delete(self, filters: Optional[Dict] = {}):
# TODO
pass
@abstractmethod
def save(self):
# TODO
pass
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""" Install lancedb with instructor embedding support
copy this and paste it in the terminal, and install additional dependencies via requirements.txt file
pip install git+https://github.com/lancedb/lancedb.git@main#subdirectory=python
"""
import lancedb
from lancedb.pydantic import LanceModel, Vector
from lancedb.embeddings import get_registry
from lancedb.embeddings import InstructorEmbeddingFunction
instructor = (
get_registry()
.get("instructor")
.create(
source_instruction="represent the document for retreival",
query_instruction="represent the document for most similar definition",
)
)
class Schema(LanceModel):
vector: Vector(instructor.ndims()) = instructor.VectorField()
text: str = instructor.SourceField()
# Creating LanceDB table
db = lancedb.connect("~/.lancedb")
tbl = db.create_table("intruct-multitask", schema=Schema, mode="overwrite")
data_f1 = [
{
"text": "Aspirin is a widely-used over-the-counter medication known for its anti-inflammatory and analgesic properties. It is commonly used to relieve pain, reduce fever, and alleviate minor aches and pains."
},
{
"text": "Amoxicillin is an antibiotic medication commonly prescribed to treat various bacterial infections, such as respiratory, ear, throat, and urinary tract infections. It belongs to the penicillin class of antibiotics and works by inhibiting bacterial cell wall synthesis."
},
{
"text": "Atorvastatin is a lipid-lowering medication used to manage high cholesterol levels and reduce the risk of cardiovascular events. It belongs to the statin class of drugs and works by inhibiting an enzyme involved in cholesterol production in the liver."
},
{
"text": "The Theory of Relativity is a fundamental physics theory developed by Albert Einstein, consisting of the special theory of relativity and the general theory of relativity. It revolutionized our understanding of space, time, and gravity."
},
{
"text": "Photosynthesis is a vital biological process by which green plants, algae, and some bacteria convert light energy into chemical energy in the form of glucose, using carbon dioxide and water."
},
{
"text": "The Big Bang Theory is the prevailing cosmological model that describes the origin of the universe. It suggests that the universe began as a singularity and has been expanding for billions of years."
},
{
"text": "Compound Interest is the addition of interest to the principal sum of a loan or investment, resulting in the interest on interest effect over time."
},
{
"text": "Stock Market is a financial marketplace where buyers and sellers trade ownership in companies, typically in the form of stocks or shares."
},
{
"text": "Inflation is the rate at which the general level of prices for goods and services is rising and subsequently purchasing power is falling."
},
{
"text": "Diversification is an investment strategy that involves spreading your investments across different asset classes to reduce risk."
},
{
"text": "Liquidity refers to how easily an asset can be converted into cash without a significant loss of value. It's a key consideration in financial management."
},
{
"text": "401(k) is a retirement savings plan offered by employers, allowing employees to save and invest a portion of their paycheck before taxes."
},
{
"text": "Ballet is a classical dance form that originated in the Italian Renaissance courts of the 15th century and later developed into a highly technical art."
},
{
"text": "Rock and Roll is a genre of popular music that originated and evolved in the United States during the late 1940s and early 1950s, characterized by a strong rhythm and amplified instruments."
},
{
"text": "Cuisine is a style or method of cooking, especially as characteristic of a particular country, region, or establishment."
},
{"text": "Renaissance was a cultural, artistic, and intellectual movement that"},
{
"text": "Neutrino is subatomic particles with very little mass and no electric charge. They are produced in various nuclear reactions, including those in the Sun, and play a significant role in astrophysics and particle physics."
},
{
"text": "Higgs Boson is a subatomic particle that gives mass to other elementary particles. Its discovery was a significant achievement in particle physics."
},
{
"text": "Quantum Entanglement is a quantum physics phenomenon where two or more particles become connected in such a way that the state of one particle is dependent on the state of the other(s), even when they are separated by large distances."
},
{
"text": "Genome Sequencing is the process of determining the complete DNA sequence of an organism's genome. It has numerous applications in genetics, biology, and medicine."
},
]
tbl.add(data_f1)
# LanceDB supports full text search, so there is no need of embedding the Query manually
query = "amoxicillin"
result = tbl.search(query).limit(1).to_pandas()
# printing the output
print(result)
#########################################################################################################################
################# SAME INPUT DATA WITH DIFFERENT INSTRUCTION PAIR #######################################################
#########################################################################################################################
# uncomment the below code to check for different instruction pair on the same data
"""instructor = get_registry().get("instructor").create(
source_instruction="represent the captions",
query_instruction="represent the captions for retrieving duplicate captions"
)
class Schema(LanceModel):
vector: Vector(instructor.ndims()) = instructor.VectorField()
text: str = instructor.SourceField()
db = lancedb.connect("~/.lancedb")
tbl = db.create_table("intruct-multitask", schema=Schema, mode="overwrite")
data_f2 = [
{"text": "Aspirin is a widely-used over-the-counter medication known for its anti-inflammatory and analgesic properties. It is commonly used to relieve pain, reduce fever, and alleviate minor aches and pains."},
{"text": "Amoxicillin is an antibiotic medication commonly prescribed to treat various bacterial infections, such as respiratory, ear, throat, and urinary tract infections. It belongs to the penicillin class of antibiotics and works by inhibiting bacterial cell wall synthesis."},
{"text": "Atorvastatin is a lipid-lowering medication used to manage high cholesterol levels and reduce the risk of cardiovascular events. It belongs to the statin class of drugs and works by inhibiting an enzyme involved in cholesterol production in the liver."},
{"text": "The Theory of Relativity is a fundamental physics theory developed by Albert Einstein, consisting of the special theory of relativity and the general theory of relativity. It revolutionized our understanding of space, time, and gravity."},
{"text": "Photosynthesis is a vital biological process by which green plants, algae, and some bacteria convert light energy into chemical energy in the form of glucose, using carbon dioxide and water."},
{"text": "The Big Bang Theory is the prevailing cosmological model that describes the origin of the universe. It suggests that the universe began as a singularity and has been expanding for billions of years."},
{"text": "Compound Interest is the addition of interest to the principal sum of a loan or investment, resulting in the interest on interest effect over time."},
{"text": "Stock Market is a financial marketplace where buyers and sellers trade ownership in companies, typically in the form of stocks or shares."},
{"text": "Inflation is the rate at which the general level of prices for goods and services is rising and subsequently purchasing power is falling."},
{"text": "Diversification is an investment strategy that involves spreading your investments across different asset classes to reduce risk."},
{"text": "Liquidity refers to how easily an asset can be converted into cash without a significant loss of value. It's a key consideration in financial management."},
{"text": "401(k) is a retirement savings plan offered by employers, allowing employees to save and invest a portion of their paycheck before taxes."},
{"text": "Ballet is a classical dance form that originated in the Italian Renaissance courts of the 15th century and later developed into a highly technical art."},
{"text": "Rock and Roll is a genre of popular music that originated and evolved in the United States during the late 1940s and early 1950s, characterized by a strong rhythm and amplified instruments."},
{"text": "Cuisine is a style or method of cooking, especially as characteristic of a particular country, region, or establishment."},
{"text": "Renaissance was a cultural, artistic, and intellectual movement that"},
{"text": "Neutrino is subatomic particles with very little mass and no electric charge. They are produced in various nuclear reactions, including those in the Sun, and play a significant role in astrophysics and particle physics."},
{"text": "Higgs Boson is a subatomic particle that gives mass to other elementary particles. Its discovery was a significant achievement in particle physics."},
{"text": "Quantum Entanglement is a quantum physics phenomenon where two or more particles become connected in such a way that the state of one particle is dependent on the state of the other(s), even when they are separated by large distances."},
{"text": "Genome Sequencing is the process of determining the complete DNA sequence of an organism's genome. It has numerous applications in genetics, biology, and medicine."},
]
tbl.add(data_f2)
#same query, but for the differently embed data
query = "amoxicillin"
result = tbl.search(query).limit(1).to_pandas()
#showing the result
print(result)
"""
| [
"lancedb.connect",
"lancedb.embeddings.get_registry"
] | [((818, 847), 'lancedb.connect', 'lancedb.connect', (['"""~/.lancedb"""'], {}), "('~/.lancedb')\n", (833, 847), False, 'import lancedb\n'), ((445, 459), 'lancedb.embeddings.get_registry', 'get_registry', ([], {}), '()\n', (457, 459), False, 'from lancedb.embeddings import get_registry\n')] |
from pathlib import Path
from uuid import uuid4
from langchain.document_loaders import TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
import lancedb
from knowledge_graph.configuration.config import cfg
from lancedb import DBConnection
def check_if_embedding_exists(text: str):
db = lancedb.connect(cfg.db_path)
tbl_text = db.open_table("knowledge_graph_text")
df = tbl_text.search(text).to_pandas(flatten=True)
print(df.text)
if text in df.text.values.astype(str):
return True
else:
return False
async def create_embeddings_text(text: str):
db = lancedb.connect(cfg.db_path)
table_text = db.create_table(
name=f"knowledge_graph_text",
data=[
{
"vector": cfg.emb_func.embed_query("Placeholder"),
"text": "Placeholder",
"id": "1",
}
],
mode="overwrite",
)
text_splitter = CharacterTextSplitter(chunk_size=cfg.chunk_size, chunk_overlap=0)
documents = text_splitter.split_text(text)
db_text = LanceDB.from_texts(documents, cfg.emb_func, connection=table_text)
return db_text
async def create_embeddings_summary(summary_path: Path):
db = lancedb.connect(cfg.db_path)
table_summary = db.create_table(
name=f"knowledge_graph_summary",
data=[
{
"vector": cfg.emb_func.embed_query("Placeholder"),
"text": "Placeholder",
"id": "1",
}
],
mode="overwrite",
)
loader = TextLoader(summary_path.as_posix())
docs_summary = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=cfg.chunk_size, chunk_overlap=0)
doc = text_splitter.split_documents(docs_summary)
db_summary = LanceDB.from_documents(doc, cfg.emb_func, connection=table_summary)
return db_summary
async def similarity_search(query: str):
db = lancedb.connect(cfg.db_path)
tbl_text = db.open_table("knowledge_graph_text")
tbl_summary = db.open_table("knowledge_graph_summary")
vectorstore_text = LanceDB(tbl_text, cfg.emb_func)
result_text = vectorstore_text.similarity_search(query)
ans_text = result_text[0].page_content
vectorstore_summary = LanceDB(tbl_summary, cfg.emb_func)
result_summary = vectorstore_summary.similarity_search(query)
ans_summary = result_summary[0].page_content
return ans_text, ans_summary
if __name__ == "__main__":
input_val = """Animals are the most adorable and loving creatures existing on Earth. They might not be able to speak, but they can understand. They have a unique mode of interaction which is beyond human understanding. There are two types of animals: domestic and wild animals.
Domestic Animals | Domestic animals such as dogs, cows, cats, donkeys, mules and elephants are the ones which are used for the purpose of domestication. Wild animals refer to animals that are not normally domesticated and generally live in forests. They are important for their economic, survival, beauty, and scientific value.
Wild Animals | Wild animals provide various useful substances and animal products such as honey, leather, ivory, tusk, etc. They are of cultural asset and aesthetic value to humankind. Human life largely depends on wild animals for elementary requirements like the medicines we consume and the clothes we wear daily.
Nature and wildlife are largely associated with humans for several reasons, such as emotional and social issues. The balanced functioning of the biosphere depends on endless interactions among microorganisms, plants and animals. This has led to countless efforts by humans for the conservation of animals and to protect them from extinction. Animals have occupied a special place of preservation and veneration in various cultures worldwide."""
print(check_if_embedding_exists(input_val))
#path = Path(r"C:\tmp\graph_desc\graph_desc_310150f8-a4a8-4ba9-b1c7-07bc5b4944d1.txt")
#db = create_embeddings_summary(path)
#print(db)
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"lancedb.connect"
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from glob import glob
from os.path import basename
from pathlib import Path
import chromadb
import lancedb
import pandas as pd
import torch
from chromadb.utils import embedding_functions
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector
from loguru import logger
from rich import print
from rich.progress import track
MODEL_NAME = "all-distilroberta-v1"
DB_PATH = "db/lancedb-test"
TABLE_NAME = COLLECTION_NAME = "test"
registry = EmbeddingFunctionRegistry.get_instance()
func = registry.get("sentence-transformers").create(
name="all-distilroberta-v1", device="cuda" if torch.cuda.is_available() else "cpu"
)
class Document(LanceModel):
document: str = func.SourceField()
embedding: Vector(func.ndims()) = func.VectorField()
source: str
def get_collection() -> chromadb.Collection:
chroma_client = chromadb.PersistentClient(DB_PATH)
try:
collection = chroma_client.get_collection(name=COLLECTION_NAME)
except Exception as e:
logger.exception(e)
logger.warning("Indexing documents...")
collection = chroma_client.create_collection(name=COLLECTION_NAME)
csvs = glob("crawled/*.csv")
sentence_transformer_ef = (
embedding_functions.SentenceTransformerEmbeddingFunction(
model_name=MODEL_NAME
)
)
data = []
for csv in track(csvs):
df = pd.read_csv(csv)
if len(df) == 0:
continue
urls, documents = df["URL"].tolist(), df["Section Content"].tolist()
embeddings = sentence_transformer_ef(documents)
assert len(urls) == len(documents) == len(embeddings)
base = basename(urls[0])
collection.add(
embeddings=embeddings,
documents=documents,
metadatas=[{"source": url} for url in urls],
ids=[f"{base}_{i}" for i in range(len(documents))],
)
return collection
def get_table():
uri = DB_PATH[:]
db = lancedb.connect(uri)
table = db.open_table(TABLE_NAME)
return table
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"lancedb.connect",
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import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
batch['cls'] = batch['cls'].flatten().int().tolist()
box_cls_pair = sorted(zip(batch['bboxes'].tolist(), batch['cls']), key=lambda x: x[1])
batch['bboxes'] = [box for box, _ in box_cls_pair]
batch['cls'] = [cls for _, cls in box_cls_pair]
batch['labels'] = [dataset_info['names'][i] for i in batch['cls']]
batch['masks'] = batch['masks'].tolist() if 'masks' in batch else [[[]]]
batch['keypoints'] = batch['keypoints'].tolist() if 'keypoints' in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = similar_set.to_dict(
orient='list') if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get('im_file', [])
bboxes = similar_set.get('bboxes', []) if similar_set.get('bboxes') is not empty_boxes else []
masks = similar_set.get('masks') if similar_set.get('masks')[0] != empty_masks else []
kpts = similar_set.get('keypoints') if similar_set.get('keypoints')[0] != empty_masks else []
cls = similar_set.get('cls', [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(imgs,
batch_idx,
cls,
bboxes=boxes,
masks=masks,
kpts=kpts,
max_subplots=len(images),
save=False,
threaded=False)
def prompt_sql_query(query):
check_requirements('openai>=1.6.1')
from openai import OpenAI
if not SETTINGS['openai_api_key']:
logger.warning('OpenAI API key not found in settings. Please enter your API key below.')
openai_api_key = getpass.getpass('OpenAI API key: ')
SETTINGS.update({'openai_api_key': openai_api_key})
openai = OpenAI(api_key=SETTINGS['openai_api_key'])
messages = [
{
'role':
'system',
'content':
'''
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
'''},
{
'role': 'user',
'content': f'{query}'}, ]
response = openai.chat.completions.create(model='gpt-3.5-turbo', messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3411, 3433), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3419, 3433), True, 'import numpy as np\n'), ((3771, 3804), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (3785, 3804), True, 'import numpy as np\n'), ((4239, 4274), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4257, 4274), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4576, 4618), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4582, 4618), False, 'from openai import OpenAI\n'), ((695, 714), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (701, 714), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2463, 2478), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2473, 2478), False, 'import cv2\n'), ((2492, 2527), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2504, 2527), False, 'import cv2\n'), ((3446, 3474), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3454, 3474), True, 'import numpy as np\n'), ((3503, 3530), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3511, 3530), True, 'import numpy as np\n'), ((3542, 3575), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3556, 3575), True, 'import numpy as np\n'), ((3603, 3638), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3611, 3638), True, 'import numpy as np\n'), ((3725, 3754), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (3733, 3754), True, 'import numpy as np\n'), ((4353, 4446), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4367, 4446), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4467, 4502), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4482, 4502), False, 'import getpass\n'), ((4511, 4562), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4526, 4562), False, 'from ultralytics.utils import SETTINGS\n'), ((3661, 3695), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3675, 3695), True, 'import numpy as np\n'), ((3831, 3858), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (3839, 3858), True, 'import numpy as np\n'), ((2788, 2825), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (2796, 2825), True, 'import numpy as np\n'), ((3209, 3244), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3217, 3244), True, 'import numpy as np\n'), ((3013, 3047), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3021, 3047), True, 'import numpy as np\n'), ((2622, 2656), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2631, 2656), False, 'from ultralytics.data.augment import LetterBox\n'), ((3085, 3119), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3094, 3119), False, 'from ultralytics.data.augment import LetterBox\n'), ((3355, 3392), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3363, 3392), True, 'import numpy as np\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4054, 4087), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4068, 4087), True, 'import numpy as np\n'), ((4421, 4456), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4439, 4456), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4758, 4800), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4764, 4800), False, 'from openai import OpenAI\n'), ((798, 817), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (804, 817), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2746, 2761), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2756, 2761), False, 'import cv2\n'), ((2775, 2810), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2787, 2810), False, 'import cv2\n'), ((3729, 3757), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3737, 3757), True, 'import numpy as np\n'), ((3786, 3813), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3794, 3813), True, 'import numpy as np\n'), ((3825, 3858), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3839, 3858), True, 'import numpy as np\n'), ((3886, 3921), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3894, 3921), True, 'import numpy as np\n'), ((4008, 4037), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (4016, 4037), True, 'import numpy as np\n'), ((4535, 4628), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4549, 4628), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4649, 4684), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4664, 4684), False, 'import getpass\n'), ((4693, 4744), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4708, 4744), False, 'from ultralytics.utils import SETTINGS\n'), ((3944, 3978), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3958, 3978), True, 'import numpy as np\n'), ((4114, 4141), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (4122, 4141), True, 'import numpy as np\n'), ((3071, 3108), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3079, 3108), True, 'import numpy as np\n'), ((3492, 3527), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3500, 3527), True, 'import numpy as np\n'), ((3296, 3330), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3304, 3330), True, 'import numpy as np\n'), ((2905, 2939), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2914, 2939), False, 'from ultralytics.data.augment import LetterBox\n'), ((3368, 3402), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3377, 3402), False, 'from ultralytics.data.augment import LetterBox\n'), ((3638, 3675), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3646, 3675), True, 'import numpy as np\n')] |
from typing import Optional
from lancedb.pydantic import Vector
from pydantic import BaseModel, ConfigDict, Field, model_validator
class Wine(BaseModel):
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
extra="allow",
str_strip_whitespace=True,
json_schema_extra={
"example": {
"id": 45100,
"points": 85,
"title": "Balduzzi 2012 Reserva Merlot (Maule Valley)",
"description": "Ripe in color and aromas, this chunky wine delivers heavy baked-berry and raisin aromas in front of a jammy, extracted palate. Raisin and cooked berry flavors finish plump, with earthy notes.",
"price": 10.0,
"variety": "Merlot",
"winery": "Balduzzi",
"vineyard": "Reserva",
"country": "Chile",
"province": "Maule Valley",
"region_1": "null",
"region_2": "null",
"taster_name": "Michael Schachner",
"taster_twitter_handle": "@wineschach",
}
},
)
id: int
points: int
title: str
description: Optional[str]
price: Optional[float]
variety: Optional[str]
winery: Optional[str]
vineyard: Optional[str] = Field(..., alias="designation")
country: Optional[str]
province: Optional[str]
region_1: Optional[str]
region_2: Optional[str]
taster_name: Optional[str]
taster_twitter_handle: Optional[str]
@model_validator(mode="before")
def _fill_country_unknowns(cls, values):
"Fill in missing country values with 'Unknown', as we always want this field to be queryable"
country = values.get("country")
if not country:
values["country"] = "Unknown"
return values
@model_validator(mode="before")
def _add_to_vectorize_fields(cls, values):
"Add a field to_vectorize that will be used to create sentence embeddings"
variety = values.get("variety", "")
title = values.get("title", "")
description = values.get("description", "")
to_vectorize = list(filter(None, [variety, title, description]))
values["to_vectorize"] = " ".join(to_vectorize).strip()
return values
class LanceModelWine(BaseModel):
model_config = ConfigDict(
populate_by_name=True,
validate_assignment=True,
extra="allow",
str_strip_whitespace=True,
json_schema_extra={
"example": {
"id": 45100,
"points": 85,
"title": "Balduzzi 2012 Reserva Merlot (Maule Valley)",
"description": "Ripe in color and aromas, this chunky wine delivers heavy baked-berry and raisin aromas in front of a jammy, extracted palate. Raisin and cooked berry flavors finish plump, with earthy notes.",
"price": 10.0,
"variety": "Merlot",
"winery": "Balduzzi",
"vineyard": "Reserva",
"country": "Chile",
"province": "Maule Valley",
"region_1": "null",
"region_2": "null",
"taster_name": "Michael Schachner",
"taster_twitter_handle": "@wineschach",
}
},
)
id: int
points: int
title: str
description: Optional[str]
price: Optional[float]
variety: Optional[str]
winery: Optional[str]
vineyard: Optional[str] = Field(..., alias="designation")
country: Optional[str]
province: Optional[str]
region_1: Optional[str]
region_2: Optional[str]
taster_name: Optional[str]
taster_twitter_handle: Optional[str]
to_vectorize: str
vector: Vector(384)
| [
"lancedb.pydantic.Vector"
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from engine.data.augment import LetterBox
from engine.utils import LOGGER as logger
from engine.utils import SETTINGS
from engine.utils.checks import check_requirements
from engine.utils.ops import xyxy2xywh
from engine.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
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import json
import lancedb
import pytest
from lancedb.utils.events import _Events
@pytest.fixture(autouse=True)
def request_log_path(tmp_path):
return tmp_path / "request.json"
def mock_register_event(name: str, **kwargs):
if _Events._instance is None:
_Events._instance = _Events()
_Events._instance.enabled = True
_Events._instance.rate_limit = 0
_Events._instance(name, **kwargs)
def test_event_reporting(monkeypatch, request_log_path, tmp_path) -> None:
def mock_request(**kwargs):
json_data = kwargs.get("json", {})
with open(request_log_path, "w") as f:
json.dump(json_data, f)
monkeypatch.setattr(
lancedb.table, "register_event", mock_register_event
) # Force enable registering events and strip exception handling
monkeypatch.setattr(lancedb.utils.events, "threaded_request", mock_request)
db = lancedb.connect(tmp_path)
db.create_table(
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
mode="overwrite",
)
assert request_log_path.exists() # test if event was registered
with open(request_log_path, "r") as f:
json_data = json.load(f)
# TODO: don't hardcode these here. Instead create a module level json scehma in
# lancedb.utils.events for better evolvability
batch_keys = ["api_key", "distinct_id", "batch"]
event_keys = ["event", "properties", "timestamp", "distinct_id"]
property_keys = ["cli", "install", "platforms", "version", "session_id"]
assert all([key in json_data for key in batch_keys])
assert all([key in json_data["batch"][0] for key in event_keys])
assert all([key in json_data["batch"][0]["properties"] for key in property_keys])
# cleanup & reset
monkeypatch.undo()
_Events._instance = None
| [
"lancedb.connect",
"lancedb.utils.events._Events",
"lancedb.utils.events._Events._instance"
] | [((86, 114), 'pytest.fixture', 'pytest.fixture', ([], {'autouse': '(True)'}), '(autouse=True)\n', (100, 114), False, 'import pytest\n'), ((383, 416), 'lancedb.utils.events._Events._instance', '_Events._instance', (['name'], {}), '(name, **kwargs)\n', (400, 416), False, 'from lancedb.utils.events import _Events\n'), ((899, 924), 'lancedb.connect', 'lancedb.connect', (['tmp_path'], {}), '(tmp_path)\n', (914, 924), False, 'import lancedb\n'), ((294, 303), 'lancedb.utils.events._Events', '_Events', ([], {}), '()\n', (301, 303), False, 'from lancedb.utils.events import _Events\n'), ((1287, 1299), 'json.load', 'json.load', (['f'], {}), '(f)\n', (1296, 1299), False, 'import json\n'), ((628, 651), 'json.dump', 'json.dump', (['json_data', 'f'], {}), '(json_data, f)\n', (637, 651), False, 'import json\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4412, 4429), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4731, 4773), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4737, 4773), False, 'from openai import OpenAI\n'), ((798, 817), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (804, 817), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2746, 2761), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2756, 2761), False, 'import cv2\n'), ((2775, 2810), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2787, 2810), False, 'import cv2\n'), ((3729, 3757), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3737, 3757), True, 'import numpy as np\n'), ((3777, 3804), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3785, 3804), True, 'import numpy as np\n'), ((3816, 3849), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3830, 3849), True, 'import numpy as np\n'), ((3868, 3903), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3876, 3903), True, 'import numpy as np\n'), ((3981, 4010), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (3989, 4010), True, 'import numpy as np\n'), ((4508, 4601), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4522, 4601), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4622, 4657), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4637, 4657), False, 'import getpass\n'), ((4666, 4717), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4681, 4717), False, 'from ultralytics.utils import SETTINGS\n'), ((3926, 3960), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3940, 3960), True, 'import numpy as np\n'), ((4087, 4114), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (4095, 4114), True, 'import numpy as np\n'), ((3071, 3108), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3079, 3108), True, 'import numpy as np\n'), ((3492, 3527), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3500, 3527), True, 'import numpy as np\n'), ((3296, 3330), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3304, 3330), True, 'import numpy as np\n'), ((2905, 2939), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2914, 2939), False, 'from ultralytics.data.augment import LetterBox\n'), ((3368, 3402), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3377, 3402), False, 'from ultralytics.data.augment import LetterBox\n'), ((3638, 3675), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3646, 3675), True, 'import numpy as np\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4412, 4429), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4731, 4773), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4737, 4773), False, 'from openai import OpenAI\n'), ((798, 817), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (804, 817), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2746, 2761), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2756, 2761), False, 'import cv2\n'), ((2775, 2810), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2787, 2810), False, 'import cv2\n'), ((3729, 3757), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3737, 3757), True, 'import numpy as np\n'), ((3777, 3804), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3785, 3804), True, 'import numpy as np\n'), ((3816, 3849), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3830, 3849), True, 'import numpy as np\n'), ((3868, 3903), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3876, 3903), True, 'import numpy as np\n'), ((3981, 4010), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (3989, 4010), True, 'import numpy as np\n'), ((4508, 4601), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4522, 4601), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4622, 4657), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4637, 4657), False, 'import getpass\n'), ((4666, 4717), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4681, 4717), False, 'from ultralytics.utils import SETTINGS\n'), ((3926, 3960), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3940, 3960), True, 'import numpy as np\n'), ((4087, 4114), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (4095, 4114), True, 'import numpy as np\n'), ((3071, 3108), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3079, 3108), True, 'import numpy as np\n'), ((3492, 3527), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3500, 3527), True, 'import numpy as np\n'), ((3296, 3330), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3304, 3330), True, 'import numpy as np\n'), ((2905, 2939), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2914, 2939), False, 'from ultralytics.data.augment import LetterBox\n'), ((3368, 3402), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3377, 3402), False, 'from ultralytics.data.augment import LetterBox\n'), ((3638, 3675), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3646, 3675), True, 'import numpy as np\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if plot_masks else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if plot_kpts else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if plot_boxes else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4027, 4060), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4041, 4060), True, 'import numpy as np\n'), ((4394, 4429), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4412, 4429), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4731, 4773), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4737, 4773), False, 'from openai import OpenAI\n'), ((798, 817), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (804, 817), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2746, 2761), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2756, 2761), False, 'import cv2\n'), ((2775, 2810), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2787, 2810), False, 'import cv2\n'), ((3729, 3757), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3737, 3757), True, 'import numpy as np\n'), ((3777, 3804), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3785, 3804), True, 'import numpy as np\n'), ((3816, 3849), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3830, 3849), True, 'import numpy as np\n'), ((3868, 3903), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3876, 3903), True, 'import numpy as np\n'), ((3981, 4010), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (3989, 4010), True, 'import numpy as np\n'), ((4508, 4601), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4522, 4601), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4622, 4657), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4637, 4657), False, 'import getpass\n'), ((4666, 4717), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4681, 4717), False, 'from ultralytics.utils import SETTINGS\n'), ((3926, 3960), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3940, 3960), True, 'import numpy as np\n'), ((4087, 4114), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (4095, 4114), True, 'import numpy as np\n'), ((3071, 3108), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3079, 3108), True, 'import numpy as np\n'), ((3492, 3527), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3500, 3527), True, 'import numpy as np\n'), ((3296, 3330), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3304, 3330), True, 'import numpy as np\n'), ((2905, 2939), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2914, 2939), False, 'from ultralytics.data.augment import LetterBox\n'), ((3368, 3402), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3377, 3402), False, 'from ultralytics.data.augment import LetterBox\n'), ((3638, 3675), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3646, 3675), True, 'import numpy as np\n')] |
# Copyright (c) Hegel AI, Inc.
# All rights reserved.
#
# This source code's license can be found in the
# LICENSE file in the root directory of this source tree.
import itertools
import warnings
import pandas as pd
from typing import Callable, Optional
try:
import lancedb
from lancedb.embeddings import with_embeddings
except ImportError:
lancedb = None
import logging
from time import perf_counter
from .experiment import Experiment
from ._utils import _get_dynamic_columns
VALID_TASKS = [""]
def query_builder(
table: "lancedb.Table",
embed_fn: Callable,
text: str,
metric: str = "cosine",
limit: int = 3,
filter: str = None,
nprobes: int = None,
refine_factor: int = None,
):
if nprobes is not None or refine_factor is not None:
warnings.warn(
"`nprobes` and `refine_factor` are not used by the default `query_builder`. "
"Feel free to open an issue to request adding support for them."
)
query = table.search(embed_fn(text)[0]).metric(metric)
if filter:
query = query.where(filter)
return query.limit(limit).to_df()
class LanceDBExperiment(Experiment):
r"""
Perform an experiment with ``LanceDB`` to test different embedding functions or retrieval arguments.
You can query from an existing table, or create a new one (and insert documents into it) during
the experiment.
Args:
uri (str): LanceDB uri to interact with your database. Default is "lancedb"
table_name (str): the table that you will get or create. Default is "table"
use_existing_table (bool): determines whether to create a new collection or use
an existing one
embedding_fns (list[Callable]): embedding functions to test in the experiment
by default only uses the default one in LanceDB
query_args (dict[str, list]): parameters used to query the table
Each value is expected to be a list to create all possible combinations
data (Optional[list[dict]]): documents or embeddings that will be added to
the newly created table
text_col_name (str): name of the text column in the table. Default is "text"
clean_up (bool): determines whether to drop the table after the experiment ends
"""
def __init__(
self,
embedding_fns: dict[str, Callable],
query_args: dict[str, list],
uri: str = "lancedb",
table_name: str = "table",
use_existing_table: bool = False,
data: Optional[list[dict]] = None,
text_col_name: str = "text",
clean_up: bool = False,
):
if lancedb is None:
raise ModuleNotFoundError(
"Package `lancedb` is required to be installed to use this experiment."
"Please use `pip install lancedb` to install the package"
)
self.table_name = table_name
self.use_existing_table = use_existing_table
self.embedding_fns = embedding_fns
if use_existing_table and data:
raise RuntimeError("You can either use an existing collection or create a new one during the experiment.")
if not use_existing_table and data is None:
raise RuntimeError("If you choose to create a new collection, you must also add to it.")
self.data = data if data is not None else []
self.argument_combos: list[dict] = []
self.text_col_name = text_col_name
self.db = lancedb.connect(uri)
self.completion_fn = self.lancedb_completion_fn
self.query_args = query_args
self.clean_up = clean_up
super().__init__()
def prepare(self):
for combo in itertools.product(*self.query_args.values()):
self.argument_combos.append(dict(zip(self.query_args.keys(), combo)))
def run(self, runs: int = 1):
input_args = [] # This will be used to construct DataFrame table
results = []
latencies = []
if not self.argument_combos:
logging.info("Preparing first...")
self.prepare()
for emb_fn_name, emb_fn in self.embedding_fns.items():
if self.use_existing_table: # Use existing table
table = self.db.open_table(self.table_name)
if not table:
raise RuntimeError(f"Table {self.table_name} does not exist.")
else: # Create table and insert data
data = with_embeddings(emb_fn, self.data, self.text_col_name)
table = self.db.create_table(self.table_name, data, mode="overwrite")
# Query from table
for query_arg_dict in self.argument_combos:
query_args = query_arg_dict.copy()
for _ in range(runs):
start = perf_counter()
results.append(self.lancedb_completion_fn(table=table, embedding_fn=emb_fn, **query_args))
latencies.append(perf_counter() - start)
query_args["emb_fn"] = emb_fn_name # Saving for visualization
input_args.append(query_args)
# Clean up
if self.clean_up:
self.db.drop_table(self.table_name)
self._construct_result_dfs(input_args, results, latencies)
def lancedb_completion_fn(self, table, embedding_fn, **kwargs):
return query_builder(table, embedding_fn, **kwargs)
def _construct_result_dfs(
self,
input_args: list[dict[str, object]],
results: list[dict[str, object]],
latencies: list[float],
):
r"""
Construct a few DataFrames that contain all relevant data (i.e. input arguments, results, evaluation metrics).
This version only extract the most relevant objects returned by LanceDB.
Args:
input_args (list[dict[str, object]]): list of dictionaries, where each of them is a set of
input argument that was passed into the model
results (list[dict[str, object]]): list of responses from the model
latencies (list[float]): list of latency measurements
"""
# `input_arg_df` contains all all input args
input_arg_df = pd.DataFrame(input_args)
# `dynamic_input_arg_df` contains input args that has more than one unique values
dynamic_input_arg_df = _get_dynamic_columns(input_arg_df)
# `response_df` contains the extracted response (often being the text response)
response_dict = dict()
response_dict["top doc ids"] = [self._extract_top_doc_ids(result) for result in results]
response_dict["distances"] = [self._extract_lancedb_dists(result) for result in results]
response_dict["documents"] = [self._extract_lancedb_docs(result) for result in results]
response_df = pd.DataFrame(response_dict)
# `result_df` contains everything returned by the completion function
result_df = response_df # pd.concat([self.response_df, pd.DataFrame(results)], axis=1)
# `score_df` contains computed metrics (e.g. latency, evaluation metrics)
self.score_df = pd.DataFrame({"latency": latencies})
# `partial_df` contains some input arguments, extracted responses, and score
self.partial_df = pd.concat([dynamic_input_arg_df, response_df, self.score_df], axis=1)
# `full_df` contains all input arguments, responses, and score
self.full_df = pd.concat([input_arg_df, result_df, self.score_df], axis=1)
@staticmethod
def _extract_top_doc_ids(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["ids"]
@staticmethod
def _extract_lancedb_dists(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["_distance"]
@staticmethod
def _extract_lancedb_docs(output: pd.DataFrame) -> list[tuple[str, float]]:
r"""Helper function to get distances between documents from LanceDB."""
return output.to_dict(orient="list")["text"]
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((797, 961), 'warnings.warn', 'warnings.warn', (['"""`nprobes` and `refine_factor` are not used by the default `query_builder`. Feel free to open an issue to request adding support for them."""'], {}), "(\n '`nprobes` and `refine_factor` are not used by the default `query_builder`. Feel free to open an issue to request adding support for them.'\n )\n", (810, 961), False, 'import warnings\n'), ((3496, 3516), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (3511, 3516), False, 'import lancedb\n'), ((6251, 6275), 'pandas.DataFrame', 'pd.DataFrame', (['input_args'], {}), '(input_args)\n', (6263, 6275), True, 'import pandas as pd\n'), ((6864, 6891), 'pandas.DataFrame', 'pd.DataFrame', (['response_dict'], {}), '(response_dict)\n', (6876, 6891), True, 'import pandas as pd\n'), ((7173, 7209), 'pandas.DataFrame', 'pd.DataFrame', (["{'latency': latencies}"], {}), "({'latency': latencies})\n", (7185, 7209), True, 'import pandas as pd\n'), ((7322, 7391), 'pandas.concat', 'pd.concat', (['[dynamic_input_arg_df, response_df, self.score_df]'], {'axis': '(1)'}), '([dynamic_input_arg_df, response_df, self.score_df], axis=1)\n', (7331, 7391), True, 'import pandas as pd\n'), ((7486, 7545), 'pandas.concat', 'pd.concat', (['[input_arg_df, result_df, self.score_df]'], {'axis': '(1)'}), '([input_arg_df, result_df, self.score_df], axis=1)\n', (7495, 7545), True, 'import pandas as pd\n'), ((4045, 4079), 'logging.info', 'logging.info', (['"""Preparing first..."""'], {}), "('Preparing first...')\n", (4057, 4079), False, 'import logging\n'), ((4479, 4533), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['emb_fn', 'self.data', 'self.text_col_name'], {}), '(emb_fn, self.data, self.text_col_name)\n', (4494, 4533), False, 'from lancedb.embeddings import with_embeddings\n'), ((4825, 4839), 'time.perf_counter', 'perf_counter', ([], {}), '()\n', (4837, 4839), False, 'from time import perf_counter\n'), ((4988, 5002), 'time.perf_counter', 'perf_counter', ([], {}), '()\n', (5000, 5002), False, 'from time import perf_counter\n')] |
"""LanceDB vector store with cloud storage support."""
import os
from typing import Any, Optional
from dotenv import load_dotenv
from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores import LanceDBVectorStore as LanceDBVectorStoreBase
from llama_index.vector_stores.lancedb import _to_lance_filter, _to_llama_similarities
from llama_index.vector_stores.types import VectorStoreQuery, VectorStoreQueryResult
from pandas import DataFrame
load_dotenv()
class LanceDBVectorStore(LanceDBVectorStoreBase):
"""Advanced LanceDB Vector Store supporting cloud storage and prefiltering."""
from lancedb.query import LanceQueryBuilder
from lancedb.table import Table
def __init__(
self,
uri: str,
table_name: str = "vectors",
nprobes: int = 20,
refine_factor: Optional[int] = None,
api_key: Optional[str] = None,
region: Optional[str] = None,
**kwargs: Any,
) -> None:
"""Init params."""
self._setup_connection(uri, api_key, region)
self.uri = uri
self.table_name = table_name
self.nprobes = nprobes
self.refine_factor = refine_factor
self.api_key = api_key
self.region = region
def _setup_connection(self, uri: str, api_key: Optional[str] = None, region: Optional[str] = None):
"""Establishes a robust connection to LanceDB."""
api_key = api_key or os.getenv('LANCEDB_API_KEY')
region = region or os.getenv('LANCEDB_REGION')
import_err_msg = "`lancedb` package not found, please run `pip install lancedb`"
try:
import lancedb
except ImportError:
raise ImportError(import_err_msg)
if api_key and region:
self.connection = lancedb.connect(uri, api_key=api_key, region=region)
else:
self.connection = lancedb.connect(uri)
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""Enhanced query method to support prefiltering in LanceDB queries."""
table = self.connection.open_table(self.table_name)
lance_query = self._prepare_lance_query(query, table, **kwargs)
results = lance_query.to_df()
return self._construct_query_result(results)
def _prepare_lance_query(self, query: VectorStoreQuery, table: Table, **kwargs) -> LanceQueryBuilder:
"""Prepares the LanceDB query considering prefiltering and additional parameters."""
if query.filters is not None:
if "where" in kwargs:
raise ValueError(
"Cannot specify filter via both query and kwargs. "
"Use kwargs only for lancedb specific items that are "
"not supported via the generic query interface.")
where = _to_lance_filter(query.filters)
else:
where = kwargs.pop("where", None)
prefilter = kwargs.pop("prefilter", False)
table = self.connection.open_table(self.table_name)
lance_query = (
table.search(query.query_embedding).limit(query.similarity_top_k).where(
where, prefilter=prefilter).nprobes(self.nprobes))
if self.refine_factor is not None:
lance_query.refine_factor(self.refine_factor)
return lance_query
def _construct_query_result(self, results: DataFrame) -> VectorStoreQueryResult:
"""Constructs a VectorStoreQueryResult from a LanceDB query result."""
nodes = []
for _, row in results.iterrows():
node = TextNode(
text=row.get('text', ''), # ensure text is a string
id_=row['id'],
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(node_id=row['doc_id']),
})
nodes.append(node)
return VectorStoreQueryResult(
nodes=nodes,
similarities=_to_llama_similarities(results),
ids=results["id"].tolist(),
)
| [
"lancedb.connect"
] | [((490, 503), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (501, 503), False, 'from dotenv import load_dotenv\n'), ((1464, 1492), 'os.getenv', 'os.getenv', (['"""LANCEDB_API_KEY"""'], {}), "('LANCEDB_API_KEY')\n", (1473, 1492), False, 'import os\n'), ((1520, 1547), 'os.getenv', 'os.getenv', (['"""LANCEDB_REGION"""'], {}), "('LANCEDB_REGION')\n", (1529, 1547), False, 'import os\n'), ((1814, 1866), 'lancedb.connect', 'lancedb.connect', (['uri'], {'api_key': 'api_key', 'region': 'region'}), '(uri, api_key=api_key, region=region)\n', (1829, 1866), False, 'import lancedb\n'), ((1911, 1931), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (1926, 1931), False, 'import lancedb\n'), ((2898, 2929), 'llama_index.vector_stores.lancedb._to_lance_filter', '_to_lance_filter', (['query.filters'], {}), '(query.filters)\n', (2914, 2929), False, 'from llama_index.vector_stores.lancedb import _to_lance_filter, _to_llama_similarities\n'), ((4021, 4052), 'llama_index.vector_stores.lancedb._to_llama_similarities', '_to_llama_similarities', (['results'], {}), '(results)\n', (4043, 4052), False, 'from llama_index.vector_stores.lancedb import _to_lance_filter, _to_llama_similarities\n'), ((3841, 3879), 'llama_index.schema.RelatedNodeInfo', 'RelatedNodeInfo', ([], {'node_id': "row['doc_id']"}), "(node_id=row['doc_id'])\n", (3856, 3879), False, 'from llama_index.schema import NodeRelationship, RelatedNodeInfo, TextNode\n')] |
from pathlib import Path
from typing import Any, Callable
from lancedb import DBConnection as LanceDBConnection
from lancedb import connect as lancedb_connect
from lancedb.table import Table as LanceDBTable
from openai import Client as OpenAIClient
from pydantic import Field, PrivateAttr
from crewai_tools.tools.rag.rag_tool import Adapter
def _default_embedding_function():
client = OpenAIClient()
def _embedding_function(input):
rs = client.embeddings.create(input=input, model="text-embedding-ada-002")
return [record.embedding for record in rs.data]
return _embedding_function
class LanceDBAdapter(Adapter):
uri: str | Path
table_name: str
embedding_function: Callable = Field(default_factory=_default_embedding_function)
top_k: int = 3
vector_column_name: str = "vector"
text_column_name: str = "text"
_db: LanceDBConnection = PrivateAttr()
_table: LanceDBTable = PrivateAttr()
def model_post_init(self, __context: Any) -> None:
self._db = lancedb_connect(self.uri)
self._table = self._db.open_table(self.table_name)
return super().model_post_init(__context)
def query(self, question: str) -> str:
query = self.embedding_function([question])[0]
results = (
self._table.search(query, vector_column_name=self.vector_column_name)
.limit(self.top_k)
.select([self.text_column_name])
.to_list()
)
values = [result[self.text_column_name] for result in results]
return "\n".join(values)
| [
"lancedb.connect"
] | [((393, 407), 'openai.Client', 'OpenAIClient', ([], {}), '()\n', (405, 407), True, 'from openai import Client as OpenAIClient\n'), ((724, 774), 'pydantic.Field', 'Field', ([], {'default_factory': '_default_embedding_function'}), '(default_factory=_default_embedding_function)\n', (729, 774), False, 'from pydantic import Field, PrivateAttr\n'), ((898, 911), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (909, 911), False, 'from pydantic import Field, PrivateAttr\n'), ((939, 952), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (950, 952), False, 'from pydantic import Field, PrivateAttr\n'), ((1028, 1053), 'lancedb.connect', 'lancedb_connect', (['self.uri'], {}), '(self.uri)\n', (1043, 1053), True, 'from lancedb import connect as lancedb_connect\n')] |
from langchain.text_splitter import (
RecursiveCharacterTextSplitter,
Language,
LatexTextSplitter,
)
from langchain.document_loaders import TextLoader
from langchain.embeddings import OpenAIEmbeddings
import argparse, os, arxiv
os.environ["OPENAI_API_KEY"] = "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"
embeddings = OpenAIEmbeddings()
from langchain.vectorstores import LanceDB
from lancedb.pydantic import Vector, LanceModel
from Typing import List
from datetime import datetime
import lancedb
global embedding_out_length
embedding_out_length = 1536
class Content(LanceModel):
id: str
arxiv_id: str
vector: Vector(embedding_out_length)
text: str
uploaded_date: datetime
title: str
authors: List[str]
abstract: str
categories: List[str]
url: str
def PyPDF_to_Vector(table: LanceDB, embeddings: OpenAIEmbeddings, src_dir: str, n_threads: int = 1):
pass
if __name__ == "__main__":
argparser = argparse.ArgumentParser(description="Create Vector DB and perform ingestion from source files")
argparser.add_argument('-s', '--src_dir', type=str, required=True, help = "Source directory where arxiv sources are stored")
argparser.add_argument('-db', '--db_name', type=str, required=True, help = "Name of the LanceDB database to be created")
argparser.add_argument('-t', '--table_name', type=str, required=False, help = "Name of the LanceDB table to be created", default = "EIC_archive")
argparser.add_argument('-openai_key', '--openai_api_key', type=str, required=True, help = "OpenAI API key")
argparser.add_argument('-c', '--chunking', type = str, required=False, help = "Type of Chunking PDF or LATEX", default = "PDF")
argparser.add_argument('-n', '--nthreads', type=int, default=-1)
args = argparser.parse_args()
SRC_DIR = args.src_dir
DB_NAME = args.db_name
TABLE_NAME = args.table_name
OPENAI_API_KEY = args.openai_api_key
NTHREADS = args.nthreads
db = lancedb.connect(DB_NAME)
table = db.create_table(TABLE_NAME, schema=Content, mode="overwrite")
db = lancedb.connect()
meta_data = {"arxiv_id": "1", "title": "EIC LLM",
"category" : "N/A",
"authors": "N/A",
"sub_categories": "N/A",
"abstract": "N/A",
"published": "N/A",
"updated": "N/A",
"doi": "N/A"
},
table = db.create_table(
"EIC_archive",
data=[
{
"vector": embeddings.embed_query("EIC LLM"),
"text": "EIC LLM",
"id": "1",
"arxiv_id" : "N/A",
"title" : "N/A",
"category" : "N/A",
"published" : "N/A"
}
],
mode="overwrite",
)
vectorstore = LanceDB(connection = table, embedding = embeddings)
sourcedir = "PDFs"
count = 0
for source in os.listdir(sourcedir):
if not os.path.isdir(os.path.join("PDFs", source)):
continue
print (f"Adding the source document {source} to the Vector DB")
import arxiv
client = arxiv.Client()
search = arxiv.Search(id_list=[source])
paper = next(arxiv.Client().results(search))
meta_data = {"arxiv_id": paper.entry_id,
"title": paper.title,
"category" : categories[paper.primary_category],
"published": paper.published
}
for file in os.listdir(os.path.join(sourcedir, source)):
if file.endswith(".tex"):
latex_file = os.path.join(sourcedir, source, file)
print (source, latex_file)
documents = TextLoader(latex_file, encoding = 'latin-1').load()
latex_splitter = LatexTextSplitter(
chunk_size=120, chunk_overlap=10
)
documents = latex_splitter.split_documents(documents)
for doc in documents:
for k, v in meta_data.items():
doc.metadata[k] = v
vectorstore.add_documents(documents = documents)
count+=len(documents) | [
"lancedb.connect",
"lancedb.pydantic.Vector"
] | [((342, 360), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (358, 360), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((2116, 2133), 'lancedb.connect', 'lancedb.connect', ([], {}), '()\n', (2131, 2133), False, 'import lancedb\n'), ((2820, 2867), 'langchain.vectorstores.LanceDB', 'LanceDB', ([], {'connection': 'table', 'embedding': 'embeddings'}), '(connection=table, embedding=embeddings)\n', (2827, 2867), False, 'from langchain.vectorstores import LanceDB\n'), ((2916, 2937), 'os.listdir', 'os.listdir', (['sourcedir'], {}), '(sourcedir)\n', (2926, 2937), False, 'import argparse, os, arxiv\n'), ((648, 676), 'lancedb.pydantic.Vector', 'Vector', (['embedding_out_length'], {}), '(embedding_out_length)\n', (654, 676), False, 'from lancedb.pydantic import Vector, LanceModel\n'), ((978, 1078), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Create Vector DB and perform ingestion from source files"""'}), "(description=\n 'Create Vector DB and perform ingestion from source files')\n", (1001, 1078), False, 'import argparse, os, arxiv\n'), ((2006, 2030), 'lancedb.connect', 'lancedb.connect', (['DB_NAME'], {}), '(DB_NAME)\n', (2021, 2030), False, 'import lancedb\n'), ((3110, 3124), 'arxiv.Client', 'arxiv.Client', ([], {}), '()\n', (3122, 3124), False, 'import arxiv\n'), ((3138, 3168), 'arxiv.Search', 'arxiv.Search', ([], {'id_list': '[source]'}), '(id_list=[source])\n', (3150, 3168), False, 'import arxiv\n'), ((3458, 3489), 'os.path.join', 'os.path.join', (['sourcedir', 'source'], {}), '(sourcedir, source)\n', (3470, 3489), False, 'import argparse, os, arxiv\n'), ((2964, 2992), 'os.path.join', 'os.path.join', (['"""PDFs"""', 'source'], {}), "('PDFs', source)\n", (2976, 2992), False, 'import argparse, os, arxiv\n'), ((3551, 3588), 'os.path.join', 'os.path.join', (['sourcedir', 'source', 'file'], {}), '(sourcedir, source, file)\n', (3563, 3588), False, 'import argparse, os, arxiv\n'), ((3733, 3784), 'langchain.text_splitter.LatexTextSplitter', 'LatexTextSplitter', ([], {'chunk_size': '(120)', 'chunk_overlap': '(10)'}), '(chunk_size=120, chunk_overlap=10)\n', (3750, 3784), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter, Language, LatexTextSplitter\n'), ((3186, 3200), 'arxiv.Client', 'arxiv.Client', ([], {}), '()\n', (3198, 3200), False, 'import arxiv\n'), ((3652, 3694), 'langchain.document_loaders.TextLoader', 'TextLoader', (['latex_file'], {'encoding': '"""latin-1"""'}), "(latex_file, encoding='latin-1')\n", (3662, 3694), False, 'from langchain.document_loaders import TextLoader\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
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# Ultralytics YOLO 🚀, AGPL-3.0 license
import getpass
from typing import List
import cv2
import numpy as np
import pandas as pd
from ultralytics.data.augment import LetterBox
from ultralytics.utils import LOGGER as logger
from ultralytics.utils import SETTINGS
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.ops import xyxy2xywh
from ultralytics.utils.plotting import plot_images
def get_table_schema(vector_size):
"""Extracts and returns the schema of a database table."""
from lancedb.pydantic import LanceModel, Vector
class Schema(LanceModel):
im_file: str
labels: List[str]
cls: List[int]
bboxes: List[List[float]]
masks: List[List[List[int]]]
keypoints: List[List[List[float]]]
vector: Vector(vector_size)
return Schema
def get_sim_index_schema():
"""Returns a LanceModel schema for a database table with specified vector size."""
from lancedb.pydantic import LanceModel
class Schema(LanceModel):
idx: int
im_file: str
count: int
sim_im_files: List[str]
return Schema
def sanitize_batch(batch, dataset_info):
"""Sanitizes input batch for inference, ensuring correct format and dimensions."""
batch["cls"] = batch["cls"].flatten().int().tolist()
box_cls_pair = sorted(zip(batch["bboxes"].tolist(), batch["cls"]), key=lambda x: x[1])
batch["bboxes"] = [box for box, _ in box_cls_pair]
batch["cls"] = [cls for _, cls in box_cls_pair]
batch["labels"] = [dataset_info["names"][i] for i in batch["cls"]]
batch["masks"] = batch["masks"].tolist() if "masks" in batch else [[[]]]
batch["keypoints"] = batch["keypoints"].tolist() if "keypoints" in batch else [[[]]]
return batch
def plot_query_result(similar_set, plot_labels=True):
"""
Plot images from the similar set.
Args:
similar_set (list): Pyarrow or pandas object containing the similar data points
plot_labels (bool): Whether to plot labels or not
"""
similar_set = (
similar_set.to_dict(orient="list") if isinstance(similar_set, pd.DataFrame) else similar_set.to_pydict()
)
empty_masks = [[[]]]
empty_boxes = [[]]
images = similar_set.get("im_file", [])
bboxes = similar_set.get("bboxes", []) if similar_set.get("bboxes") is not empty_boxes else []
masks = similar_set.get("masks") if similar_set.get("masks")[0] != empty_masks else []
kpts = similar_set.get("keypoints") if similar_set.get("keypoints")[0] != empty_masks else []
cls = similar_set.get("cls", [])
plot_size = 640
imgs, batch_idx, plot_boxes, plot_masks, plot_kpts = [], [], [], [], []
for i, imf in enumerate(images):
im = cv2.imread(imf)
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
h, w = im.shape[:2]
r = min(plot_size / h, plot_size / w)
imgs.append(LetterBox(plot_size, center=False)(image=im).transpose(2, 0, 1))
if plot_labels:
if len(bboxes) > i and len(bboxes[i]) > 0:
box = np.array(bboxes[i], dtype=np.float32)
box[:, [0, 2]] *= r
box[:, [1, 3]] *= r
plot_boxes.append(box)
if len(masks) > i and len(masks[i]) > 0:
mask = np.array(masks[i], dtype=np.uint8)[0]
plot_masks.append(LetterBox(plot_size, center=False)(image=mask))
if len(kpts) > i and kpts[i] is not None:
kpt = np.array(kpts[i], dtype=np.float32)
kpt[:, :, :2] *= r
plot_kpts.append(kpt)
batch_idx.append(np.ones(len(np.array(bboxes[i], dtype=np.float32))) * i)
imgs = np.stack(imgs, axis=0)
masks = np.stack(plot_masks, axis=0) if len(plot_masks) > 0 else np.zeros(0, dtype=np.uint8)
kpts = np.concatenate(plot_kpts, axis=0) if len(plot_kpts) > 0 else np.zeros((0, 51), dtype=np.float32)
boxes = xyxy2xywh(np.concatenate(plot_boxes, axis=0)) if len(plot_boxes) > 0 else np.zeros(0, dtype=np.float32)
batch_idx = np.concatenate(batch_idx, axis=0)
cls = np.concatenate([np.array(c, dtype=np.int32) for c in cls], axis=0)
return plot_images(
imgs, batch_idx, cls, bboxes=boxes, masks=masks, kpts=kpts, max_subplots=len(images), save=False, threaded=False
)
def prompt_sql_query(query):
"""Plots images with optional labels from a similar data set."""
check_requirements("openai>=1.6.1")
from openai import OpenAI
if not SETTINGS["openai_api_key"]:
logger.warning("OpenAI API key not found in settings. Please enter your API key below.")
openai_api_key = getpass.getpass("OpenAI API key: ")
SETTINGS.update({"openai_api_key": openai_api_key})
openai = OpenAI(api_key=SETTINGS["openai_api_key"])
messages = [
{
"role": "system",
"content": """
You are a helpful data scientist proficient in SQL. You need to output exactly one SQL query based on
the following schema and a user request. You only need to output the format with fixed selection
statement that selects everything from "'table'", like `SELECT * from 'table'`
Schema:
im_file: string not null
labels: list<item: string> not null
child 0, item: string
cls: list<item: int64> not null
child 0, item: int64
bboxes: list<item: list<item: double>> not null
child 0, item: list<item: double>
child 0, item: double
masks: list<item: list<item: list<item: int64>>> not null
child 0, item: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
keypoints: list<item: list<item: list<item: double>>> not null
child 0, item: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
vector: fixed_size_list<item: float>[256] not null
child 0, item: float
Some details about the schema:
- the "labels" column contains the string values like 'person' and 'dog' for the respective objects
in each image
- the "cls" column contains the integer values on these classes that map them the labels
Example of a correct query:
request - Get all data points that contain 2 or more people and at least one dog
correct query-
SELECT * FROM 'table' WHERE ARRAY_LENGTH(cls) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'person')) >= 2 AND ARRAY_LENGTH(FILTER(labels, x -> x = 'dog')) >= 1;
""",
},
{"role": "user", "content": f"{query}"},
]
response = openai.chat.completions.create(model="gpt-3.5-turbo", messages=messages)
return response.choices[0].message.content
| [
"lancedb.pydantic.Vector"
] | [((3694, 3716), 'numpy.stack', 'np.stack', (['imgs'], {'axis': '(0)'}), '(imgs, axis=0)\n', (3702, 3716), True, 'import numpy as np\n'), ((4054, 4087), 'numpy.concatenate', 'np.concatenate', (['batch_idx'], {'axis': '(0)'}), '(batch_idx, axis=0)\n', (4068, 4087), True, 'import numpy as np\n'), ((4421, 4456), 'ultralytics.utils.checks.check_requirements', 'check_requirements', (['"""openai>=1.6.1"""'], {}), "('openai>=1.6.1')\n", (4439, 4456), False, 'from ultralytics.utils.checks import check_requirements\n'), ((4758, 4800), 'openai.OpenAI', 'OpenAI', ([], {'api_key': "SETTINGS['openai_api_key']"}), "(api_key=SETTINGS['openai_api_key'])\n", (4764, 4800), False, 'from openai import OpenAI\n'), ((798, 817), 'lancedb.pydantic.Vector', 'Vector', (['vector_size'], {}), '(vector_size)\n', (804, 817), False, 'from lancedb.pydantic import LanceModel, Vector\n'), ((2746, 2761), 'cv2.imread', 'cv2.imread', (['imf'], {}), '(imf)\n', (2756, 2761), False, 'import cv2\n'), ((2775, 2810), 'cv2.cvtColor', 'cv2.cvtColor', (['im', 'cv2.COLOR_BGR2RGB'], {}), '(im, cv2.COLOR_BGR2RGB)\n', (2787, 2810), False, 'import cv2\n'), ((3729, 3757), 'numpy.stack', 'np.stack', (['plot_masks'], {'axis': '(0)'}), '(plot_masks, axis=0)\n', (3737, 3757), True, 'import numpy as np\n'), ((3786, 3813), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.uint8'}), '(0, dtype=np.uint8)\n', (3794, 3813), True, 'import numpy as np\n'), ((3825, 3858), 'numpy.concatenate', 'np.concatenate', (['plot_kpts'], {'axis': '(0)'}), '(plot_kpts, axis=0)\n', (3839, 3858), True, 'import numpy as np\n'), ((3886, 3921), 'numpy.zeros', 'np.zeros', (['(0, 51)'], {'dtype': 'np.float32'}), '((0, 51), dtype=np.float32)\n', (3894, 3921), True, 'import numpy as np\n'), ((4008, 4037), 'numpy.zeros', 'np.zeros', (['(0)'], {'dtype': 'np.float32'}), '(0, dtype=np.float32)\n', (4016, 4037), True, 'import numpy as np\n'), ((4535, 4628), 'ultralytics.utils.LOGGER.warning', 'logger.warning', (['"""OpenAI API key not found in settings. Please enter your API key below."""'], {}), "(\n 'OpenAI API key not found in settings. Please enter your API key below.')\n", (4549, 4628), True, 'from ultralytics.utils import LOGGER as logger\n'), ((4649, 4684), 'getpass.getpass', 'getpass.getpass', (['"""OpenAI API key: """'], {}), "('OpenAI API key: ')\n", (4664, 4684), False, 'import getpass\n'), ((4693, 4744), 'ultralytics.utils.SETTINGS.update', 'SETTINGS.update', (["{'openai_api_key': openai_api_key}"], {}), "({'openai_api_key': openai_api_key})\n", (4708, 4744), False, 'from ultralytics.utils import SETTINGS\n'), ((3944, 3978), 'numpy.concatenate', 'np.concatenate', (['plot_boxes'], {'axis': '(0)'}), '(plot_boxes, axis=0)\n', (3958, 3978), True, 'import numpy as np\n'), ((4114, 4141), 'numpy.array', 'np.array', (['c'], {'dtype': 'np.int32'}), '(c, dtype=np.int32)\n', (4122, 4141), True, 'import numpy as np\n'), ((3071, 3108), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3079, 3108), True, 'import numpy as np\n'), ((3492, 3527), 'numpy.array', 'np.array', (['kpts[i]'], {'dtype': 'np.float32'}), '(kpts[i], dtype=np.float32)\n', (3500, 3527), True, 'import numpy as np\n'), ((3296, 3330), 'numpy.array', 'np.array', (['masks[i]'], {'dtype': 'np.uint8'}), '(masks[i], dtype=np.uint8)\n', (3304, 3330), True, 'import numpy as np\n'), ((2905, 2939), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (2914, 2939), False, 'from ultralytics.data.augment import LetterBox\n'), ((3368, 3402), 'ultralytics.data.augment.LetterBox', 'LetterBox', (['plot_size'], {'center': '(False)'}), '(plot_size, center=False)\n', (3377, 3402), False, 'from ultralytics.data.augment import LetterBox\n'), ((3638, 3675), 'numpy.array', 'np.array', (['bboxes[i]'], {'dtype': 'np.float32'}), '(bboxes[i], dtype=np.float32)\n', (3646, 3675), True, 'import numpy as np\n')] |
import os
import argparse
import lancedb
from lancedb.context import contextualize
from lancedb.embeddings import with_embeddings
from datasets import load_dataset
import openai
import pytest
import subprocess
from main import embed_func, create_prompt, complete
# DOWNLOAD ==============================================================
subprocess.Popen(
"wget -c https://eto-public.s3.us-west-2.amazonaws.com/datasets/youtube_transcript/youtube-transcriptions_sample.jsonl",
shell=True,
).wait()
# Testing ===========================================================
@pytest.fixture
def mock_embed_func(monkeypatch):
def mock_api_call(*args, **kwargs):
return {"data": [{"embedding": [0.5]} for _ in range(10)]}
monkeypatch.setattr(openai.Embedding, "create", mock_api_call)
@pytest.fixture
def mock_complete(monkeypatch):
def mock_api_call(*args, **kwargs):
return {"choices": [{"text": "test"}]}
monkeypatch.setattr(openai.Completion, "create", mock_api_call)
def test_main(mock_embed_func, mock_complete):
args = argparse.Namespace(
query="test",
context_length=3,
window_size=20,
stride=4,
openai_key="test",
model="test",
)
db = lancedb.connect("~/tmp/lancedb")
table_name = "youtube-chatbot"
if table_name not in db.table_names():
data = load_dataset("jamescalam/youtube-transcriptions", split="train")
df = (
contextualize(data.to_pandas())
.groupby("title")
.text_col("text")
.window(args.window_size)
.stride(args.stride)
.to_df()
)
df = df.iloc[:10].reset_index(drop=True)
print(df.shape)
data = with_embeddings(embed_func, df, show_progress=True)
data.to_pandas().head(1)
tbl = db.create_table(table_name, data)
print(f"Created LaneDB table of length: {len(tbl)}")
else:
tbl = db.open_table(table_name)
load_dataset("jamescalam/youtube-transcriptions", split="train")
emb = embed_func(args.query)[0]
context = tbl.search(emb).limit(args.context_length).to_df()
prompt = create_prompt(args.query, context)
complete(prompt)
top_match = context.iloc[0]
print(f"Top Match: {top_match['url']}&t={top_match['start']}")
| [
"lancedb.connect",
"lancedb.embeddings.with_embeddings"
] | [((1071, 1184), 'argparse.Namespace', 'argparse.Namespace', ([], {'query': '"""test"""', 'context_length': '(3)', 'window_size': '(20)', 'stride': '(4)', 'openai_key': '"""test"""', 'model': '"""test"""'}), "(query='test', context_length=3, window_size=20, stride=4,\n openai_key='test', model='test')\n", (1089, 1184), False, 'import argparse\n'), ((1246, 1278), 'lancedb.connect', 'lancedb.connect', (['"""~/tmp/lancedb"""'], {}), "('~/tmp/lancedb')\n", (1261, 1278), False, 'import lancedb\n'), ((1995, 2059), 'datasets.load_dataset', 'load_dataset', (['"""jamescalam/youtube-transcriptions"""'], {'split': '"""train"""'}), "('jamescalam/youtube-transcriptions', split='train')\n", (2007, 2059), False, 'from datasets import load_dataset\n'), ((2174, 2208), 'main.create_prompt', 'create_prompt', (['args.query', 'context'], {}), '(args.query, context)\n', (2187, 2208), False, 'from main import embed_func, create_prompt, complete\n'), ((2213, 2229), 'main.complete', 'complete', (['prompt'], {}), '(prompt)\n', (2221, 2229), False, 'from main import embed_func, create_prompt, complete\n'), ((339, 498), 'subprocess.Popen', 'subprocess.Popen', (['"""wget -c https://eto-public.s3.us-west-2.amazonaws.com/datasets/youtube_transcript/youtube-transcriptions_sample.jsonl"""'], {'shell': '(True)'}), "(\n 'wget -c https://eto-public.s3.us-west-2.amazonaws.com/datasets/youtube_transcript/youtube-transcriptions_sample.jsonl'\n , shell=True)\n", (355, 498), False, 'import subprocess\n'), ((1372, 1436), 'datasets.load_dataset', 'load_dataset', (['"""jamescalam/youtube-transcriptions"""'], {'split': '"""train"""'}), "('jamescalam/youtube-transcriptions', split='train')\n", (1384, 1436), False, 'from datasets import load_dataset\n'), ((1746, 1797), 'lancedb.embeddings.with_embeddings', 'with_embeddings', (['embed_func', 'df'], {'show_progress': '(True)'}), '(embed_func, df, show_progress=True)\n', (1761, 1797), False, 'from lancedb.embeddings import with_embeddings\n'), ((2070, 2092), 'main.embed_func', 'embed_func', (['args.query'], {}), '(args.query)\n', (2080, 2092), False, 'from main import embed_func, create_prompt, complete\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
from copy import copy
from datetime import date, datetime, timedelta
from pathlib import Path
from time import sleep
from typing import List
from unittest.mock import PropertyMock, patch
import lance
import lancedb
import numpy as np
import pandas as pd
import polars as pl
import pyarrow as pa
import pytest
import pytest_asyncio
from lancedb.conftest import MockTextEmbeddingFunction
from lancedb.db import AsyncConnection, LanceDBConnection
from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector
from lancedb.table import LanceTable
from pydantic import BaseModel
class MockDB:
def __init__(self, uri: Path):
self.uri = uri
self.read_consistency_interval = None
@functools.cached_property
def is_managed_remote(self) -> bool:
return False
@pytest.fixture
def db(tmp_path) -> MockDB:
return MockDB(tmp_path)
@pytest_asyncio.fixture
async def db_async(tmp_path) -> AsyncConnection:
return await lancedb.connect_async(
tmp_path, read_consistency_interval=timedelta(seconds=0)
)
def test_basic(db):
ds = LanceTable.create(
db,
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
).to_lance()
table = LanceTable(db, "test")
assert table.name == "test"
assert table.schema == ds.schema
assert table.to_lance().to_table() == ds.to_table()
@pytest.mark.asyncio
async def test_close(db_async: AsyncConnection):
table = await db_async.create_table("some_table", data=[{"id": 0}])
assert table.is_open()
table.close()
assert not table.is_open()
with pytest.raises(Exception, match="Table some_table is closed"):
await table.count_rows()
assert str(table) == "ClosedTable(some_table)"
@pytest.mark.asyncio
async def test_update_async(db_async: AsyncConnection):
table = await db_async.create_table("some_table", data=[{"id": 0}])
assert await table.count_rows("id == 0") == 1
assert await table.count_rows("id == 7") == 0
await table.update({"id": 7})
assert await table.count_rows("id == 7") == 1
assert await table.count_rows("id == 0") == 0
await table.add([{"id": 2}])
await table.update(where="id % 2 == 0", updates_sql={"id": "5"})
assert await table.count_rows("id == 7") == 1
assert await table.count_rows("id == 2") == 0
assert await table.count_rows("id == 5") == 1
await table.update({"id": 10}, where="id == 5")
assert await table.count_rows("id == 10") == 1
def test_create_table(db):
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 2)),
pa.field("item", pa.string()),
pa.field("price", pa.float32()),
]
)
expected = pa.Table.from_arrays(
[
pa.FixedSizeListArray.from_arrays(pa.array([3.1, 4.1, 5.9, 26.5]), 2),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
schema=schema,
)
data = [
[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
]
df = pd.DataFrame(data[0])
data.append(df)
data.append(pa.Table.from_pandas(df, schema=schema))
for i, d in enumerate(data):
tbl = (
LanceTable.create(db, f"test_{i}", data=d, schema=schema)
.to_lance()
.to_table()
)
assert expected == tbl
def test_empty_table(db):
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 2)),
pa.field("item", pa.string()),
pa.field("price", pa.float32()),
]
)
tbl = LanceTable.create(db, "test", schema=schema)
data = [
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
]
tbl.add(data=data)
def test_add(db):
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 2)),
pa.field("item", pa.string()),
pa.field("price", pa.float64()),
]
)
table = LanceTable.create(
db,
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
)
_add(table, schema)
table = LanceTable.create(db, "test2", schema=schema)
table.add(
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
)
_add(table, schema)
def test_add_pydantic_model(db):
# https://github.com/lancedb/lancedb/issues/562
class Metadata(BaseModel):
source: str
timestamp: datetime
class Document(BaseModel):
content: str
meta: Metadata
class LanceSchema(LanceModel):
id: str
vector: Vector(2)
li: List[int]
payload: Document
tbl = LanceTable.create(db, "mytable", schema=LanceSchema, mode="overwrite")
assert tbl.schema == LanceSchema.to_arrow_schema()
# add works
expected = LanceSchema(
id="id",
vector=[0.0, 0.0],
li=[1, 2, 3],
payload=Document(
content="foo", meta=Metadata(source="bar", timestamp=datetime.now())
),
)
tbl.add([expected])
result = tbl.search([0.0, 0.0]).limit(1).to_pydantic(LanceSchema)[0]
assert result == expected
flattened = tbl.search([0.0, 0.0]).limit(1).to_pandas(flatten=1)
assert len(flattened.columns) == 6 # _distance is automatically added
really_flattened = tbl.search([0.0, 0.0]).limit(1).to_pandas(flatten=True)
assert len(really_flattened.columns) == 7
@pytest.mark.asyncio
async def test_add_async(db_async: AsyncConnection):
table = await db_async.create_table(
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
)
assert await table.count_rows() == 2
await table.add(
data=[
{"vector": [10.0, 11.0], "item": "baz", "price": 30.0},
],
)
table = await db_async.open_table("test")
assert await table.count_rows() == 3
def test_polars(db):
data = {
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
# Ingest polars dataframe
table = LanceTable.create(db, "test", data=pl.DataFrame(data))
assert len(table) == 2
result = table.to_pandas()
assert np.allclose(result["vector"].tolist(), data["vector"])
assert result["item"].tolist() == data["item"]
assert np.allclose(result["price"].tolist(), data["price"])
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 2)),
pa.field("item", pa.large_string()),
pa.field("price", pa.float64()),
]
)
assert table.schema == schema
# search results to polars dataframe
q = [3.1, 4.1]
result = table.search(q).limit(1).to_polars()
assert np.allclose(result["vector"][0], q)
assert result["item"][0] == "foo"
assert np.allclose(result["price"][0], 10.0)
# enter table to polars dataframe
result = table.to_polars()
assert np.allclose(result.collect()["vector"].to_list(), data["vector"])
# make sure filtering isn't broken
filtered_result = result.filter(pl.col("item").is_in(["foo", "bar"])).collect()
assert len(filtered_result) == 2
def _add(table, schema):
# table = LanceTable(db, "test")
assert len(table) == 2
table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
assert len(table) == 3
expected = pa.Table.from_arrays(
[
pa.FixedSizeListArray.from_arrays(
pa.array([3.1, 4.1, 5.9, 26.5, 6.3, 100.5]), 2
),
pa.array(["foo", "bar", "new"]),
pa.array([10.0, 20.0, 30.0]),
],
schema=schema,
)
assert expected == table.to_arrow()
def test_versioning(db):
table = LanceTable.create(
db,
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
)
assert len(table.list_versions()) == 2
assert table.version == 2
table.add([{"vector": [6.3, 100.5], "item": "new", "price": 30.0}])
assert len(table.list_versions()) == 3
assert table.version == 3
assert len(table) == 3
table.checkout(2)
assert table.version == 2
assert len(table) == 2
def test_create_index_method():
with patch.object(
LanceTable, "_dataset_mut", new_callable=PropertyMock
) as mock_dataset:
# Setup mock responses
mock_dataset.return_value.create_index.return_value = None
# Create a LanceTable object
connection = LanceDBConnection(uri="mock.uri")
table = LanceTable(connection, "test_table")
# Call the create_index method
table.create_index(
metric="L2",
num_partitions=256,
num_sub_vectors=96,
vector_column_name="vector",
replace=True,
index_cache_size=256,
)
# Check that the _dataset.create_index method was called
# with the right parameters
mock_dataset.return_value.create_index.assert_called_once_with(
column="vector",
index_type="IVF_PQ",
metric="L2",
num_partitions=256,
num_sub_vectors=96,
replace=True,
accelerator=None,
index_cache_size=256,
)
def test_add_with_nans(db):
# by default we raise an error on bad input vectors
bad_data = [
{"vector": [np.nan], "item": "bar", "price": 20.0},
{"vector": [5], "item": "bar", "price": 20.0},
{"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
{"vector": [np.nan, 5.0], "item": "bar", "price": 20.0},
]
for row in bad_data:
with pytest.raises(ValueError):
LanceTable.create(
db,
"error_test",
data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, row],
)
table = LanceTable.create(
db,
"drop_test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [np.nan], "item": "bar", "price": 20.0},
{"vector": [5], "item": "bar", "price": 20.0},
{"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
],
on_bad_vectors="drop",
)
assert len(table) == 1
# We can fill bad input with some value
table = LanceTable.create(
db,
"fill_test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [np.nan], "item": "bar", "price": 20.0},
{"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
],
on_bad_vectors="fill",
fill_value=0.0,
)
assert len(table) == 3
arrow_tbl = table.to_lance().to_table(filter="item == 'bar'")
v = arrow_tbl["vector"].to_pylist()[0]
assert np.allclose(v, np.array([0.0, 0.0]))
def test_restore(db):
table = LanceTable.create(
db,
"my_table",
data=[{"vector": [1.1, 0.9], "type": "vector"}],
)
table.add([{"vector": [0.5, 0.2], "type": "vector"}])
table.restore(2)
assert len(table.list_versions()) == 4
assert len(table) == 1
expected = table.to_arrow()
table.checkout(2)
table.restore()
assert len(table.list_versions()) == 5
assert table.to_arrow() == expected
table.restore(5) # latest version should be no-op
assert len(table.list_versions()) == 5
with pytest.raises(ValueError):
table.restore(6)
with pytest.raises(ValueError):
table.restore(0)
def test_merge(db, tmp_path):
table = LanceTable.create(
db,
"my_table",
data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
)
other_table = pa.table({"document": ["foo", "bar"], "id": [0, 1]})
table.merge(other_table, left_on="id")
assert len(table.list_versions()) == 3
expected = pa.table(
{"vector": [[1.1, 0.9], [1.2, 1.9]], "id": [0, 1], "document": ["foo", "bar"]},
schema=table.schema,
)
assert table.to_arrow() == expected
other_dataset = lance.write_dataset(other_table, tmp_path / "other_table.lance")
table.restore(1)
table.merge(other_dataset, left_on="id")
def test_delete(db):
table = LanceTable.create(
db,
"my_table",
data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
)
assert len(table) == 2
assert len(table.list_versions()) == 2
table.delete("id=0")
assert len(table.list_versions()) == 3
assert table.version == 3
assert len(table) == 1
assert table.to_pandas()["id"].tolist() == [1]
def test_update(db):
table = LanceTable.create(
db,
"my_table",
data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
)
assert len(table) == 2
assert len(table.list_versions()) == 2
table.update(where="id=0", values={"vector": [1.1, 1.1]})
assert len(table.list_versions()) == 3
assert table.version == 3
assert len(table) == 2
v = table.to_arrow()["vector"].combine_chunks()
v = v.values.to_numpy().reshape(2, 2)
assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
def test_update_types(db):
table = LanceTable.create(
db,
"my_table",
data=[
{
"id": 0,
"str": "foo",
"float": 1.1,
"timestamp": datetime(2021, 1, 1),
"date": date(2021, 1, 1),
"vector1": [1.0, 0.0],
"vector2": [1.0, 1.0],
}
],
)
# Update with SQL
table.update(
values_sql=dict(
id="1",
str="'bar'",
float="2.2",
timestamp="TIMESTAMP '2021-01-02 00:00:00'",
date="DATE '2021-01-02'",
vector1="[2.0, 2.0]",
vector2="[3.0, 3.0]",
)
)
actual = table.to_arrow().to_pylist()[0]
expected = dict(
id=1,
str="bar",
float=2.2,
timestamp=datetime(2021, 1, 2),
date=date(2021, 1, 2),
vector1=[2.0, 2.0],
vector2=[3.0, 3.0],
)
assert actual == expected
# Update with values
table.update(
values=dict(
id=2,
str="baz",
float=3.3,
timestamp=datetime(2021, 1, 3),
date=date(2021, 1, 3),
vector1=[3.0, 3.0],
vector2=np.array([4.0, 4.0]),
)
)
actual = table.to_arrow().to_pylist()[0]
expected = dict(
id=2,
str="baz",
float=3.3,
timestamp=datetime(2021, 1, 3),
date=date(2021, 1, 3),
vector1=[3.0, 3.0],
vector2=[4.0, 4.0],
)
assert actual == expected
def test_merge_insert(db):
table = LanceTable.create(
db,
"my_table",
data=pa.table({"a": [1, 2, 3], "b": ["a", "b", "c"]}),
)
assert len(table) == 3
version = table.version
new_data = pa.table({"a": [2, 3, 4], "b": ["x", "y", "z"]})
# upsert
table.merge_insert(
"a"
).when_matched_update_all().when_not_matched_insert_all().execute(new_data)
expected = pa.table({"a": [1, 2, 3, 4], "b": ["a", "x", "y", "z"]})
assert table.to_arrow().sort_by("a") == expected
table.restore(version)
# conditional update
table.merge_insert("a").when_matched_update_all(where="target.b = 'b'").execute(
new_data
)
expected = pa.table({"a": [1, 2, 3], "b": ["a", "x", "c"]})
assert table.to_arrow().sort_by("a") == expected
table.restore(version)
# insert-if-not-exists
table.merge_insert("a").when_not_matched_insert_all().execute(new_data)
expected = pa.table({"a": [1, 2, 3, 4], "b": ["a", "b", "c", "z"]})
assert table.to_arrow().sort_by("a") == expected
table.restore(version)
new_data = pa.table({"a": [2, 4], "b": ["x", "z"]})
# replace-range
table.merge_insert(
"a"
).when_matched_update_all().when_not_matched_insert_all().when_not_matched_by_source_delete(
"a > 2"
).execute(new_data)
expected = pa.table({"a": [1, 2, 4], "b": ["a", "x", "z"]})
assert table.to_arrow().sort_by("a") == expected
table.restore(version)
# replace-range no condition
table.merge_insert(
"a"
).when_matched_update_all().when_not_matched_insert_all().when_not_matched_by_source_delete().execute(
new_data
)
expected = pa.table({"a": [2, 4], "b": ["x", "z"]})
assert table.to_arrow().sort_by("a") == expected
def test_create_with_embedding_function(db):
class MyTable(LanceModel):
text: str
vector: Vector(10)
func = MockTextEmbeddingFunction()
texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
df = pd.DataFrame({"text": texts, "vector": func.compute_source_embeddings(texts)})
conf = EmbeddingFunctionConfig(
source_column="text", vector_column="vector", function=func
)
table = LanceTable.create(
db,
"my_table",
schema=MyTable,
embedding_functions=[conf],
)
table.add(df)
query_str = "hi how are you?"
query_vector = func.compute_query_embeddings(query_str)[0]
expected = table.search(query_vector).limit(2).to_arrow()
actual = table.search(query_str).limit(2).to_arrow()
assert actual == expected
def test_create_f16_table(db):
class MyTable(LanceModel):
text: str
vector: Vector(128, value_type=pa.float16())
df = pd.DataFrame(
{
"text": [f"s-{i}" for i in range(10000)],
"vector": [np.random.randn(128).astype(np.float16) for _ in range(10000)],
}
)
table = LanceTable.create(
db,
"f16_tbl",
schema=MyTable,
)
table.add(df)
table.create_index(num_partitions=2, num_sub_vectors=8)
query = df.vector.iloc[2]
expected = table.search(query).limit(2).to_arrow()
assert "s-2" in expected["text"].to_pylist()
def test_add_with_embedding_function(db):
emb = EmbeddingFunctionRegistry.get_instance().get("test")()
class MyTable(LanceModel):
text: str = emb.SourceField()
vector: Vector(emb.ndims()) = emb.VectorField()
table = LanceTable.create(db, "my_table", schema=MyTable)
texts = ["hello world", "goodbye world", "foo bar baz fizz buzz"]
df = pd.DataFrame({"text": texts})
table.add(df)
texts = ["the quick brown fox", "jumped over the lazy dog"]
table.add([{"text": t} for t in texts])
query_str = "hi how are you?"
query_vector = emb.compute_query_embeddings(query_str)[0]
expected = table.search(query_vector).limit(2).to_arrow()
actual = table.search(query_str).limit(2).to_arrow()
assert actual == expected
def test_multiple_vector_columns(db):
class MyTable(LanceModel):
text: str
vector1: Vector(10)
vector2: Vector(10)
table = LanceTable.create(
db,
"my_table",
schema=MyTable,
)
v1 = np.random.randn(10)
v2 = np.random.randn(10)
data = [
{"vector1": v1, "vector2": v2, "text": "foo"},
{"vector1": v2, "vector2": v1, "text": "bar"},
]
df = pd.DataFrame(data)
table.add(df)
q = np.random.randn(10)
result1 = table.search(q, vector_column_name="vector1").limit(1).to_pandas()
result2 = table.search(q, vector_column_name="vector2").limit(1).to_pandas()
assert result1["text"].iloc[0] != result2["text"].iloc[0]
def test_create_scalar_index(db):
vec_array = pa.array(
[[1, 1], [2, 2], [3, 3], [4, 4], [5, 5]], pa.list_(pa.float32(), 2)
)
test_data = pa.Table.from_pydict(
{"x": ["c", "b", "a", "e", "b"], "y": [1, 2, 3, 4, 5], "vector": vec_array}
)
table = LanceTable.create(
db,
"my_table",
data=test_data,
)
table.create_scalar_index("x")
indices = table.to_lance().list_indices()
assert len(indices) == 1
scalar_index = indices[0]
assert scalar_index["type"] == "Scalar"
# Confirm that prefiltering still works with the scalar index column
results = table.search().where("x = 'c'").to_arrow()
assert results == test_data.slice(0, 1)
results = table.search([5, 5]).to_arrow()
assert results["_distance"][0].as_py() == 0
results = table.search([5, 5]).where("x != 'b'").to_arrow()
assert results["_distance"][0].as_py() > 0
def test_empty_query(db):
table = LanceTable.create(
db,
"my_table",
data=[{"text": "foo", "id": 0}, {"text": "bar", "id": 1}],
)
df = table.search().select(["id"]).where("text='bar'").limit(1).to_pandas()
val = df.id.iloc[0]
assert val == 1
table = LanceTable.create(db, "my_table2", data=[{"id": i} for i in range(100)])
df = table.search().select(["id"]).to_pandas()
assert len(df) == 10
df = table.search().select(["id"]).limit(None).to_pandas()
assert len(df) == 100
df = table.search().select(["id"]).limit(-1).to_pandas()
assert len(df) == 100
def test_search_with_schema_inf_single_vector(db):
class MyTable(LanceModel):
text: str
vector_col: Vector(10)
table = LanceTable.create(
db,
"my_table",
schema=MyTable,
)
v1 = np.random.randn(10)
v2 = np.random.randn(10)
data = [
{"vector_col": v1, "text": "foo"},
{"vector_col": v2, "text": "bar"},
]
df = pd.DataFrame(data)
table.add(df)
q = np.random.randn(10)
result1 = table.search(q, vector_column_name="vector_col").limit(1).to_pandas()
result2 = table.search(q).limit(1).to_pandas()
assert result1["text"].iloc[0] == result2["text"].iloc[0]
def test_search_with_schema_inf_multiple_vector(db):
class MyTable(LanceModel):
text: str
vector1: Vector(10)
vector2: Vector(10)
table = LanceTable.create(
db,
"my_table",
schema=MyTable,
)
v1 = np.random.randn(10)
v2 = np.random.randn(10)
data = [
{"vector1": v1, "vector2": v2, "text": "foo"},
{"vector1": v2, "vector2": v1, "text": "bar"},
]
df = pd.DataFrame(data)
table.add(df)
q = np.random.randn(10)
with pytest.raises(ValueError):
table.search(q).limit(1).to_pandas()
def test_compact_cleanup(db):
table = LanceTable.create(
db,
"my_table",
data=[{"text": "foo", "id": 0}, {"text": "bar", "id": 1}],
)
table.add([{"text": "baz", "id": 2}])
assert len(table) == 3
assert table.version == 3
stats = table.compact_files()
assert len(table) == 3
# Compact_files bump 2 versions.
assert table.version == 5
assert stats.fragments_removed > 0
assert stats.fragments_added == 1
stats = table.cleanup_old_versions()
assert stats.bytes_removed == 0
stats = table.cleanup_old_versions(older_than=timedelta(0), delete_unverified=True)
assert stats.bytes_removed > 0
assert table.version == 5
with pytest.raises(Exception, match="Version 3 no longer exists"):
table.checkout(3)
def test_count_rows(db):
table = LanceTable.create(
db,
"my_table",
data=[{"text": "foo", "id": 0}, {"text": "bar", "id": 1}],
)
assert len(table) == 2
assert table.count_rows() == 2
assert table.count_rows(filter="text='bar'") == 1
def test_hybrid_search(db, tmp_path):
# This test uses an FTS index
pytest.importorskip("lancedb.fts")
db = MockDB(str(tmp_path))
# Create a LanceDB table schema with a vector and a text column
emb = EmbeddingFunctionRegistry.get_instance().get("test")()
class MyTable(LanceModel):
text: str = emb.SourceField()
vector: Vector(emb.ndims()) = emb.VectorField()
# Initialize the table using the schema
table = LanceTable.create(
db,
"my_table",
schema=MyTable,
)
# Create a list of 10 unique english phrases
phrases = [
"great kid don't get cocky",
"now that's a name I haven't heard in a long time",
"if you strike me down I shall become more powerful than you imagine",
"I find your lack of faith disturbing",
"I've got a bad feeling about this",
"never tell me the odds",
"I am your father",
"somebody has to save our skins",
"New strategy R2 let the wookiee win",
"Arrrrggghhhhhhh",
]
# Add the phrases and vectors to the table
table.add([{"text": p} for p in phrases])
# Create a fts index
table.create_fts_index("text")
result1 = (
table.search("Our father who art in heaven", query_type="hybrid")
.rerank(normalize="score")
.to_pydantic(MyTable)
)
result2 = ( # noqa
table.search("Our father who art in heaven", query_type="hybrid")
.rerank(normalize="rank")
.to_pydantic(MyTable)
)
result3 = table.search(
"Our father who art in heaven", query_type="hybrid"
).to_pydantic(MyTable)
assert result1 == result3
# with post filters
result = (
table.search("Arrrrggghhhhhhh", query_type="hybrid")
.where("text='Arrrrggghhhhhhh'")
.to_list()
)
len(result) == 1
@pytest.mark.parametrize(
"consistency_interval", [None, timedelta(seconds=0), timedelta(seconds=0.1)]
)
def test_consistency(tmp_path, consistency_interval):
db = lancedb.connect(tmp_path)
table = LanceTable.create(db, "my_table", data=[{"id": 0}])
db2 = lancedb.connect(tmp_path, read_consistency_interval=consistency_interval)
table2 = db2.open_table("my_table")
assert table2.version == table.version
table.add([{"id": 1}])
if consistency_interval is None:
assert table2.version == table.version - 1
table2.checkout_latest()
assert table2.version == table.version
elif consistency_interval == timedelta(seconds=0):
assert table2.version == table.version
else:
# (consistency_interval == timedelta(seconds=0.1)
assert table2.version == table.version - 1
sleep(0.1)
assert table2.version == table.version
def test_restore_consistency(tmp_path):
db = lancedb.connect(tmp_path)
table = LanceTable.create(db, "my_table", data=[{"id": 0}])
db2 = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
table2 = db2.open_table("my_table")
assert table2.version == table.version
# If we call checkout, it should lose consistency
table_fixed = copy(table2)
table_fixed.checkout(table.version)
# But if we call checkout_latest, it should be consistent again
table_ref_latest = copy(table_fixed)
table_ref_latest.checkout_latest()
table.add([{"id": 2}])
assert table_fixed.version == table.version - 1
assert table_ref_latest.version == table.version
# Schema evolution
def test_add_columns(tmp_path):
db = lancedb.connect(tmp_path)
data = pa.table({"id": [0, 1]})
table = LanceTable.create(db, "my_table", data=data)
table.add_columns({"new_col": "id + 2"})
assert table.to_arrow().column_names == ["id", "new_col"]
assert table.to_arrow()["new_col"].to_pylist() == [2, 3]
def test_alter_columns(tmp_path):
db = lancedb.connect(tmp_path)
data = pa.table({"id": [0, 1]})
table = LanceTable.create(db, "my_table", data=data)
table.alter_columns({"path": "id", "rename": "new_id"})
assert table.to_arrow().column_names == ["new_id"]
def test_drop_columns(tmp_path):
db = lancedb.connect(tmp_path)
data = pa.table({"id": [0, 1], "category": ["a", "b"]})
table = LanceTable.create(db, "my_table", data=data)
table.drop_columns(["category"])
assert table.to_arrow().column_names == ["id"]
@pytest.mark.asyncio
async def test_time_travel(db_async: AsyncConnection):
# Setup
table = await db_async.create_table("some_table", data=[{"id": 0}])
version = await table.version()
await table.add([{"id": 1}])
assert await table.count_rows() == 2
# Make sure we can rewind
await table.checkout(version)
assert await table.count_rows() == 1
# Can't add data in time travel mode
with pytest.raises(
ValueError,
match="table cannot be modified when a specific version is checked out",
):
await table.add([{"id": 2}])
# Can go back to normal mode
await table.checkout_latest()
assert await table.count_rows() == 2
# Should be able to add data again
await table.add([{"id": 3}])
assert await table.count_rows() == 3
# Now checkout and restore
await table.checkout(version)
await table.restore()
assert await table.count_rows() == 1
# Should be able to add data
await table.add([{"id": 4}])
assert await table.count_rows() == 2
# Can't use restore if not checked out
with pytest.raises(ValueError, match="checkout before running restore"):
await table.restore()
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"lancedb.table.LanceTable.create",
"lancedb.db.LanceDBConnection",
"lancedb.embeddings.EmbeddingFunctionConfig",
"lancedb.embeddings.EmbeddingFunctionRegistry.get_instance"
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import functools
import logging
import os
from typing import Any, Callable, Dict, List, Optional, Union
from urllib.parse import urljoin
import attrs
import pyarrow as pa
import requests
from pydantic import BaseModel
from requests.adapters import HTTPAdapter
from urllib3 import Retry
from lancedb.common import Credential
from lancedb.remote import VectorQuery, VectorQueryResult
from lancedb.remote.connection_timeout import LanceDBClientHTTPAdapterFactory
from lancedb.remote.errors import LanceDBClientError
ARROW_STREAM_CONTENT_TYPE = "application/vnd.apache.arrow.stream"
def _check_not_closed(f):
@functools.wraps(f)
def wrapped(self, *args, **kwargs):
if self.closed:
raise ValueError("Connection is closed")
return f(self, *args, **kwargs)
return wrapped
def _read_ipc(resp: requests.Response) -> pa.Table:
resp_body = resp.content
with pa.ipc.open_file(pa.BufferReader(resp_body)) as reader:
return reader.read_all()
@attrs.define(slots=False)
class RestfulLanceDBClient:
db_name: str
region: str
api_key: Credential
host_override: Optional[str] = attrs.field(default=None)
closed: bool = attrs.field(default=False, init=False)
@functools.cached_property
def session(self) -> requests.Session:
sess = requests.Session()
retry_adapter_instance = retry_adapter(retry_adapter_options())
sess.mount(urljoin(self.url, "/v1/table/"), retry_adapter_instance)
adapter_class = LanceDBClientHTTPAdapterFactory()
sess.mount("https://", adapter_class())
return sess
@property
def url(self) -> str:
return (
self.host_override
or f"https://{self.db_name}.{self.region}.api.lancedb.com"
)
def close(self):
self.session.close()
self.closed = True
@functools.cached_property
def headers(self) -> Dict[str, str]:
headers = {
"x-api-key": self.api_key,
}
if self.region == "local": # Local test mode
headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
if self.host_override:
headers["x-lancedb-database"] = self.db_name
return headers
@staticmethod
def _check_status(resp: requests.Response):
if resp.status_code == 404:
raise LanceDBClientError(f"Not found: {resp.text}")
elif 400 <= resp.status_code < 500:
raise LanceDBClientError(
f"Bad Request: {resp.status_code}, error: {resp.text}"
)
elif 500 <= resp.status_code < 600:
raise LanceDBClientError(
f"Internal Server Error: {resp.status_code}, error: {resp.text}"
)
elif resp.status_code != 200:
raise LanceDBClientError(
f"Unknown Error: {resp.status_code}, error: {resp.text}"
)
@_check_not_closed
def get(self, uri: str, params: Union[Dict[str, Any], BaseModel] = None):
"""Send a GET request and returns the deserialized response payload."""
if isinstance(params, BaseModel):
params: Dict[str, Any] = params.dict(exclude_none=True)
with self.session.get(
urljoin(self.url, uri),
params=params,
headers=self.headers,
timeout=(120.0, 300.0),
) as resp:
self._check_status(resp)
return resp.json()
@_check_not_closed
def post(
self,
uri: str,
data: Optional[Union[Dict[str, Any], BaseModel, bytes]] = None,
params: Optional[Dict[str, Any]] = None,
content_type: Optional[str] = None,
deserialize: Callable = lambda resp: resp.json(),
request_id: Optional[str] = None,
) -> Dict[str, Any]:
"""Send a POST request and returns the deserialized response payload.
Parameters
----------
uri : str
The uri to send the POST request to.
data: Union[Dict[str, Any], BaseModel]
request_id: Optional[str]
Optional client side request id to be sent in the request headers.
"""
if isinstance(data, BaseModel):
data: Dict[str, Any] = data.dict(exclude_none=True)
if isinstance(data, bytes):
req_kwargs = {"data": data}
else:
req_kwargs = {"json": data}
headers = self.headers.copy()
if content_type is not None:
headers["content-type"] = content_type
if request_id is not None:
headers["x-request-id"] = request_id
with self.session.post(
urljoin(self.url, uri),
headers=headers,
params=params,
timeout=(120.0, 300.0),
**req_kwargs,
) as resp:
self._check_status(resp)
return deserialize(resp)
@_check_not_closed
def list_tables(self, limit: int, page_token: Optional[str] = None) -> List[str]:
"""List all tables in the database."""
if page_token is None:
page_token = ""
json = self.get("/v1/table/", {"limit": limit, "page_token": page_token})
return json["tables"]
@_check_not_closed
def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
"""Query a table."""
tbl = self.post(f"/v1/table/{table_name}/query/", query, deserialize=_read_ipc)
return VectorQueryResult(tbl)
def mount_retry_adapter_for_table(self, table_name: str) -> None:
"""
Adds an http adapter to session that will retry retryable requests to the table.
"""
retry_options = retry_adapter_options(methods=["GET", "POST"])
retry_adapter_instance = retry_adapter(retry_options)
session = self.session
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/query/"), retry_adapter_instance
)
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/describe/"),
retry_adapter_instance,
)
session.mount(
urljoin(self.url, f"/v1/table/{table_name}/index/list/"),
retry_adapter_instance,
)
def retry_adapter_options(methods=["GET"]) -> Dict[str, Any]:
return {
"retries": int(os.environ.get("LANCE_CLIENT_MAX_RETRIES", "3")),
"connect_retries": int(os.environ.get("LANCE_CLIENT_CONNECT_RETRIES", "3")),
"read_retries": int(os.environ.get("LANCE_CLIENT_READ_RETRIES", "3")),
"backoff_factor": float(
os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_FACTOR", "0.25")
),
"backoff_jitter": float(
os.environ.get("LANCE_CLIENT_RETRY_BACKOFF_JITTER", "0.25")
),
"statuses": [
int(i.strip())
for i in os.environ.get(
"LANCE_CLIENT_RETRY_STATUSES", "429, 500, 502, 503"
).split(",")
],
"methods": methods,
}
def retry_adapter(options: Dict[str, Any]) -> HTTPAdapter:
total_retries = options["retries"]
connect_retries = options["connect_retries"]
read_retries = options["read_retries"]
backoff_factor = options["backoff_factor"]
backoff_jitter = options["backoff_jitter"]
statuses = options["statuses"]
methods = frozenset(options["methods"])
logging.debug(
f"Setting up retry adapter with {total_retries} retries," # noqa G003
+ f"connect retries {connect_retries}, read retries {read_retries},"
+ f"backoff factor {backoff_factor}, statuses {statuses}, "
+ f"methods {methods}"
)
return HTTPAdapter(
max_retries=Retry(
total=total_retries,
connect=connect_retries,
read=read_retries,
backoff_factor=backoff_factor,
backoff_jitter=backoff_jitter,
status_forcelist=statuses,
allowed_methods=methods,
)
)
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import sys
from datetime import date, datetime
from typing import List, Optional, Tuple
import pyarrow as pa
import pydantic
import pytest
from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
from pydantic import Field
@pytest.mark.skipif(
sys.version_info < (3, 9),
reason="using native type alias requires python3.9 or higher",
)
def test_pydantic_to_arrow():
class StructModel(pydantic.BaseModel):
a: str
b: Optional[float]
class TestModel(pydantic.BaseModel):
id: int
s: str
vec: list[float]
li: list[int]
lili: list[list[float]]
litu: list[tuple[float, float]]
opt: Optional[str] = None
st: StructModel
dt: date
dtt: datetime
dt_with_tz: datetime = Field(json_schema_extra={"tz": "Asia/Shanghai"})
# d: dict
# TODO: test we can actually convert the model into data.
# m = TestModel(
# id=1,
# s="hello",
# vec=[1.0, 2.0, 3.0],
# li=[2, 3, 4],
# lili=[[2.5, 1.5], [3.5, 4.5], [5.5, 6.5]],
# litu=[(2.5, 1.5), (3.5, 4.5), (5.5, 6.5)],
# st=StructModel(a="a", b=1.0),
# dt=date.today(),
# dtt=datetime.now(),
# dt_with_tz=datetime.now(pytz.timezone("Asia/Shanghai")),
# )
schema = pydantic_to_schema(TestModel)
expect_schema = pa.schema(
[
pa.field("id", pa.int64(), False),
pa.field("s", pa.utf8(), False),
pa.field("vec", pa.list_(pa.float64()), False),
pa.field("li", pa.list_(pa.int64()), False),
pa.field("lili", pa.list_(pa.list_(pa.float64())), False),
pa.field("litu", pa.list_(pa.list_(pa.float64())), False),
pa.field("opt", pa.utf8(), True),
pa.field(
"st",
pa.struct(
[pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
),
False,
),
pa.field("dt", pa.date32(), False),
pa.field("dtt", pa.timestamp("us"), False),
pa.field("dt_with_tz", pa.timestamp("us", tz="Asia/Shanghai"), False),
]
)
assert schema == expect_schema
@pytest.mark.skipif(
sys.version_info < (3, 10),
reason="using | type syntax requires python3.10 or higher",
)
def test_optional_types_py310():
class TestModel(pydantic.BaseModel):
a: str | None
b: None | str
c: Optional[str]
schema = pydantic_to_schema(TestModel)
expect_schema = pa.schema(
[
pa.field("a", pa.utf8(), True),
pa.field("b", pa.utf8(), True),
pa.field("c", pa.utf8(), True),
]
)
assert schema == expect_schema
@pytest.mark.skipif(
sys.version_info > (3, 8),
reason="using native type alias requires python3.9 or higher",
)
def test_pydantic_to_arrow_py38():
class StructModel(pydantic.BaseModel):
a: str
b: Optional[float]
class TestModel(pydantic.BaseModel):
id: int
s: str
vec: List[float]
li: List[int]
lili: List[List[float]]
litu: List[Tuple[float, float]]
opt: Optional[str] = None
st: StructModel
dt: date
dtt: datetime
dt_with_tz: datetime = Field(json_schema_extra={"tz": "Asia/Shanghai"})
# d: dict
# TODO: test we can actually convert the model to Arrow data.
# m = TestModel(
# id=1,
# s="hello",
# vec=[1.0, 2.0, 3.0],
# li=[2, 3, 4],
# lili=[[2.5, 1.5], [3.5, 4.5], [5.5, 6.5]],
# litu=[(2.5, 1.5), (3.5, 4.5), (5.5, 6.5)],
# st=StructModel(a="a", b=1.0),
# dt=date.today(),
# dtt=datetime.now(),
# dt_with_tz=datetime.now(pytz.timezone("Asia/Shanghai")),
# )
schema = pydantic_to_schema(TestModel)
expect_schema = pa.schema(
[
pa.field("id", pa.int64(), False),
pa.field("s", pa.utf8(), False),
pa.field("vec", pa.list_(pa.float64()), False),
pa.field("li", pa.list_(pa.int64()), False),
pa.field("lili", pa.list_(pa.list_(pa.float64())), False),
pa.field("litu", pa.list_(pa.list_(pa.float64())), False),
pa.field("opt", pa.utf8(), True),
pa.field(
"st",
pa.struct(
[pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
),
False,
),
pa.field("dt", pa.date32(), False),
pa.field("dtt", pa.timestamp("us"), False),
pa.field("dt_with_tz", pa.timestamp("us", tz="Asia/Shanghai"), False),
]
)
assert schema == expect_schema
def test_fixed_size_list_field():
class TestModel(pydantic.BaseModel):
vec: Vector(16)
li: List[int]
data = TestModel(vec=list(range(16)), li=[1, 2, 3])
if PYDANTIC_VERSION >= (2,):
assert json.loads(data.model_dump_json()) == {
"vec": list(range(16)),
"li": [1, 2, 3],
}
else:
assert data.dict() == {
"vec": list(range(16)),
"li": [1, 2, 3],
}
schema = pydantic_to_schema(TestModel)
assert schema == pa.schema(
[
pa.field("vec", pa.list_(pa.float32(), 16), False),
pa.field("li", pa.list_(pa.int64()), False),
]
)
if PYDANTIC_VERSION >= (2,):
json_schema = TestModel.model_json_schema()
else:
json_schema = TestModel.schema()
assert json_schema == {
"properties": {
"vec": {
"items": {"type": "number"},
"maxItems": 16,
"minItems": 16,
"title": "Vec",
"type": "array",
},
"li": {"items": {"type": "integer"}, "title": "Li", "type": "array"},
},
"required": ["vec", "li"],
"title": "TestModel",
"type": "object",
}
def test_fixed_size_list_validation():
class TestModel(pydantic.BaseModel):
vec: Vector(8)
with pytest.raises(pydantic.ValidationError):
TestModel(vec=range(9))
with pytest.raises(pydantic.ValidationError):
TestModel(vec=range(7))
TestModel(vec=range(8))
def test_lance_model():
class TestModel(LanceModel):
vector: Vector(16) = Field(default=[0.0] * 16)
li: List[int] = Field(default=[1, 2, 3])
schema = pydantic_to_schema(TestModel)
assert schema == TestModel.to_arrow_schema()
assert TestModel.field_names() == ["vector", "li"]
t = TestModel()
assert t == TestModel(vec=[0.0] * 16, li=[1, 2, 3])
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"lancedb.pydantic.Vector",
"lancedb.pydantic.pydantic_to_schema"
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(5127, 5129), True, 'import pyarrow as pa\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest.mock as mock
from datetime import timedelta
import lance
import lancedb
import numpy as np
import pandas.testing as tm
import pyarrow as pa
import pytest
import pytest_asyncio
from lancedb.db import LanceDBConnection
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import AsyncQueryBase, LanceVectorQueryBuilder, Query
from lancedb.table import AsyncTable, LanceTable
class MockTable:
def __init__(self, tmp_path):
self.uri = tmp_path
self._conn = LanceDBConnection(self.uri)
def to_lance(self):
return lance.dataset(self.uri)
def _execute_query(self, query):
ds = self.to_lance()
return ds.to_table(
columns=query.columns,
filter=query.filter,
prefilter=query.prefilter,
nearest={
"column": query.vector_column,
"q": query.vector,
"k": query.k,
"metric": query.metric,
"nprobes": query.nprobes,
"refine_factor": query.refine_factor,
},
)
@pytest.fixture
def table(tmp_path) -> MockTable:
df = pa.table(
{
"vector": pa.array(
[[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
),
"id": pa.array([1, 2]),
"str_field": pa.array(["a", "b"]),
"float_field": pa.array([1.0, 2.0]),
}
)
lance.write_dataset(df, tmp_path)
return MockTable(tmp_path)
@pytest_asyncio.fixture
async def table_async(tmp_path) -> AsyncTable:
conn = await lancedb.connect_async(
tmp_path, read_consistency_interval=timedelta(seconds=0)
)
data = pa.table(
{
"vector": pa.array(
[[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
),
"id": pa.array([1, 2]),
"str_field": pa.array(["a", "b"]),
"float_field": pa.array([1.0, 2.0]),
}
)
return await conn.create_table("test", data)
def test_cast(table):
class TestModel(LanceModel):
vector: Vector(2)
id: int
str_field: str
float_field: float
q = LanceVectorQueryBuilder(table, [0, 0], "vector").limit(1)
results = q.to_pydantic(TestModel)
assert len(results) == 1
r0 = results[0]
assert isinstance(r0, TestModel)
assert r0.id == 1
assert r0.vector == [1, 2]
assert r0.str_field == "a"
assert r0.float_field == 1.0
def test_query_builder(table):
rs = (
LanceVectorQueryBuilder(table, [0, 0], "vector")
.limit(1)
.select(["id", "vector"])
.to_list()
)
assert rs[0]["id"] == 1
assert all(np.array(rs[0]["vector"]) == [1, 2])
def test_dynamic_projection(table):
rs = (
LanceVectorQueryBuilder(table, [0, 0], "vector")
.limit(1)
.select({"id": "id", "id2": "id * 2"})
.to_list()
)
assert rs[0]["id"] == 1
assert rs[0]["id2"] == 2
def test_query_builder_with_filter(table):
rs = LanceVectorQueryBuilder(table, [0, 0], "vector").where("id = 2").to_list()
assert rs[0]["id"] == 2
assert all(np.array(rs[0]["vector"]) == [3, 4])
def test_query_builder_with_prefilter(table):
df = (
LanceVectorQueryBuilder(table, [0, 0], "vector")
.where("id = 2")
.limit(1)
.to_pandas()
)
assert len(df) == 0
df = (
LanceVectorQueryBuilder(table, [0, 0], "vector")
.where("id = 2", prefilter=True)
.limit(1)
.to_pandas()
)
assert df["id"].values[0] == 2
assert all(df["vector"].values[0] == [3, 4])
def test_query_builder_with_metric(table):
query = [4, 8]
vector_column_name = "vector"
df_default = LanceVectorQueryBuilder(table, query, vector_column_name).to_pandas()
df_l2 = (
LanceVectorQueryBuilder(table, query, vector_column_name)
.metric("L2")
.to_pandas()
)
tm.assert_frame_equal(df_default, df_l2)
df_cosine = (
LanceVectorQueryBuilder(table, query, vector_column_name)
.metric("cosine")
.limit(1)
.to_pandas()
)
assert df_cosine._distance[0] == pytest.approx(
cosine_distance(query, df_cosine.vector[0]),
abs=1e-6,
)
assert 0 <= df_cosine._distance[0] <= 1
def test_query_builder_with_different_vector_column():
table = mock.MagicMock(spec=LanceTable)
query = [4, 8]
vector_column_name = "foo_vector"
builder = (
LanceVectorQueryBuilder(table, query, vector_column_name)
.metric("cosine")
.where("b < 10")
.select(["b"])
.limit(2)
)
ds = mock.Mock()
table.to_lance.return_value = ds
builder.to_arrow()
table._execute_query.assert_called_once_with(
Query(
vector=query,
filter="b < 10",
k=2,
metric="cosine",
columns=["b"],
nprobes=20,
refine_factor=None,
vector_column="foo_vector",
)
)
def cosine_distance(vec1, vec2):
return 1 - np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
async def check_query(
query: AsyncQueryBase, *, expected_num_rows=None, expected_columns=None
):
num_rows = 0
results = await query.to_batches()
async for batch in results:
if expected_columns is not None:
assert batch.schema.names == expected_columns
num_rows += batch.num_rows
if expected_num_rows is not None:
assert num_rows == expected_num_rows
@pytest.mark.asyncio
async def test_query_async(table_async: AsyncTable):
await check_query(
table_async.query(),
expected_num_rows=2,
expected_columns=["vector", "id", "str_field", "float_field"],
)
await check_query(table_async.query().where("id = 2"), expected_num_rows=1)
await check_query(
table_async.query().select(["id", "vector"]), expected_columns=["id", "vector"]
)
await check_query(
table_async.query().select({"foo": "id", "bar": "id + 1"}),
expected_columns=["foo", "bar"],
)
await check_query(table_async.query().limit(1), expected_num_rows=1)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])), expected_num_rows=2
)
# Support different types of inputs for the vector query
for vector_query in [
[1, 2],
[1.0, 2.0],
np.array([1, 2]),
(1, 2),
]:
await check_query(
table_async.query().nearest_to(vector_query), expected_num_rows=2
)
# No easy way to check these vector query parameters are doing what they say. We
# just check that they don't raise exceptions and assume this is tested at a lower
# level.
await check_query(
table_async.query().where("id = 2").nearest_to(pa.array([1, 2])).postfilter(),
expected_num_rows=1,
)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])).refine_factor(1),
expected_num_rows=2,
)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])).nprobes(10),
expected_num_rows=2,
)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])).bypass_vector_index(),
expected_num_rows=2,
)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])).distance_type("dot"),
expected_num_rows=2,
)
await check_query(
table_async.query().nearest_to(pa.array([1, 2])).distance_type("DoT"),
expected_num_rows=2,
)
# Make sure we can use a vector query as a base query (e.g. call limit on it)
# Also make sure `vector_search` works
await check_query(table_async.vector_search([1, 2]).limit(1), expected_num_rows=1)
# Also check an empty query
await check_query(table_async.query().where("id < 0"), expected_num_rows=0)
@pytest.mark.asyncio
async def test_query_to_arrow_async(table_async: AsyncTable):
table = await table_async.to_arrow()
assert table.num_rows == 2
assert table.num_columns == 4
table = await table_async.query().to_arrow()
assert table.num_rows == 2
assert table.num_columns == 4
table = await table_async.query().where("id < 0").to_arrow()
assert table.num_rows == 0
assert table.num_columns == 4
@pytest.mark.asyncio
async def test_query_to_pandas_async(table_async: AsyncTable):
df = await table_async.to_pandas()
assert df.shape == (2, 4)
df = await table_async.query().to_pandas()
assert df.shape == (2, 4)
df = await table_async.query().where("id < 0").to_pandas()
assert df.shape == (0, 4)
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"lancedb.pydantic.Vector",
"lancedb.query.Query",
"lancedb.query.LanceVectorQueryBuilder",
"lancedb.db.LanceDBConnection"
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|
# Copyright (c) 2023. LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
import io
import os
import lancedb
import numpy as np
import pandas as pd
import pytest
import requests
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
# These are integration tests for embedding functions.
# They are slow because they require downloading models
# or connection to external api
try:
if importlib.util.find_spec("mlx.core") is not None:
_mlx = True
else:
_mlx = None
except Exception:
_mlx = None
try:
if importlib.util.find_spec("imagebind") is not None:
_imagebind = True
else:
_imagebind = None
except Exception:
_imagebind = None
@pytest.mark.slow
@pytest.mark.parametrize("alias", ["sentence-transformers", "openai"])
def test_basic_text_embeddings(alias, tmp_path):
db = lancedb.connect(tmp_path)
registry = get_registry()
func = registry.get(alias).create(max_retries=0)
func2 = registry.get(alias).create(max_retries=0)
class Words(LanceModel):
text: str = func.SourceField()
text2: str = func2.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
vector2: Vector(func2.ndims()) = func2.VectorField()
table = db.create_table("words", schema=Words)
table.add(
pd.DataFrame(
{
"text": [
"hello world",
"goodbye world",
"fizz",
"buzz",
"foo",
"bar",
"baz",
],
"text2": [
"to be or not to be",
"that is the question",
"for whether tis nobler",
"in the mind to suffer",
"the slings and arrows",
"of outrageous fortune",
"or to take arms",
],
}
)
)
query = "greetings"
actual = (
table.search(query, vector_column_name="vector").limit(1).to_pydantic(Words)[0]
)
vec = func.compute_query_embeddings(query)[0]
expected = (
table.search(vec, vector_column_name="vector").limit(1).to_pydantic(Words)[0]
)
assert actual.text == expected.text
assert actual.text == "hello world"
assert not np.allclose(actual.vector, actual.vector2)
actual = (
table.search(query, vector_column_name="vector2").limit(1).to_pydantic(Words)[0]
)
assert actual.text != "hello world"
assert not np.allclose(actual.vector, actual.vector2)
@pytest.mark.slow
def test_openclip(tmp_path):
from PIL import Image
db = lancedb.connect(tmp_path)
registry = get_registry()
func = registry.get("open-clip").create(max_retries=0)
class Images(LanceModel):
label: str
image_uri: str = func.SourceField()
image_bytes: bytes = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
vec_from_bytes: Vector(func.ndims()) = func.VectorField()
table = db.create_table("images", schema=Images)
labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
uris = [
"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
]
# get each uri as bytes
image_bytes = [requests.get(uri).content for uri in uris]
table.add(
pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
)
# text search
actual = (
table.search("man's best friend", vector_column_name="vector")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == "dog"
frombytes = (
table.search("man's best friend", vector_column_name="vec_from_bytes")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == frombytes.label
assert np.allclose(actual.vector, frombytes.vector)
# image search
query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
image_bytes = requests.get(query_image_uri).content
query_image = Image.open(io.BytesIO(image_bytes))
actual = (
table.search(query_image, vector_column_name="vector")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == "dog"
other = (
table.search(query_image, vector_column_name="vec_from_bytes")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == other.label
arrow_table = table.search().select(["vector", "vec_from_bytes"]).to_arrow()
assert np.allclose(
arrow_table["vector"].combine_chunks().values.to_numpy(),
arrow_table["vec_from_bytes"].combine_chunks().values.to_numpy(),
)
@pytest.mark.skipif(
_imagebind is None,
reason="skip if imagebind not installed.",
)
@pytest.mark.slow
def test_imagebind(tmp_path):
import os
import shutil
import tempfile
import lancedb.embeddings.imagebind
import pandas as pd
import requests
from lancedb.embeddings import get_registry
from lancedb.pydantic import LanceModel, Vector
with tempfile.TemporaryDirectory() as temp_dir:
print(f"Created temporary directory {temp_dir}")
def download_images(image_uris):
downloaded_image_paths = []
for uri in image_uris:
try:
response = requests.get(uri, stream=True)
if response.status_code == 200:
# Extract image name from URI
image_name = os.path.basename(uri)
image_path = os.path.join(temp_dir, image_name)
with open(image_path, "wb") as out_file:
shutil.copyfileobj(response.raw, out_file)
downloaded_image_paths.append(image_path)
except Exception as e: # noqa: PERF203
print(f"Failed to download {uri}. Error: {e}")
return temp_dir, downloaded_image_paths
db = lancedb.connect(tmp_path)
registry = get_registry()
func = registry.get("imagebind").create(max_retries=0)
class Images(LanceModel):
label: str
image_uri: str = func.SourceField()
vector: Vector(func.ndims()) = func.VectorField()
table = db.create_table("images", schema=Images)
labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
uris = [
"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
]
temp_dir, downloaded_images = download_images(uris)
table.add(pd.DataFrame({"label": labels, "image_uri": downloaded_images}))
# text search
actual = (
table.search("man's best friend", vector_column_name="vector")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == "dog"
# image search
query_image_uri = [
"https://live.staticflickr.com/65535/33336453970_491665f66e_h.jpg"
]
temp_dir, downloaded_images = download_images(query_image_uri)
query_image_uri = downloaded_images[0]
actual = (
table.search(query_image_uri, vector_column_name="vector")
.limit(1)
.to_pydantic(Images)[0]
)
assert actual.label == "dog"
if os.path.isdir(temp_dir):
shutil.rmtree(temp_dir)
print(f"Deleted temporary directory {temp_dir}")
@pytest.mark.slow
@pytest.mark.skipif(
os.environ.get("COHERE_API_KEY") is None, reason="COHERE_API_KEY not set"
) # also skip if cohere not installed
def test_cohere_embedding_function():
cohere = (
get_registry()
.get("cohere")
.create(name="embed-multilingual-v2.0", max_retries=0)
)
class TextModel(LanceModel):
text: str = cohere.SourceField()
vector: Vector(cohere.ndims()) = cohere.VectorField()
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect("~/lancedb")
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == cohere.ndims()
@pytest.mark.slow
def test_instructor_embedding(tmp_path):
model = get_registry().get("instructor").create(max_retries=0)
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect(tmp_path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
@pytest.mark.slow
@pytest.mark.skipif(
os.environ.get("GOOGLE_API_KEY") is None, reason="GOOGLE_API_KEY not set"
)
def test_gemini_embedding(tmp_path):
model = get_registry().get("gemini-text").create(max_retries=0)
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect(tmp_path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
@pytest.mark.skipif(
_mlx is None,
reason="mlx tests only required for apple users.",
)
@pytest.mark.slow
def test_gte_embedding(tmp_path):
import lancedb.embeddings.gte
model = get_registry().get("gte-text").create()
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect(tmp_path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
def aws_setup():
try:
import boto3
sts = boto3.client("sts")
sts.get_caller_identity()
return True
except Exception:
return False
@pytest.mark.slow
@pytest.mark.skipif(
not aws_setup(), reason="AWS credentials not set or libraries not installed"
)
def test_bedrock_embedding(tmp_path):
for name in [
"amazon.titan-embed-text-v1",
"cohere.embed-english-v3",
"cohere.embed-multilingual-v3",
]:
model = get_registry().get("bedrock-text").create(max_retries=0, name=name)
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
db = lancedb.connect(tmp_path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
@pytest.mark.slow
@pytest.mark.skipif(
os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
)
def test_openai_embedding(tmp_path):
def _get_table(model):
class TextModel(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
db = lancedb.connect(tmp_path)
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
return tbl
model = get_registry().get("openai").create(max_retries=0)
tbl = _get_table(model)
df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
model = (
get_registry()
.get("openai")
.create(max_retries=0, name="text-embedding-3-large")
)
tbl = _get_table(model)
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
model = (
get_registry()
.get("openai")
.create(max_retries=0, name="text-embedding-3-large", dim=1024)
)
tbl = _get_table(model)
tbl.add(df)
assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
| [
"lancedb.connect",
"lancedb.embeddings.get_registry"
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import lancedb
import pyarrow as pa
from lancedb.remote.client import VectorQuery, VectorQueryResult
class FakeLanceDBClient:
def close(self):
pass
def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
assert table_name == "test"
t = pa.schema([]).empty_table()
return VectorQueryResult(t)
def post(self, path: str):
pass
def mount_retry_adapter_for_table(self, table_name: str):
pass
def test_remote_db():
conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
setattr(conn, "_client", FakeLanceDBClient())
table = conn["test"]
table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
table.search([1.0, 2.0]).to_pandas()
| [
"lancedb.connect",
"lancedb.remote.client.VectorQueryResult"
] | [((1101, 1164), 'lancedb.connect', 'lancedb.connect', (['"""db://client-will-be-injected"""'], {'api_key': '"""fake"""'}), "('db://client-will-be-injected', api_key='fake')\n", (1116, 1164), False, 'import lancedb\n'), ((924, 944), 'lancedb.remote.client.VectorQueryResult', 'VectorQueryResult', (['t'], {}), '(t)\n', (941, 944), False, 'from lancedb.remote.client import VectorQuery, VectorQueryResult\n'), ((881, 894), 'pyarrow.schema', 'pa.schema', (['[]'], {}), '([])\n', (890, 894), True, 'import pyarrow as pa\n'), ((1299, 1311), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (1309, 1311), True, 'import pyarrow as pa\n')] |
# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from typing import List, Union
import lance
import lancedb
import numpy as np
import pyarrow as pa
import pytest
from lancedb.conftest import MockTextEmbeddingFunction
from lancedb.embeddings import (
EmbeddingFunctionConfig,
EmbeddingFunctionRegistry,
with_embeddings,
)
from lancedb.embeddings.base import TextEmbeddingFunction
from lancedb.embeddings.registry import get_registry, register
from lancedb.pydantic import LanceModel, Vector
def mock_embed_func(input_data):
return [np.random.randn(128).tolist() for _ in range(len(input_data))]
def test_with_embeddings():
for wrap_api in [True, False]:
if wrap_api and sys.version_info.minor >= 11:
# ratelimiter package doesn't work on 3.11
continue
data = pa.Table.from_arrays(
[
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
names=["text", "price"],
)
data = with_embeddings(mock_embed_func, data, wrap_api=wrap_api)
assert data.num_columns == 3
assert data.num_rows == 2
assert data.column_names == ["text", "price", "vector"]
assert data.column("text").to_pylist() == ["foo", "bar"]
assert data.column("price").to_pylist() == [10.0, 20.0]
def test_embedding_function(tmp_path):
registry = EmbeddingFunctionRegistry.get_instance()
# let's create a table
table = pa.table(
{
"text": pa.array(["hello world", "goodbye world"]),
"vector": [np.random.randn(10), np.random.randn(10)],
}
)
conf = EmbeddingFunctionConfig(
source_column="text",
vector_column="vector",
function=MockTextEmbeddingFunction(),
)
metadata = registry.get_table_metadata([conf])
table = table.replace_schema_metadata(metadata)
# Write it to disk
lance.write_dataset(table, tmp_path / "test.lance")
# Load this back
ds = lance.dataset(tmp_path / "test.lance")
# can we get the serialized version back out?
configs = registry.parse_functions(ds.schema.metadata)
conf = configs["vector"]
func = conf.function
actual = func.compute_query_embeddings("hello world")
# And we make sure we can call it
expected = func.compute_query_embeddings("hello world")
assert np.allclose(actual, expected)
@pytest.mark.slow
def test_embedding_function_rate_limit(tmp_path):
def _get_schema_from_model(model):
class Schema(LanceModel):
text: str = model.SourceField()
vector: Vector(model.ndims()) = model.VectorField()
return Schema
db = lancedb.connect(tmp_path)
registry = EmbeddingFunctionRegistry.get_instance()
model = registry.get("test-rate-limited").create(max_retries=0)
schema = _get_schema_from_model(model)
table = db.create_table("test", schema=schema, mode="overwrite")
table.add([{"text": "hello world"}])
with pytest.raises(Exception):
table.add([{"text": "hello world"}])
assert len(table) == 1
model = registry.get("test-rate-limited").create()
schema = _get_schema_from_model(model)
table = db.create_table("test", schema=schema, mode="overwrite")
table.add([{"text": "hello world"}])
table.add([{"text": "hello world"}])
assert len(table) == 2
def test_add_optional_vector(tmp_path):
@register("mock-embedding")
class MockEmbeddingFunction(TextEmbeddingFunction):
def ndims(self):
return 128
def generate_embeddings(
self, texts: Union[List[str], np.ndarray]
) -> List[np.array]:
"""
Generate the embeddings for the given texts
"""
return [np.random.randn(self.ndims()).tolist() for _ in range(len(texts))]
registry = get_registry()
model = registry.get("mock-embedding").create()
class LanceSchema(LanceModel):
id: str
vector: Vector(model.ndims()) = model.VectorField(default=None)
text: str = model.SourceField()
db = lancedb.connect(tmp_path)
tbl = db.create_table("optional_vector", schema=LanceSchema)
# add works
expected = LanceSchema(id="id", text="text")
tbl.add([expected])
assert not (np.abs(tbl.to_pandas()["vector"][0]) < 1e-6).all()
| [
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"lancedb.embeddings.with_embeddings",
"lancedb.embeddings.registry.get_registry",
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import importlib.metadata
import platform
import random
import sys
import time
from lancedb.utils import CONFIG
from lancedb.utils.general import TryExcept
from .general import (
PLATFORMS,
get_git_origin_url,
is_git_dir,
is_github_actions_ci,
is_online,
is_pip_package,
is_pytest_running,
threaded_request,
)
class _Events:
"""
A class for collecting anonymous event analytics. Event analytics are enabled when
``diagnostics=True`` in config and disabled when ``diagnostics=False``.
You can enable or disable diagnostics by running ``lancedb diagnostics --enabled``
or ``lancedb diagnostics --disabled``.
Attributes
----------
url : str
The URL to send anonymous events.
rate_limit : float
The rate limit in seconds for sending events.
metadata : dict
A dictionary containing metadata about the environment.
enabled : bool
A flag to enable or disable Events based on certain conditions.
"""
_instance = None
url = "https://app.posthog.com/capture/"
headers = {"Content-Type": "application/json"}
api_key = "phc_oENDjGgHtmIDrV6puUiFem2RB4JA8gGWulfdulmMdZP"
# This api-key is write only and is safe to expose in the codebase.
def __init__(self):
"""
Initializes the Events object with default values for events, rate_limit,
and metadata.
"""
self.events = [] # events list
self.throttled_event_names = ["search_table"]
self.throttled_events = set()
self.max_events = 5 # max events to store in memory
self.rate_limit = 60.0 * 5 # rate limit (seconds)
self.time = 0.0
if is_git_dir():
install = "git"
elif is_pip_package():
install = "pip"
else:
install = "other"
self.metadata = {
"cli": sys.argv[0],
"install": install,
"python": ".".join(platform.python_version_tuple()[:2]),
"version": importlib.metadata.version("lancedb"),
"platforms": PLATFORMS,
"session_id": round(random.random() * 1e15),
# TODO: In future we might be interested in this metric
# 'engagement_time_msec': 1000
}
TESTS_RUNNING = is_pytest_running() or is_github_actions_ci()
ONLINE = is_online()
self.enabled = (
CONFIG["diagnostics"]
and not TESTS_RUNNING
and ONLINE
and (
is_pip_package()
or get_git_origin_url() == "https://github.com/lancedb/lancedb.git"
)
)
def __call__(self, event_name, params={}):
"""
Attempts to add a new event to the events list and send events if the rate
limit is reached.
Args
----
event_name : str
The name of the event to be logged.
params : dict, optional
A dictionary of additional parameters to be logged with the event.
"""
### NOTE: We might need a way to tag a session with a label to check usage
### from a source. Setting label should be exposed to the user.
if not self.enabled:
return
if (
len(self.events) < self.max_events
): # Events list limited to self.max_events (drop any events past this)
params.update(self.metadata)
event = {
"event": event_name,
"properties": params,
"timestamp": datetime.datetime.now(
tz=datetime.timezone.utc
).isoformat(),
"distinct_id": CONFIG["uuid"],
}
if event_name not in self.throttled_event_names:
self.events.append(event)
elif event_name not in self.throttled_events:
self.throttled_events.add(event_name)
self.events.append(event)
# Check rate limit
t = time.time()
if (t - self.time) < self.rate_limit:
return
# Time is over rate limiter, send now
data = {
"api_key": self.api_key,
"distinct_id": CONFIG["uuid"], # posthog needs this to accepts the event
"batch": self.events,
}
# POST equivalent to requests.post(self.url, json=data).
# threaded request is used to avoid blocking, retries are disabled, and
# verbose is disabled to avoid any possible disruption in the console.
threaded_request(
method="post",
url=self.url,
headers=self.headers,
json=data,
retry=0,
verbose=False,
)
# Flush & Reset
self.events = []
self.throttled_events = set()
self.time = t
@TryExcept(verbose=False)
def register_event(name: str, **kwargs):
if _Events._instance is None:
_Events._instance = _Events()
_Events._instance(name, **kwargs)
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# Copyright 2023 LanceDB Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from datetime import timedelta
import lancedb
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from lancedb.pydantic import LanceModel, Vector
def test_basic(tmp_path):
db = lancedb.connect(tmp_path)
assert db.uri == str(tmp_path)
assert db.table_names() == []
table = db.create_table(
"test",
data=[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
)
rs = table.search([100, 100]).limit(1).to_pandas()
assert len(rs) == 1
assert rs["item"].iloc[0] == "bar"
rs = table.search([100, 100]).where("price < 15").limit(2).to_pandas()
assert len(rs) == 1
assert rs["item"].iloc[0] == "foo"
assert db.table_names() == ["test"]
assert "test" in db
assert len(db) == 1
assert db.open_table("test").name == db["test"].name
def test_ingest_pd(tmp_path):
db = lancedb.connect(tmp_path)
assert db.uri == str(tmp_path)
assert db.table_names() == []
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
table = db.create_table("test", data=data)
rs = table.search([100, 100]).limit(1).to_pandas()
assert len(rs) == 1
assert rs["item"].iloc[0] == "bar"
rs = table.search([100, 100]).where("price < 15").limit(2).to_pandas()
assert len(rs) == 1
assert rs["item"].iloc[0] == "foo"
assert db.table_names() == ["test"]
assert "test" in db
assert len(db) == 1
assert db.open_table("test").name == db["test"].name
def test_ingest_iterator(tmp_path):
class PydanticSchema(LanceModel):
vector: Vector(2)
item: str
price: float
arrow_schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), 2)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float32()),
]
)
def make_batches():
for _ in range(5):
yield from [
# pandas
pd.DataFrame(
{
"vector": [[3.1, 4.1], [1, 1]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
),
# pylist
[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
# recordbatch
pa.RecordBatch.from_arrays(
[
pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
),
# pa Table
pa.Table.from_arrays(
[
pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
),
# pydantic list
[
PydanticSchema(vector=[3.1, 4.1], item="foo", price=10.0),
PydanticSchema(vector=[5.9, 26.5], item="bar", price=20.0),
],
# TODO: test pydict separately. it is unique column number and
# name constraints
]
def run_tests(schema):
db = lancedb.connect(tmp_path)
tbl = db.create_table("table2", make_batches(), schema=schema, mode="overwrite")
tbl.to_pandas()
assert tbl.search([3.1, 4.1]).limit(1).to_pandas()["_distance"][0] == 0.0
assert tbl.search([5.9, 26.5]).limit(1).to_pandas()["_distance"][0] == 0.0
tbl_len = len(tbl)
tbl.add(make_batches())
assert tbl_len == 50
assert len(tbl) == tbl_len * 2
assert len(tbl.list_versions()) == 3
db.drop_database()
run_tests(arrow_schema)
run_tests(PydanticSchema)
def test_table_names(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
db.create_table("test2", data=data)
db.create_table("test1", data=data)
db.create_table("test3", data=data)
assert db.table_names() == ["test1", "test2", "test3"]
@pytest.mark.asyncio
async def test_table_names_async(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
db.create_table("test2", data=data)
db.create_table("test1", data=data)
db.create_table("test3", data=data)
db = await lancedb.connect_async(tmp_path)
assert await db.table_names() == ["test1", "test2", "test3"]
assert await db.table_names(limit=1) == ["test1"]
assert await db.table_names(start_after="test1", limit=1) == ["test2"]
assert await db.table_names(start_after="test1") == ["test2", "test3"]
def test_create_mode(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
db.create_table("test", data=data)
with pytest.raises(Exception):
db.create_table("test", data=data)
new_data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["fizz", "buzz"],
"price": [10.0, 20.0],
}
)
tbl = db.create_table("test", data=new_data, mode="overwrite")
assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
def test_create_exist_ok(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
tbl = db.create_table("test", data=data)
with pytest.raises(OSError):
db.create_table("test", data=data)
# open the table but don't add more rows
tbl2 = db.create_table("test", data=data, exist_ok=True)
assert tbl.name == tbl2.name
assert tbl.schema == tbl2.schema
assert len(tbl) == len(tbl2)
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), list_size=2)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float64()),
]
)
tbl3 = db.create_table("test", schema=schema, exist_ok=True)
assert tbl3.schema == schema
bad_schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), list_size=2)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float64()),
pa.field("extra", pa.float32()),
]
)
with pytest.raises(ValueError):
db.create_table("test", schema=bad_schema, exist_ok=True)
@pytest.mark.asyncio
async def test_connect(tmp_path):
db = await lancedb.connect_async(tmp_path)
assert str(db) == f"NativeDatabase(uri={tmp_path}, read_consistency_interval=None)"
db = await lancedb.connect_async(
tmp_path, read_consistency_interval=timedelta(seconds=5)
)
assert str(db) == f"NativeDatabase(uri={tmp_path}, read_consistency_interval=5s)"
@pytest.mark.asyncio
async def test_close(tmp_path):
db = await lancedb.connect_async(tmp_path)
assert db.is_open()
db.close()
assert not db.is_open()
with pytest.raises(RuntimeError, match="is closed"):
await db.table_names()
@pytest.mark.asyncio
async def test_create_mode_async(tmp_path):
db = await lancedb.connect_async(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
await db.create_table("test", data=data)
with pytest.raises(RuntimeError):
await db.create_table("test", data=data)
new_data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["fizz", "buzz"],
"price": [10.0, 20.0],
}
)
_tbl = await db.create_table("test", data=new_data, mode="overwrite")
# MIGRATION: to_pandas() is not available in async
# assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
@pytest.mark.asyncio
async def test_create_exist_ok_async(tmp_path):
db = await lancedb.connect_async(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
tbl = await db.create_table("test", data=data)
with pytest.raises(RuntimeError):
await db.create_table("test", data=data)
# open the table but don't add more rows
tbl2 = await db.create_table("test", data=data, exist_ok=True)
assert tbl.name == tbl2.name
assert await tbl.schema() == await tbl2.schema()
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), list_size=2)),
pa.field("item", pa.utf8()),
pa.field("price", pa.float64()),
]
)
tbl3 = await db.create_table("test", schema=schema, exist_ok=True)
assert await tbl3.schema() == schema
# Migration: When creating a table, but the table already exists, but
# the schema is different, it should raise an error.
# bad_schema = pa.schema(
# [
# pa.field("vector", pa.list_(pa.float32(), list_size=2)),
# pa.field("item", pa.utf8()),
# pa.field("price", pa.float64()),
# pa.field("extra", pa.float32()),
# ]
# )
# with pytest.raises(ValueError):
# await db.create_table("test", schema=bad_schema, exist_ok=True)
@pytest.mark.asyncio
async def test_open_table(tmp_path):
db = await lancedb.connect_async(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
await db.create_table("test", data=data)
tbl = await db.open_table("test")
assert tbl.name == "test"
assert (
re.search(
r"NativeTable\(test, uri=.*test\.lance, read_consistency_interval=None\)",
str(tbl),
)
is not None
)
assert await tbl.schema() == pa.schema(
{
"vector": pa.list_(pa.float32(), list_size=2),
"item": pa.utf8(),
"price": pa.float64(),
}
)
with pytest.raises(ValueError, match="was not found"):
await db.open_table("does_not_exist")
def test_delete_table(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
db.create_table("test", data=data)
with pytest.raises(Exception):
db.create_table("test", data=data)
assert db.table_names() == ["test"]
db.drop_table("test")
assert db.table_names() == []
db.create_table("test", data=data)
assert db.table_names() == ["test"]
# dropping a table that does not exist should pass
# if ignore_missing=True
db.drop_table("does_not_exist", ignore_missing=True)
def test_drop_database(tmp_path):
db = lancedb.connect(tmp_path)
data = pd.DataFrame(
{
"vector": [[3.1, 4.1], [5.9, 26.5]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}
)
new_data = pd.DataFrame(
{
"vector": [[5.1, 4.1], [5.9, 10.5]],
"item": ["kiwi", "avocado"],
"price": [12.0, 17.0],
}
)
db.create_table("test", data=data)
with pytest.raises(Exception):
db.create_table("test", data=data)
assert db.table_names() == ["test"]
db.create_table("new_test", data=new_data)
db.drop_database()
assert db.table_names() == []
# it should pass when no tables are present
db.create_table("test", data=new_data)
db.drop_table("test")
assert db.table_names() == []
db.drop_database()
assert db.table_names() == []
# creating an empty database with schema
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
db.create_table("empty_table", schema=schema)
# dropping a empty database should pass
db.drop_database()
assert db.table_names() == []
def test_empty_or_nonexistent_table(tmp_path):
db = lancedb.connect(tmp_path)
with pytest.raises(Exception):
db.create_table("test_with_no_data")
with pytest.raises(Exception):
db.open_table("does_not_exist")
schema = pa.schema([pa.field("a", pa.int64(), nullable=False)])
test = db.create_table("test", schema=schema)
class TestModel(LanceModel):
a: int
test2 = db.create_table("test2", schema=TestModel)
assert test.schema == test2.schema
def test_replace_index(tmp_path):
db = lancedb.connect(uri=tmp_path)
table = db.create_table(
"test",
[
{"vector": np.random.rand(128), "item": "foo", "price": float(i)}
for i in range(1000)
],
)
table.create_index(
num_partitions=2,
num_sub_vectors=4,
)
with pytest.raises(Exception):
table.create_index(
num_partitions=2,
num_sub_vectors=4,
replace=False,
)
table.create_index(
num_partitions=2,
num_sub_vectors=4,
replace=True,
index_cache_size=10,
)
def test_prefilter_with_index(tmp_path):
db = lancedb.connect(uri=tmp_path)
data = [
{"vector": np.random.rand(128), "item": "foo", "price": float(i)}
for i in range(1000)
]
sample_key = data[100]["vector"]
table = db.create_table(
"test",
data,
)
table.create_index(
num_partitions=2,
num_sub_vectors=4,
)
table = (
table.search(sample_key)
.where("price == 500", prefilter=True)
.limit(5)
.to_arrow()
)
assert table.num_rows == 1
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# Ultralytics YOLO 🚀, AGPL-3.0 license
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import cv2
import numpy as np
import torch
from PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment import Format
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
from ultralytics.models.yolo.model import YOLO
from ultralytics.utils import LOGGER, IterableSimpleNamespace, checks, USER_CONFIG_DIR
from .utils import get_sim_index_schema, get_table_schema, plot_query_result, prompt_sql_query, sanitize_batch
class ExplorerDataset(YOLODataset):
def __init__(self, *args, data: dict = None, **kwargs) -> None:
super().__init__(*args, data=data, **kwargs)
def load_image(self, i: int) -> Union[Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]], Tuple[None, None, None]]:
"""Loads 1 image from dataset index 'i' without any resize ops."""
im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
if im is None: # not cached in RAM
if fn.exists(): # load npy
im = np.load(fn)
else: # read image
im = cv2.imread(f) # BGR
if im is None:
raise FileNotFoundError(f"Image Not Found {f}")
h0, w0 = im.shape[:2] # orig hw
return im, (h0, w0), im.shape[:2]
return self.ims[i], self.im_hw0[i], self.im_hw[i]
def build_transforms(self, hyp: IterableSimpleNamespace = None):
"""Creates transforms for dataset images without resizing."""
return Format(
bbox_format="xyxy",
normalize=False,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
batch_idx=True,
mask_ratio=hyp.mask_ratio,
mask_overlap=hyp.overlap_mask,
)
class Explorer:
def __init__(
self,
data: Union[str, Path] = "coco128.yaml",
model: str = "yolov8n.pt",
uri: str = USER_CONFIG_DIR / "explorer",
) -> None:
# Note duckdb==0.10.0 bug https://github.com/ultralytics/ultralytics/pull/8181
checks.check_requirements(["lancedb>=0.4.3", "duckdb<=0.9.2"])
import lancedb
self.connection = lancedb.connect(uri)
self.table_name = Path(data).name.lower() + "_" + model.lower()
self.sim_idx_base_name = (
f"{self.table_name}_sim_idx".lower()
) # Use this name and append thres and top_k to reuse the table
self.model = YOLO(model)
self.data = data # None
self.choice_set = None
self.table = None
self.progress = 0
def create_embeddings_table(self, force: bool = False, split: str = "train") -> None:
"""
Create LanceDB table containing the embeddings of the images in the dataset. The table will be reused if it
already exists. Pass force=True to overwrite the existing table.
Args:
force (bool): Whether to overwrite the existing table or not. Defaults to False.
split (str): Split of the dataset to use. Defaults to 'train'.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
```
"""
if self.table is not None and not force:
LOGGER.info("Table already exists. Reusing it. Pass force=True to overwrite it.")
return
if self.table_name in self.connection.table_names() and not force:
LOGGER.info(f"Table {self.table_name} already exists. Reusing it. Pass force=True to overwrite it.")
self.table = self.connection.open_table(self.table_name)
self.progress = 1
return
if self.data is None:
raise ValueError("Data must be provided to create embeddings table")
data_info = check_det_dataset(self.data)
if split not in data_info:
raise ValueError(
f"Split {split} is not found in the dataset. Available keys in the dataset are {list(data_info.keys())}"
)
choice_set = data_info[split]
choice_set = choice_set if isinstance(choice_set, list) else [choice_set]
self.choice_set = choice_set
dataset = ExplorerDataset(img_path=choice_set, data=data_info, augment=False, cache=False, task=self.model.task)
# Create the table schema
batch = dataset[0]
vector_size = self.model.embed(batch["im_file"], verbose=False)[0].shape[0]
table = self.connection.create_table(self.table_name, schema=get_table_schema(vector_size), mode="overwrite")
table.add(
self._yield_batches(
dataset,
data_info,
self.model,
exclude_keys=["img", "ratio_pad", "resized_shape", "ori_shape", "batch_idx"],
)
)
self.table = table
def _yield_batches(self, dataset: ExplorerDataset, data_info: dict, model: YOLO, exclude_keys: List[str]):
"""Generates batches of data for embedding, excluding specified keys."""
for i in tqdm(range(len(dataset))):
self.progress = float(i + 1) / len(dataset)
batch = dataset[i]
for k in exclude_keys:
batch.pop(k, None)
batch = sanitize_batch(batch, data_info)
batch["vector"] = model.embed(batch["im_file"], verbose=False)[0].detach().tolist()
yield [batch]
def query(
self, imgs: Union[str, np.ndarray, List[str], List[np.ndarray]] = None, limit: int = 25
) -> Any: # pyarrow.Table
"""
Query the table for similar images. Accepts a single image or a list of images.
Args:
imgs (str or list): Path to the image or a list of paths to the images.
limit (int): Number of results to return.
Returns:
(pyarrow.Table): An arrow table containing the results. Supports converting to:
- pandas dataframe: `result.to_pandas()`
- dict of lists: `result.to_pydict()`
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.query(img='https://ultralytics.com/images/zidane.jpg')
```
"""
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
if isinstance(imgs, str):
imgs = [imgs]
assert isinstance(imgs, list), f"img must be a string or a list of strings. Got {type(imgs)}"
embeds = self.model.embed(imgs)
# Get avg if multiple images are passed (len > 1)
embeds = torch.mean(torch.stack(embeds), 0).cpu().numpy() if len(embeds) > 1 else embeds[0].cpu().numpy()
return self.table.search(embeds).limit(limit).to_arrow()
def sql_query(
self, query: str, return_type: str = "pandas"
) -> Union[DataFrame, Any, None]: # pandas.dataframe or pyarrow.Table
"""
Run a SQL-Like query on the table. Utilizes LanceDB predicate pushdown.
Args:
query (str): SQL query to run.
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
(pyarrow.Table): An arrow table containing the results.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
query = "SELECT * FROM 'table' WHERE labels LIKE '%person%'"
result = exp.sql_query(query)
```
"""
assert return_type in {
"pandas",
"arrow",
}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
import duckdb
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
# Note: using filter pushdown would be a better long term solution. Temporarily using duckdb for this.
table = self.table.to_arrow() # noqa NOTE: Don't comment this. This line is used by DuckDB
if not query.startswith("SELECT") and not query.startswith("WHERE"):
raise ValueError(
f"Query must start with SELECT or WHERE. You can either pass the entire query or just the WHERE clause. found {query}"
)
if query.startswith("WHERE"):
query = f"SELECT * FROM 'table' {query}"
LOGGER.info(f"Running query: {query}")
rs = duckdb.sql(query)
if return_type == "arrow":
return rs.arrow()
elif return_type == "pandas":
return rs.df()
def plot_sql_query(self, query: str, labels: bool = True) -> Image.Image:
"""
Plot the results of a SQL-Like query on the table.
Args:
query (str): SQL query to run.
labels (bool): Whether to plot the labels or not.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
query = "SELECT * FROM 'table' WHERE labels LIKE '%person%'"
result = exp.plot_sql_query(query)
```
"""
result = self.sql_query(query, return_type="arrow")
if len(result) == 0:
LOGGER.info("No results found.")
return None
img = plot_query_result(result, plot_labels=labels)
return Image.fromarray(img)
def get_similar(
self,
img: Union[str, np.ndarray, List[str], List[np.ndarray]] = None,
idx: Union[int, List[int]] = None,
limit: int = 25,
return_type: str = "pandas",
) -> Union[DataFrame, Any]: # pandas.dataframe or pyarrow.Table
"""
Query the table for similar images. Accepts a single image or a list of images.
Args:
img (str or list): Path to the image or a list of paths to the images.
idx (int or list): Index of the image in the table or a list of indexes.
limit (int): Number of results to return. Defaults to 25.
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
(pandas.DataFrame): A dataframe containing the results.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg')
```
"""
assert return_type in {
"pandas",
"arrow",
}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
img = self._check_imgs_or_idxs(img, idx)
similar = self.query(img, limit=limit)
if return_type == "arrow":
return similar
elif return_type == "pandas":
return similar.to_pandas()
def plot_similar(
self,
img: Union[str, np.ndarray, List[str], List[np.ndarray]] = None,
idx: Union[int, List[int]] = None,
limit: int = 25,
labels: bool = True,
) -> Image.Image:
"""
Plot the similar images. Accepts images or indexes.
Args:
img (str or list): Path to the image or a list of paths to the images.
idx (int or list): Index of the image in the table or a list of indexes.
labels (bool): Whether to plot the labels or not.
limit (int): Number of results to return. Defaults to 25.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.plot_similar(img='https://ultralytics.com/images/zidane.jpg')
```
"""
similar = self.get_similar(img, idx, limit, return_type="arrow")
if len(similar) == 0:
LOGGER.info("No results found.")
return None
img = plot_query_result(similar, plot_labels=labels)
return Image.fromarray(img)
def similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> DataFrame:
"""
Calculate the similarity index of all the images in the table. Here, the index will contain the data points that
are max_dist or closer to the image in the embedding space at a given index.
Args:
max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running
vector search. Defaults: None.
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
(pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image, and columns
include indices of similar images and their respective distances.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
sim_idx = exp.similarity_index()
```
"""
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
sim_idx_table_name = f"{self.sim_idx_base_name}_thres_{max_dist}_top_{top_k}".lower()
if sim_idx_table_name in self.connection.table_names() and not force:
LOGGER.info("Similarity matrix already exists. Reusing it. Pass force=True to overwrite it.")
return self.connection.open_table(sim_idx_table_name).to_pandas()
if top_k and not (1.0 >= top_k >= 0.0):
raise ValueError(f"top_k must be between 0.0 and 1.0. Got {top_k}")
if max_dist < 0.0:
raise ValueError(f"max_dist must be greater than 0. Got {max_dist}")
top_k = int(top_k * len(self.table)) if top_k else len(self.table)
top_k = max(top_k, 1)
features = self.table.to_lance().to_table(columns=["vector", "im_file"]).to_pydict()
im_files = features["im_file"]
embeddings = features["vector"]
sim_table = self.connection.create_table(sim_idx_table_name, schema=get_sim_index_schema(), mode="overwrite")
def _yield_sim_idx():
"""Generates a dataframe with similarity indices and distances for images."""
for i in tqdm(range(len(embeddings))):
sim_idx = self.table.search(embeddings[i]).limit(top_k).to_pandas().query(f"_distance <= {max_dist}")
yield [
{
"idx": i,
"im_file": im_files[i],
"count": len(sim_idx),
"sim_im_files": sim_idx["im_file"].tolist(),
}
]
sim_table.add(_yield_sim_idx())
self.sim_index = sim_table
return sim_table.to_pandas()
def plot_similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> Image:
"""
Plot the similarity index of all the images in the table. Here, the index will contain the data points that are
max_dist or closer to the image in the embedding space at a given index.
Args:
max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
top_k (float): Percentage of closest data points to consider when counting. Used to apply limit when
running vector search. Defaults to 0.01.
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similarity_idx_plot = exp.plot_similarity_index()
similarity_idx_plot.show() # view image preview
similarity_idx_plot.save('path/to/save/similarity_index_plot.png') # save contents to file
```
"""
sim_idx = self.similarity_index(max_dist=max_dist, top_k=top_k, force=force)
sim_count = sim_idx["count"].tolist()
sim_count = np.array(sim_count)
indices = np.arange(len(sim_count))
# Create the bar plot
plt.bar(indices, sim_count)
# Customize the plot (optional)
plt.xlabel("data idx")
plt.ylabel("Count")
plt.title("Similarity Count")
buffer = BytesIO()
plt.savefig(buffer, format="png")
buffer.seek(0)
# Use Pillow to open the image from the buffer
return Image.fromarray(np.array(Image.open(buffer)))
def _check_imgs_or_idxs(
self, img: Union[str, np.ndarray, List[str], List[np.ndarray], None], idx: Union[None, int, List[int]]
) -> List[np.ndarray]:
if img is None and idx is None:
raise ValueError("Either img or idx must be provided.")
if img is not None and idx is not None:
raise ValueError("Only one of img or idx must be provided.")
if idx is not None:
idx = idx if isinstance(idx, list) else [idx]
img = self.table.to_lance().take(idx, columns=["im_file"]).to_pydict()["im_file"]
return img if isinstance(img, list) else [img]
def ask_ai(self, query):
"""
Ask AI a question.
Args:
query (str): Question to ask.
Returns:
(pandas.DataFrame): A dataframe containing filtered results to the SQL query.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
answer = exp.ask_ai('Show images with 1 person and 2 dogs')
```
"""
result = prompt_sql_query(query)
try:
df = self.sql_query(result)
except Exception as e:
LOGGER.error("AI generated query is not valid. Please try again with a different prompt")
LOGGER.error(e)
return None
return df
def visualize(self, result):
"""
Visualize the results of a query. TODO.
Args:
result (pyarrow.Table): Table containing the results of a query.
"""
pass
def generate_report(self, result):
"""
Generate a report of the dataset.
TODO
"""
pass
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import logging
import lancedb
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import CTransformers
from langchain.prompts import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import LanceDB
# Configure basic logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class ChatWithVideo:
def __init__(self, input_file, llm_model_name, llm_model_file, llm_model_type, embedding_model_name):
self.input_file = input_file
self.llm_model_name = llm_model_name
self.llm_model_file = llm_model_file
self.llm_model_type = llm_model_type
self.embedding_model_name = embedding_model_name
def load_llm_model(self):
try:
logger.info(f"Starting to download the {self.llm_model_name} model...")
llm_model = CTransformers(
model=self.llm_model_name, model_file=self.llm_model_file, model_type=self.llm_model_type)
logger.info(f"{self.llm_model_name} model successfully loaded.")
return llm_model
except Exception as e:
logger.error(f"Error loading the {self.llm_model_name} model: {e}")
return None
def load_text_file(self):
try:
logger.info(f"Loading transcript file from {self.input_file}...")
loader = TextLoader(self.input_file)
docs = loader.load()
logger.info("Transcript file successfully loaded.")
return docs
except Exception as e:
logger.error(f"Error loading text file: {e}")
return None
@staticmethod
def setup_database(embeddings):
try:
logger.info("Setting up the database...")
db = lancedb.connect('/tmp/lancedb')
table = db.create_table(
"xxxxxxx",
data=[{
"vector": embeddings.embed_query("Hello World"),
"text": "Hellos World",
"id": "1"
}],
mode="overwrite")
logger.info("Database setup complete.")
return table
except Exception as e:
logger.error(f"Error setting up the database: {e}")
raise e # Raising the exception for further debugging
@staticmethod
def prepare_embeddings(model_name):
try:
logger.info(f"Preparing embeddings with model: {model_name}...")
embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs={'device': 'cpu'})
logger.info("Embeddings prepared successfully.")
return embeddings
except Exception as e:
logger.error(f"Error preparing embeddings: {e}")
return None
@staticmethod
def prepare_documents(docs):
if not docs:
logger.info("No documents provided for preparation.")
return None
try:
logger.info("Preparing documents...")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200, chunk_overlap=50)
documents = text_splitter.split_documents(docs)
logger.info("Documents prepared successfully.")
return documents
except Exception as e:
logger.error(f"Error preparing documents: {e}")
return None
def run_query(self, query):
if not query:
logger.info("No query provided.")
return "No query provided."
logger.info(f"Running query: {query}")
docs = self.load_text_file()
if not docs:
return "Failed to load documents."
documents = self.prepare_documents(docs)
if not documents:
return "Failed to prepare documents."
embeddings = self.prepare_embeddings(self.embedding_model_name)
if not embeddings:
return "Failed to prepare embeddings."
db = self.setup_database(embeddings)
if not db:
return "Failed to setup database."
try:
docsearch = LanceDB.from_documents(documents, embeddings, connection=db)
llm = self.load_llm_model()
if not llm:
return "Failed to load LLM model."
template = """Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
Use three sentences maximum and keep the answer as concise as possible.
Always say "thanks for asking!" at the end of the answer.
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"], template=template)
logger.info("Prompt loaded")
qa = RetrievalQA.from_chain_type(
llm,
chain_type='stuff',
retriever=docsearch.as_retriever(),
chain_type_kwargs={"prompt": QA_CHAIN_PROMPT})
logger.info("Query processed successfully.")
result = qa.run(query)
logger.info(f"Result of the query: {result}")
return result
except Exception as e:
logger.error(f"Error running query: {e}")
return f"Error: {e}"
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from collections import OrderedDict
from typing import List, Optional
import lancedb
import pandas as pd
import pyarrow as pa
from lance.vector import vec_to_table
import duckdb
import json
from mindsdb.integrations.libs.const import HANDLER_CONNECTION_ARG_TYPE as ARG_TYPE
from mindsdb.integrations.libs.response import RESPONSE_TYPE
from mindsdb.integrations.libs.response import HandlerResponse
from mindsdb.integrations.libs.response import HandlerResponse as Response
from mindsdb.integrations.libs.response import HandlerStatusResponse as StatusResponse
from mindsdb.integrations.libs.vectordatabase_handler import (
FilterCondition,
FilterOperator,
TableField,
VectorStoreHandler,
)
from mindsdb.utilities import log
logger = log.getLogger(__name__)
class LanceDBHandler(VectorStoreHandler):
"""This handler handles connection and execution of the LanceDB statements."""
name = "lancedb"
def __init__(self, name: str, **kwargs):
super().__init__(name, **kwargs)
self._connection_data = kwargs.get("connection_data")
self._client_config = {
"uri": self._connection_data.get("persist_directory"),
"api_key": self._connection_data.get("api_key", None),
"region": self._connection_data.get("region"),
"host_override": self._connection_data.get("host_override"),
}
# uri is required either for LanceDB Cloud or local
if not self._client_config["uri"]:
raise Exception(
"persist_directory is required for LanceDB connection!"
)
# uri, api_key and region is required either for LanceDB Cloud
elif self._client_config["uri"] and self._client_config["api_key"] and not self._client_config["region"]:
raise Exception(
"region is required for LanceDB Cloud connection!"
)
self._client = None
self.is_connected = False
self.connect()
def _get_client(self):
client_config = self._client_config
if client_config is None:
raise Exception("Client config is not set!")
return lancedb.connect(**client_config)
def __del__(self):
if self.is_connected is True:
self.disconnect()
def connect(self):
"""Connect to a LanceDB database."""
if self.is_connected is True:
return
try:
self._client = self._get_client()
self.is_connected = True
except Exception as e:
logger.error(f"Error connecting to LanceDB client, {e}!")
self.is_connected = False
def disconnect(self):
"""Close the database connection."""
if self.is_connected is False:
return
self._client = None
self.is_connected = False
def check_connection(self):
"""Check the connection to the LanceDB database."""
response_code = StatusResponse(False)
need_to_close = self.is_connected is False
try:
self._client.table_names()
response_code.success = True
except Exception as e:
logger.error(f"Error connecting to LanceDB , {e}!")
response_code.error_message = str(e)
finally:
if response_code.success is True and need_to_close:
self.disconnect()
if response_code.success is False and self.is_connected is True:
self.is_connected = False
return response_code
def _get_lancedb_operator(self, operator: FilterOperator) -> str:
# The in values are not returned with () and only one element is returned. Bug
mapping = {
FilterOperator.EQUAL: "=",
FilterOperator.NOT_EQUAL: "!=",
FilterOperator.LESS_THAN: "<",
FilterOperator.LESS_THAN_OR_EQUAL: "<=",
FilterOperator.GREATER_THAN: ">",
FilterOperator.GREATER_THAN_OR_EQUAL: ">=",
# FilterOperator.IN: "in",
# FilterOperator.NOT_IN: "not in",
FilterOperator.LIKE: "like",
FilterOperator.NOT_LIKE: "not like",
FilterOperator.IS_NULL: "is null",
FilterOperator.IS_NOT_NULL: "is not null",
}
if operator not in mapping:
raise Exception(f"Operator {operator} is not supported by LanceDB!")
return mapping[operator]
def _translate_condition(
self, conditions: List[FilterCondition]
) -> Optional[dict]:
"""
Translate a list of FilterCondition objects to string that can be used by LanceDB.
E.g.,
[
FilterCondition(
column="content",
op=FilterOperator.NOT_EQUAL,
value="a",
),
FilterCondition(
column="id",
op=FilterOperator.EQUAL,
value="6",
)
]
-->
"content != 'a' and id = '6'"
"""
# we ignore all non-metadata conditions
if not conditions:
return
filtered_conditions = [
condition
for condition in conditions
if condition.column.startswith(TableField.ID.value) or condition.column.startswith(TableField.CONTENT.value)
]
if len(filtered_conditions) == 0:
return None
# generate the LanceDB filter string
lancedb_conditions = []
for condition in filtered_conditions:
if isinstance(condition.value, str):
condition.value = f"'{condition.value}'"
condition_key = condition.column.split(".")[-1]
lancedb_conditions.append(
' '.join([condition_key, self._get_lancedb_operator(condition.op), str(condition.value)])
)
# Combine all conditions into a single string and return
return " and ".join(lancedb_conditions) if lancedb_conditions else None
def select(
self,
table_name: str,
columns: List[str] = None,
conditions: List[FilterCondition] = None,
offset: int = None,
limit: int = None,
) -> HandlerResponse:
try:
# Load collection table
collection = self._client.open_table(table_name)
except Exception as e:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Error loading collection {table_name}: {e}",
)
filters = self._translate_condition(conditions)
# check if embedding vector filter is present
vector_filter = (
[]
if conditions is None
else [
condition
for condition in conditions
if condition.column == TableField.SEARCH_VECTOR.value
]
)
if len(vector_filter) > 0:
vector_filter = vector_filter[0]
else:
vector_filter = None
if vector_filter is not None:
vec = json.loads(vector_filter.value) if isinstance(vector_filter.value, str) else vector_filter.value
result = collection.search(vec).select(columns).to_pandas()
result = result.rename(columns={"_distance": TableField.DISTANCE.value})
else:
result = self._client.open_table(table_name).to_pandas()
new_columns = columns + [TableField.DISTANCE.value] if TableField.DISTANCE.value in result.columns else columns
col_str = ', '.join([col for col in new_columns if col in (TableField.ID.value, TableField.CONTENT.value, TableField.METADATA.value, TableField.EMBEDDINGS.value, TableField.DISTANCE.value)])
where_str = f'where {filters}' if filters else ''
# implementing limit and offset. Not supported natively in lancedb
if limit and offset:
sql = f"""select {col_str} from result {where_str} limit {limit} offset {offset}"""
elif limit and not offset:
sql = f"""select {col_str} from result {where_str} limit {limit}"""
elif offset and not limit:
sql = f"""select {col_str} from result {where_str} offset {offset}"""
else:
sql = f"""select {col_str} from result {where_str}"""
data_df = duckdb.query(sql).to_df()
return Response(resp_type=RESPONSE_TYPE.TABLE, data_frame=data_df)
def insert(
self, table_name: str, data: pd.DataFrame, columns: List[str] = None
) -> HandlerResponse:
"""
Insert data into the LanceDB database.
In case of create table statements the there is a mismatch between the column types of the `data` pandas dataframe filled with data
and the empty base table column types which raises a pa.lib.ArrowNotImplementedError, in that case the base table is deleted (doesn't matter as it is empty)
and recreated with the right datatypes
"""
try:
collection = self._client.open_table(table_name)
df = data[[TableField.ID.value, TableField.CONTENT.value, TableField.METADATA.value, TableField.EMBEDDINGS.value]]
pa_data = pa.Table.from_pandas(df, preserve_index=False)
vec_data = vec_to_table(df[TableField.EMBEDDINGS.value].values.tolist())
new_pa_data = pa_data.append_column("vector", vec_data["vector"])
collection.add(new_pa_data)
except pa.lib.ArrowNotImplementedError:
collection_df = collection.to_pandas()
column_dtypes = collection_df.dtypes
df = df.astype(column_dtypes)
new_df = pd.concat([collection_df, df])
new_df['id'] = new_df['id'].apply(str)
pa_data = pa.Table.from_pandas(new_df, preserve_index=False)
vec_data = vec_to_table(df[TableField.EMBEDDINGS.value].values.tolist())
new_pa_data = pa_data.append_column("vector", vec_data["vector"])
self.drop_table(table_name)
self._client.create_table(table_name, new_pa_data)
except Exception as e:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Unable to insert data into collection `{table_name}`: {e}"
)
return Response(resp_type=RESPONSE_TYPE.OK)
def update(
self, table_name: str, data: pd.DataFrame, columns: List[str] = None
) -> HandlerResponse:
"""
Update data in the LanceDB database.
TODO: not implemented yet
"""
return super().update(table_name, data, columns)
def delete(
self, table_name: str, conditions: List[FilterCondition] = None
) -> HandlerResponse:
try:
filters = self._translate_condition(conditions)
if filters is None:
raise Exception("Delete query must have at least one condition!")
collection = self._client.open_table(table_name)
collection.delete(filters)
except Exception as e:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Error deleting from collection `{table_name}`: {e}",
)
return Response(resp_type=RESPONSE_TYPE.OK)
def create_table(self, table_name: str, if_not_exists=True) -> HandlerResponse:
"""
Create a collection with the given name in the LanceDB database.
"""
try:
data = {
TableField.ID.value: str,
TableField.CONTENT.value: str,
TableField.METADATA.value: object,
TableField.EMBEDDINGS.value: object,
}
df = pd.DataFrame(columns=data.keys()).astype(data)
self._client.create_table(table_name, df)
except Exception as e:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Unable to create collection `{table_name}`: {e}"
)
return Response(resp_type=RESPONSE_TYPE.OK)
def drop_table(self, table_name: str, if_exists=True) -> HandlerResponse:
"""
Delete a collection from the LanceDB database.
"""
try:
self._client.drop_table(table_name)
except ValueError:
if if_exists:
return Response(resp_type=RESPONSE_TYPE.OK)
else:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Table {table_name} does not exist!",
)
return Response(resp_type=RESPONSE_TYPE.OK)
def get_tables(self) -> HandlerResponse:
"""
Get the list of collections in the LanceDB database.
"""
collections = self._client.table_names()
collections_name = pd.DataFrame(
columns=["table_name"],
data=collections,
)
return Response(resp_type=RESPONSE_TYPE.TABLE, data_frame=collections_name)
def get_columns(self, table_name: str) -> HandlerResponse:
# check if collection exists
try:
df = self._client.open_table(table_name).to_pandas()
column_df = pd.DataFrame(df.dtypes).reset_index()
column_df.columns = ['column_name', 'data_type']
except ValueError:
return Response(
resp_type=RESPONSE_TYPE.ERROR,
error_message=f"Table {table_name} does not exist!",
)
return Response(resp_type=RESPONSE_TYPE.TABLE, data_frame=column_df)
connection_args = OrderedDict(
persist_directory={
"type": ARG_TYPE.STR,
"description": "The uri of the database.",
"required": True,
},
api_key={
"type": ARG_TYPE.STR,
"description": "If presented, connect to LanceDB cloud. Otherwise, connect to a database on file system or cloud storage.",
"required": False,
},
region={
"type": ARG_TYPE.STR,
"description": "The region to use for LanceDB Cloud.",
"required": False,
},
host_override={
"type": ARG_TYPE.STR,
"description": "The override url for LanceDB Cloud.",
"required": False,
},
)
connection_args_example = OrderedDict(
persist_directory="~/lancedb",
api_key=None,
region="us-west-2",
host_override=None,
)
| [
"lancedb.connect"
] | [((752, 775), 'mindsdb.utilities.log.getLogger', 'log.getLogger', (['__name__'], {}), '(__name__)\n', (765, 775), False, 'from mindsdb.utilities import log\n'), ((13628, 14162), 'collections.OrderedDict', 'OrderedDict', ([], {'persist_directory': "{'type': ARG_TYPE.STR, 'description': 'The uri of the database.',\n 'required': True}", 'api_key': "{'type': ARG_TYPE.STR, 'description':\n 'If presented, connect to LanceDB cloud. Otherwise, connect to a database on file system or cloud storage.'\n , 'required': False}", 'region': "{'type': ARG_TYPE.STR, 'description':\n 'The region to use for LanceDB Cloud.', 'required': False}", 'host_override': "{'type': ARG_TYPE.STR, 'description': 'The override url for LanceDB Cloud.',\n 'required': False}"}), "(persist_directory={'type': ARG_TYPE.STR, 'description':\n 'The uri of the database.', 'required': True}, api_key={'type':\n ARG_TYPE.STR, 'description':\n 'If presented, connect to LanceDB cloud. 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"""Vector store."""
from langchain.vectorstores import LanceDB
import lancedb
class VectorStoreLanceDB:
"""Vector store lance DB."""
def __init__(self, db_path, table_name, mode, embedding_model):
self.db = lancedb.connect(db_path)
self.embedding_model = embedding_model
print(db_path)
hello_world_vector = self.embedding_model.embed_text("Hello world")
if mode == "read":
table = self.db.open_table(table_name)
elif mode == "overwrite":
# pylint: disable=unexpected-keyword-arg
table = self.db.create_table(name=table_name,
data=[
{
"vector": hello_world_vector,
"text": "Hello World",
"id": "1"
}
],
mode="overwrite")
else:
table = self.db.create_table(name=table_name,
data=[
{
"vector": hello_world_vector,
"text": "Hello World",
"id": "1"
}
])
# pylint: disable=not-callable
self.vec_db = LanceDB(
connection=table, embedding=self.embedding_model.embedding_model)
def drop_table(self, table_name):
"""Drop table."""
self.db.drop_table(table_name)
def add_text(self, text):
"""Add text."""
self.vec_db.add_texts([text])
def add_text_list(self, text_list):
"""Add text list."""
self.vec_db.add_texts(text_list)
def add_document(self, doc):
"""Add document."""
self.vec_db.add_documents([doc])
def add_document_list(self, doc_list):
"""Add document list."""
self.vec_db.add_documents(doc_list)
def similarity_search(self, query, k=3):
"""Similarity search."""
docs = self.vec_db.similarity_search(query, k=k)
text_list = [doc.page_content for doc in docs]
return text_list
| [
"lancedb.connect"
] | [((226, 250), 'lancedb.connect', 'lancedb.connect', (['db_path'], {}), '(db_path)\n', (241, 250), False, 'import lancedb\n'), ((1608, 1681), 'langchain.vectorstores.LanceDB', 'LanceDB', ([], {'connection': 'table', 'embedding': 'self.embedding_model.embedding_model'}), '(connection=table, embedding=self.embedding_model.embedding_model)\n', (1615, 1681), False, 'from langchain.vectorstores import LanceDB\n')] |
import os
from dotenv import load_dotenv
import streamlit as st
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.agents.agent_toolkits import create_retriever_tool
from langchain.agents.agent_toolkits import create_conversational_retrieval_agent
from langchain.callbacks import StreamlitCallbackHandler
from langchain.tools import BaseTool, Tool, tool
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import ChatMessage
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain import PromptTemplate, LLMChain
from langchain.vectorstores import LanceDB
import lancedb
import pandas as pd
from langchain.chains import RetrievalQA
st.set_page_config(page_title="GlobeBotter", page_icon="🎬")
st.header('🎬 Welcome to MovieHarbor, your favourite movie recommender')
load_dotenv()
#os.environ["HUGGINGFACEHUB_API_TOKEN"]
openai_api_key = os.environ['OPENAI_API_KEY']
embeddings = OpenAIEmbeddings()
uri = "data/sample-lancedb"
db = lancedb.connect(uri)
table = db.open_table('movies')
docsearch = LanceDB(connection = table, embedding = embeddings)
# Import the movie dataset
md = pd.read_pickle('movies.pkl')
# Create a sidebar for user input
st.sidebar.title("Movie Recommendation System")
st.sidebar.markdown("Please enter your details and preferences below:")
# Ask the user for age, gender and favourite movie genre
age = st.sidebar.slider("What is your age?", 1, 100, 25)
gender = st.sidebar.radio("What is your gender?", ("Male", "Female", "Other"))
genre = st.sidebar.selectbox("What is your favourite movie genre?", md.explode('genres')["genres"].unique())
# Filter the movies based on the user input
df_filtered = md[md['genres'].apply(lambda x: genre in x)]
template_prefix = """You are a movie recommender system that help users to find movies that match their preferences.
Use the following pieces of context to answer the question at the end.
If you don't know the answer, just say that you don't know, don't try to make up an answer.
{context}"""
user_info = """This is what we know about the user, and you can use this information to better tune your research:
Age: {age}
Gender: {gender}"""
template_suffix= """Question: {question}
Your response:"""
user_info = user_info.format(age = age, gender = gender)
COMBINED_PROMPT = template_prefix +'\n'+ user_info +'\n'+ template_suffix
print(COMBINED_PROMPT)
#setting up the chain
qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={'data': df_filtered}), return_source_documents=True)
query = st.text_input('Enter your question:', placeholder = 'What action movies do you suggest?')
if query:
result = qa({"query": query})
st.write(result['result'])
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"lancedb.connect"
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from pathlib import Path
import geopandas as gpd
import lancedb
import matplotlib.pyplot as plt
from skimage import io
# Set working directory
wd = "/home/usr/Desktop/"
# To download the existing embeddings run aws s3 sync
# aws s3 sync s3://clay-worldcover-embeddings /my/dir/clay-worldcover-embeddings
vector_dir = Path(wd + "clay-worldcover-embeddings/v002/2021/")
# Create new DB structure or open existing
db = lancedb.connect(wd + "worldcoverembeddings_db")
# Read all vector embeddings into a list
data = []
for strip in vector_dir.glob("*.gpq"):
print(strip)
tile_df = gpd.read_parquet(strip).to_crs("epsg:3857")
for _, row in tile_df.iterrows():
data.append(
{"vector": row["embeddings"], "year": 2021, "bbox": row.geometry.bounds}
)
# Show table names
db.table_names()
# Drop existing table if exists
db.drop_table("worldcover-2021-v001")
# Create embeddings table and insert the vector data
tbl = db.create_table("worldcover-2021-v001", data=data, mode="overwrite")
# Visualize some image chips
def plot(df, cols=10):
fig, axs = plt.subplots(1, cols, figsize=(20, 10))
for ax, (i, row) in zip(axs.flatten(), df.iterrows()):
bbox = row["bbox"]
url = f"https://services.terrascope.be/wms/v2?SERVICE=WMS&version=1.1.1&REQUEST=GetMap&layers=WORLDCOVER_2021_S2_TCC&BBOX={','.join([str(dat) for dat in bbox])}&SRS=EPSG:3857&FORMAT=image/png&WIDTH=512&HEIGHT=512" # noqa: E501
image = io.imread(url)
ax.imshow(image)
ax.set_axis_off()
plt.tight_layout()
plt.show()
# Select a vector by index, and search 10 similar pairs, and plot
v = tbl.to_pandas()["vector"].values[10540]
result = tbl.search(query=v).limit(5).to_pandas()
plot(result, 5)
| [
"lancedb.connect"
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# Copyright 2023 llmware
# Licensed under the Apache License, Version 2.0 (the "License"); you
# may not use this file except in compliance with the License. You
# may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# implied. See the License for the specific language governing
# permissions and limitations under the License.
"""The embeddings module implements the supported vector databases.
The common abstraction for all supported vector databases is the EmbeddingHandler class, which supports
creating a new embedding, as well as searching and deleting the vector index. The module also implements the
_EmbeddingUtils class, which provides a set of functions used by all vector database classes.
"""
import os
import logging
import numpy as np
import re
import time
import uuid
from pymilvus import connections, utility, FieldSchema, CollectionSchema, DataType, Collection
from pymongo import MongoClient
try:
import faiss
except ImportError:
pass
# note: update- adding psycopg and postgres to core llmware package in version 0.2.0
try:
from pgvector.psycopg import register_vector
import psycopg
except ImportError:
pass
# optional imports of redis - not in project requirements
try:
import redis
from redis.commands.search.field import TagField, TextField, NumericField
from redis.commands.search.indexDefinition import IndexDefinition, IndexType
from redis.commands.search.query import Query
from redis.commands.search.field import VectorField
except ImportError:
pass
# optional imports of qdrant - not in project requirements
try:
from qdrant_client import QdrantClient
from qdrant_client.http.models import Distance, VectorParams, PointStruct
except ImportError:
pass
# optional import of pinecone - not in project requirements
try:
import pinecone
except ImportError:
pass
# optional import of lancedb - not in project requirements
try:
import lancedb
except ImportError:
pass
# optional import of neo4j - not in project requirements
try:
import neo4j
from neo4j import GraphDatabase
except:
pass
# optional import of chromadb - not in project requirements
try:
import chromadb
except:
pass
from llmware.configs import LLMWareConfig, MongoConfig, MilvusConfig, PostgresConfig, RedisConfig, \
PineconeConfig, QdrantConfig, Neo4jConfig, LanceDBConfig, ChromaDBConfig
from llmware.exceptions import (UnsupportedEmbeddingDatabaseException, EmbeddingModelNotFoundException,
DependencyNotInstalledException)
from llmware.resources import CollectionRetrieval, CollectionWriter
from llmware.status import Status
from llmware.util import Utilities
class EmbeddingHandler:
"""Provides an interface to all supported vector dabases, which is used by the ``Library`` class.
``EmbeddingHandler`` is responsible for embedding-related interactions between a library and a vector
store. This includes creating, reading, updating, and deleting (CRUD) embeddings. The ``EmbeddingHandler``,
in addition, synchronizes the vector store with the text collection database, this includes incremental
updates to the embeddings. Finally, it also allows one library to have multiple embeddings.
Parameters
----------
library : Library
The library with which the ``EmbeddingHandler`` interacts.
Returns
-------
embedding_handler : EmbeddingHandler
A new ``EmbeddingHandler`` object.
"""
def __init__(self, library):
self.supported_embedding_dbs = LLMWareConfig().get_supported_vector_db()
self.library = library
def create_new_embedding(self, embedding_db, model, doc_ids=None, batch_size=500):
""" Creates new embedding - routes to correct vector db and loads the model and text collection """
embedding_class = self._load_embedding_db(embedding_db, model=model)
embedding_status = embedding_class.create_new_embedding(doc_ids, batch_size)
if embedding_status:
if "embeddings_created" in embedding_status:
if embedding_status["embeddings_created"] > 0:
# only update if non-zero embeddings created
if "embedded_blocks" in embedding_status:
embedded_blocks = embedding_status["embedded_blocks"]
else:
embedded_blocks = -1
logging.warning("update: embedding_handler - unable to determine if embeddings have "
"been properly counted and captured. Please check if databases connected.")
self.library.update_embedding_status("yes", model.model_name, embedding_db,
embedded_blocks=embedded_blocks,
embedding_dims=embedding_status["embedding_dims"],
time_stamp=embedding_status["time_stamp"])
return embedding_status
def search_index(self, query_vector, embedding_db, model, sample_count=10):
""" Main entry point to vector search query """
# Need to normalize the query_vector.
# Sometimes it comes in as [[1.1,2.1,3.1]] (from Transformers) and sometimes as [1.1,2.1,3.1]
# We'll make sure it's the latter and then each Embedding Class will deal with it how it needs to
if len(query_vector) == 1:
query_vector = query_vector[0]
embedding_class = self._load_embedding_db(embedding_db, model=model)
return embedding_class.search_index(query_vector,sample_count=sample_count)
def delete_index(self, embedding_db, model_name, embedding_dims):
""" Deletes vector embedding - note: does not delete the underlying text collection """
embedding_class = self._load_embedding_db(embedding_db, model_name=model_name,
embedding_dims=embedding_dims)
embedding_class.delete_index()
self.library.update_embedding_status("delete", model_name, embedding_db,
embedded_blocks=0, delete_record=True)
return 0
def _load_embedding_db(self, embedding_db, model=None, model_name=None, embedding_dims=None):
""" Looks up and loads the selected vector database """
if not embedding_db in self.supported_embedding_dbs:
raise UnsupportedEmbeddingDatabaseException(embedding_db)
if embedding_db == "milvus":
return EmbeddingMilvus(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "faiss":
return EmbeddingFAISS(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "pinecone":
return EmbeddingPinecone(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "mongo_atlas":
return EmbeddingMongoAtlas(self.library, model=model,model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "redis":
return EmbeddingRedis(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "qdrant":
return EmbeddingQdrant(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "lancedb":
return EmbeddingLanceDB(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
# note: pg_vector == postgres (two aliases provided)
if embedding_db in ["pg_vector", "postgres"]:
return EmbeddingPGVector(self.library,model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "neo4j":
return EmbeddingNeo4j(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
if embedding_db == "chromadb":
return EmbeddingChromaDB(self.library, model=model, model_name=model_name,
embedding_dims=embedding_dims)
def generate_index_name(self, account_name, library_name, model_name, max_component_length=19):
""" Creates a unique name for the vector index that concats library_name + model_name + account_name """
index_name = account_name
# Remove non-alphanumerics from the remaining components and if still longer than the max, remove middle chars
for s in [library_name, model_name]:
s = re.sub(r'\W+', '', s)
if len(s) > max_component_length:
excess_length = len(s) - max_component_length
left_length = (len(s) - excess_length) // 2
right_start = left_length + excess_length
index_name += s[:left_length] + s[right_start:]
# Return the lowercase name:
return index_name.lower()
class _EmbeddingUtils:
"""Provides functions to vector stores, such as creating names for the text collection database as well
as creating names for vector such, and creating a summary of an embedding process.
``_EmbeddingUTils`` provides utilities used by all vector stores, especially in interaction and
synchronization with the underlying text collection database. In short, it has functions for
creating names, the text index, the embedding flag, the block curser, and the embedding summary.
Parameters
----------
library_name : str, default=None
Name of the library.
model_name : str, default=None
Name of the model.
account_name : str, default=None
Name of the account.
db_name : str, default=None
Name of the vector store.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_utils : _EmbeddingUtils
A new ``_EmbeddingUtils`` object.
"""
def __init__(self, library_name=None, model_name=None, account_name=None,db_name=None,
embedding_dims=None):
self.library_name = library_name
self.account_name = account_name
self.model_name = model_name
self.db_name = db_name
self.embedding_dims = embedding_dims
self.collection_key= None
self.collection_name= None
def create_safe_collection_name(self):
""" Creates concatenated safe name for collection """
converted_library_name = re.sub(r"[-@_.\/ ]", "", self.library_name).lower()
if len(converted_library_name) > 18:
converted_library_name = converted_library_name[0:18]
converted_model_name = re.sub(r"[-@_.\/ ]", "", self.model_name).lower()
if len(converted_model_name) > 18:
# chops off the start of the model name if longer than 18 chars
starter = len(converted_model_name) - 18
converted_model_name = converted_model_name[starter:]
converted_account_name = re.sub(r"[-@_.\/ ]", "", self.account_name).lower()
if len(converted_model_name) > 7:
converted_account_name = converted_account_name[0:7]
# create collection name here - based on account + library + model_name
self.collection_name = f"{converted_account_name}_{converted_library_name}_{converted_model_name}"
return self.collection_name
def create_db_specific_key(self):
""" Creates db_specific key """
# will leave "-" and "_" in file path, but remove "@" and " "
model_safe_path = re.sub(r"[@ ]", "", self.model_name).lower()
self.collection_key = f"embedding_{self.db_name}_" + model_safe_path
return self.collection_key
def get_blocks_cursor(self, doc_ids = None):
""" Retrieves a cursor from the text collection database that will define the scope of text chunks
to be embedded """
if not self.collection_key:
self.create_db_specific_key()
cr = CollectionRetrieval(self.library_name, account_name=self.account_name)
num_of_blocks, all_blocks_cursor = cr.embedding_job_cursor(self.collection_key,doc_id=doc_ids)
return all_blocks_cursor, num_of_blocks
def generate_embedding_summary(self, embeddings_created):
""" Common summary dictionary at end of embedding job """
if not self.collection_key:
self.create_db_specific_key()
cr = CollectionRetrieval(self.library_name,account_name=self.account_name)
embedded_blocks = cr.count_embedded_blocks(self.collection_key)
embedding_summary = {"embeddings_created": embeddings_created,
"embedded_blocks": embedded_blocks,
"embedding_dims": self.embedding_dims,
"time_stamp": Utilities().get_current_time_now()}
# print("update: embedding_summary - ", embedding_summary)
return embedding_summary
def update_text_index(self, block_ids, current_index):
""" Update main text collection db """
for block_id in block_ids:
cw = CollectionWriter(self.library_name, account_name=self.account_name)
cw.add_new_embedding_flag(block_id,self.collection_key,current_index)
current_index += 1
return current_index
def lookup_text_index(self, _id, key="_id"):
"""Returns a single block entry from text index collection with lookup by _id - returns a list, not a cursor"""
cr = CollectionRetrieval(self.library_name, account_name=self.account_name)
block_cursor = cr.lookup(key, _id)
return block_cursor
def lookup_embedding_flag(self, key, value):
""" Used to look up an embedding flag in text collection index """
# used specifically by FAISS index - which uses the embedding flag value as lookup
cr = CollectionRetrieval(self.library_name, account_name=self.account_name)
block_cursor = cr.embedding_key_lookup(key,value)
return block_cursor
def unset_text_index(self):
"""Removes embedding key flag for library, e.g., 'unsets' a group of blocks in text index """
cw = CollectionWriter(self.library_name, account_name=self.account_name)
cw.unset_embedding_flag(self.collection_key)
return 0
class EmbeddingMilvus:
"""Implements the vector database Milvius.
``EmbeddingMivlus`` implements the interface to the ``Milvus`` vector store. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_milvus : EmbeddingMilvus
A new ``EmbeddingMilvus`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.library = library
self.library_name = library.library_name
self.account_name = library.account_name
self.milvus_alias = "default"
# Connect to milvus
connections.connect(self.milvus_alias,
host=MilvusConfig.get_config("host"),
port=MilvusConfig.get_config("port"),
db_name=MilvusConfig.get_config("db_name"))
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.model=model
self.model_name=model_name
self.embedding_dims = embedding_dims
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = self.model.embedding_dims
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="milvus",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# if collection does not exist, create it
if not utility.has_collection(self.collection_name):
fields = [
FieldSchema(name="block_mongo_id", dtype=DataType.VARCHAR, is_primary=True, max_length=30,auto_id=False),
FieldSchema(name="block_doc_id", dtype=DataType.INT64),
FieldSchema(name="embedding_vector", dtype=DataType.FLOAT_VECTOR, dim=self.embedding_dims)
]
collection = Collection(self.collection_name, CollectionSchema(fields))
index_params = {
"metric_type": "L2",
"index_type": "IVF_FLAT",
"params": {"nlist": 1024}
}
collection.create_index("embedding_vector", index_params)
self.collection = Collection(self.collection_name)
def create_new_embedding(self, doc_ids = None, batch_size=500):
""" Create new embedding """
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.account_name)
status.new_embedding_status(self.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
# data model
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
data = [block_ids, doc_ids, vectors]
self.collection.insert(data)
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library_name, self.model_name, len(sentences))
# will add configuration options to show/display
print (f"update: embedding_handler - Milvus - Embeddings Created: {embeddings_created} of {num_of_blocks}")
self.collection.flush()
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Milvus - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
self.collection.load()
search_params = {
"metric_type": "L2",
"params": {"nprobe": 10}
}
# TODO: add optional / configurable partitions
result = self.collection.search(
data=[query_embedding_vector],
anns_field="embedding_vector",
param=search_params,
limit=sample_count,
output_fields=["block_mongo_id"]
)
block_list = []
for hits in result:
for hit in hits:
_id = hit.entity.get('block_mongo_id')
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, hit.distance))
"""
try:
block = block_cursor.next()
block_list.append((block, hit.distance))
except StopIteration:
# The cursor is empty (no blocks found)
continue
"""
return block_list
def delete_index(self):
collection = Collection(self.collection_name)
collection.release()
utility.drop_collection(self.collection_name)
connections.disconnect(self.milvus_alias)
# Synchronize and remove embedding flag from collection db
self.utils.unset_text_index()
return 1
class EmbeddingFAISS:
"""Implements the vector database FAISS.
``EmbeddingFAISS`` implements the interface to the ``FAISS`` vector database. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_faiss : EmbeddingFAISS
A new ``EmbeddingFAISS`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.library = library
self.library_name = library.library_name
self.account_name = library.account_name
self.index = None
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.model=model
self.model_name=model_name
self.embedding_dims=embedding_dims
# if model passed (not None), then use model name and embedding dims
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = self.model.embedding_dims
# embedding file name here
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="faiss",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# will leave "-" and "_" in file path, but remove "@" and " "
model_safe_path = re.sub(r"[@\/. ]", "", self.model_name).lower()
self.embedding_file_path = os.path.join(self.library.embedding_path, model_safe_path, "embedding_file_faiss")
# self.collection_key = "embedding_faiss_" + model_safe_path
def create_new_embedding(self, doc_ids=None, batch_size=100):
""" Load or create index """
if not self.index:
if os.path.exists(self.embedding_file_path):
# shifted faiss to optional dependency
# note: there may be an edge case where this faiss command would fail even with
# library installed, but we throw dependency not installed error as most likely cause
try:
self.index = faiss.read_index(self.embedding_file_path)
except:
raise DependencyNotInstalledException("faiss-cpu")
else:
try:
self.index = faiss.IndexFlatL2(self.embedding_dims)
except:
raise DependencyNotInstalledException("faiss-cpu")
# get cursor for text collection with blocks requiring embedding
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.account_name)
status.new_embedding_status(self.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
finished = False
# batch_size = 50
# all_blocks_iter = iter(all_blocks_cursor)
while not finished:
block_ids, sentences = [], []
current_index = self.index.ntotal
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
self.index.add(np.array(vectors))
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add options to display/hide
print (f"update: embedding_handler - FAISS - Embeddings Created: {embeddings_created} of {num_of_blocks}")
# Ensure any existing file is removed before saving
if os.path.exists(self.embedding_file_path):
os.remove(self.embedding_file_path)
os.makedirs(os.path.dirname(self.embedding_file_path), exist_ok=True)
faiss.write_index(self.index, self.embedding_file_path)
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - FAISS - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index (self, query_embedding_vector, sample_count=10):
""" Search FAISS index """
if not self.index:
self.index = faiss.read_index(self.embedding_file_path)
distance_list, index_list = self.index.search(np.array([query_embedding_vector]), sample_count)
block_list = []
for i, index in enumerate(index_list[0]):
index_int = int(index.item())
# FAISS is unique in that it requires a 'reverse lookup' to match the FAISS index in the
# text collection
block_result_list = self.utils.lookup_embedding_flag(self.collection_key,index_int)
# block_result_list = self.utils.lookup_text_index(index_int, key=self.collection_key)
for block in block_result_list:
block_list.append((block, distance_list[0][i]))
return block_list
def delete_index(self):
""" Delete FAISS index """
if os.path.exists(self.embedding_file_path):
os.remove(self.embedding_file_path)
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 1
class EmbeddingLanceDB:
"""Implements the vector database LanceDB.
``EmbeddingLancDB`` implements the interface to the ``LanceDB`` vector database. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_lancedb : EmbeddingLanceDB
A new ``EmbeddingLanceDB`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.uri = LanceDBConfig().get_config("uri")
self.library = library
self.library_name = self.library.library_name
self.account_name = self.library.account_name
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = model.embedding_dims
# initialize LanceDB
self.index = None
# initiate connection to LanceDB locally
try:
self.db = lancedb.connect(self.uri)
except:
raise ImportError(
"Exception - could not connect to LanceDB - please check:"
"1. LanceDB python package is installed, e.g,. 'pip install lancedb', and"
"2. The uri is properly set.")
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="lancedb",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# build new name here
# self.index_name = self.collection_name
if self.collection_name not in self.db.table_names():
self.index = self._init_table(self.collection_name)
# you don't need to create an index with lanceDB upto million vectors is efficiently supported with peak performance,
# Creating an index will fasten the search process and it needs to be done once table has some vectors already.
# connect to table
self.index = self.db.open_table(self.collection_name)
def _init_table(self,table_name):
try:
import pyarrow as pa
except:
raise DependencyNotInstalledException("pyarrow")
schema = pa.schema([
pa.field("vector", pa.list_(pa.float32(), int(self.embedding_dims))),
pa.field("id", pa.string()),
])
tbl = self.db.create_table(table_name, schema=schema, mode="overwrite")
return tbl
def create_new_embedding(self, doc_ids = None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
# starting current_index @ 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
# block = next(all_blocks_iter, None)
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
# expects records as tuples - (batch of _ids, batch of vectors, batch of dict metadata)
# records = zip(block_ids, vectors) #, doc_ids)
# upsert to lanceDB
try :
vectors_ingest = [{ 'id' : block_id,'vector': vector.tolist()} for block_id,vector in zip(block_ids,vectors)]
self.index.add(vectors_ingest)
except Exception as e :
print(self.index)
print('schema',self.index.schema)
raise e
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add options to configure to show/hide
print (f"update: embedding_handler - Lancedb - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Lancedb - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
try:
result = self.index.search(query=query_embedding_vector.tolist())\
.select(["id", "vector"])\
.limit(sample_count).to_pandas()
block_list = []
for (_, id, vec, score) in result.itertuples(name=None):
_id = id
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, score))
# for match in result.itertuples(index=False):
# _id = match.id
# block_result_list = self.utils.lookup_text_index(_id)
# for block in block_result_list:
# block_list.append((block, match._distance))
except Exception as e:
print("result df cols" ,result.columns, type(result))
raise e
return block_list
def delete_index(self):
self.db.drop_table(self.collection_name)
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 1
class EmbeddingPinecone:
"""Implements the vector database Pinecone.
``EmbeddingPinecone`` implements the interface to the ``Pinecone`` vector database. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_pinecone : EmbeddingPinecone
A new ``EmbeddingPinecone`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.api_key = PineconeConfig().get_config("pinecone_api_key")
self.environment = PineconeConfig().get_config("pinecone_environment")
self.library = library
self.library_name = self.library.library_name
self.account_name = self.library.account_name
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = model.embedding_dims
# initialize pinecone
self.index = None
# initiate connection to Pinecone
try:
pinecone.init(api_key=self.api_key, environment=self.environment)
except:
raise ImportError(
"Exception - could not connect to Pinecone - please check:"
"1. Pinecone python package is installed, e.g,. 'pip install pinecone-client', and"
"2. The api key and environment is properly set.")
# check index name - pinecone - 45 chars - numbers, letters and "-" ok - no "_" and all lowercase
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="pinecone",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# build new name here
# self.index_name = self.collection_name
if self.collection_name not in pinecone.list_indexes():
pinecone.create_index(self.collection_name, dimension=self.embedding_dims, metric="euclidean")
pinecone.describe_index(self.collection_name) # Waits for index to be created
# describe_index_stats() # Returns: {'dimension': 8, 'index_fullness': 0.0, 'namespaces': {'': {'vector_count': 5}}}
# connect to index
self.index = pinecone.Index(self.collection_name)
def create_new_embedding(self, doc_ids = None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
# starting current_index @ 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
# block = next(all_blocks_iter, None)
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences).tolist()
# expects records as tuples - (batch of _ids, batch of vectors, batch of dict metadata)
records = zip(block_ids, vectors) #, doc_ids)
# upsert to Pinecone
self.index.upsert(vectors=records)
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add options to configure to show/hide
print (f"update: embedding_handler - Pinecone - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Pinecone - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
result = self.index.query(vector=query_embedding_vector.tolist(), top_k=sample_count,include_values=True)
block_list = []
for match in result["matches"]:
_id = match["id"]
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, match["score"]))
return block_list
def delete_index(self, index_name):
pinecone.delete_index(index_name)
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 1
class EmbeddingMongoAtlas:
"""Implements the use of MongoDB Atlas as a vector database.
``EmbeddingMongoAtlas`` implements the interface to ``MongoDB Atlas``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_mongoatlas : EmbeddingMongoAtlas
A new ``EmbeddingMongoAtlas`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
# Use a specified Mongo Atlas connection string if supplied.
# Otherwise fallback to the the Mongo DB connection string
# self.connection_uri = os.environ.get("MONGO_ATLAS_CONNECTION_URI", MongoConfig.get_config("collection_db_uri"))
self.connection_uri = MongoConfig().get_config("atlas_db_uri")
self.library = library
self.library_name = self.library.library_name
self.account_name = self.library.account_name
# look up model card
self.model_name = model.model_name
self.model = model
self.embedding_dims = embedding_dims
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = model.embedding_dims
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="mongoatlas",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# build new name here
# self.index_name = self.collection_name
# Connect and create a MongoClient
self.mongo_client = MongoClient(self.connection_uri)
# Make sure the Database exists by creating a dummy metadata collection
self.embedding_db_name = "llmware_embeddings"
self.embedding_db = self.mongo_client["llmware_embeddings"]
if self.embedding_db_name not in self.mongo_client.list_database_names():
self.embedding_db["metadata"].insert_one({"created": Utilities().get_current_time_now()})
# Connect to collection and create it if it doesn't exist by creating a dummy doc
self.embedding_collection = self.embedding_db[self.collection_name]
if self.collection_name not in self.embedding_db.list_collection_names():
self.embedding_collection.insert_one({"created": Utilities().get_current_time_now()})
# If the collection does not have a search index (e.g if it's new), create one
if len (list(self.embedding_collection.list_search_indexes())) < 1:
model = {
'name': self.collection_name,
'definition': {
'mappings': {
'dynamic': True,
'fields': {
'eVector': {
'type': 'knnVector',
'dimensions': self.embedding_dims,
'similarity': 'euclidean'
},
}
}
}
}
self.embedding_collection.create_search_index(model)
def create_new_embedding(self, doc_ids = None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
# starting current_index @ 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
last_block_id = ""
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
# block = next(all_blocks_iter, None)
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences).tolist()
docs_to_insert = []
for i, vector in enumerate(vectors):
doc = {
"id": str(block_ids[i]),
"doc_ID": str(doc_ids[i]),
"eVector": vector
}
docs_to_insert.append(doc)
insert_result = self.embedding_collection.insert_many(docs_to_insert)
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add configuration options to hide/show
print (f"update: embedding_handler - Mongo Atlas - Embeddings Created: {embeddings_created} of {num_of_blocks}")
last_block_id = block_ids[-1]
if embeddings_created > 0:
print(f"Embedding(Mongo Atlas): Waiting for {self.embedding_db_name}.{self.collection_name} to be ready for vector search...")
start_time = time.time()
self.wait_for_search_index(last_block_id, start_time)
wait_time = time.time() - start_time
print(f"Embedding(Mongo Atlas): {self.embedding_db_name}.{self.collection_name} ready ({wait_time: .2f} seconds)")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Mongo Atlas - embedding_summary - %s", embedding_summary)
return embedding_summary
# After doc insertion we want to make sure the index is ready before proceeding
def wait_for_search_index(self, last_block_id, start_time):
# If we've been waiting for 5 mins, then time out and just return
if time.time() > start_time + (5 * 60):
return
# Get the atlas search index
the_index = self.embedding_collection.list_search_indexes().next()
# If the index doesn't have status="READY" or queryable=True, wait
if the_index["status"] != "READY" or not the_index["queryable"]:
time.sleep(3)
return self.wait_for_search_index(last_block_id, start_time)
# If we can't find the last block yet in the search index, wait
search_query = {
"$search": {
"index": self.collection_name,
"text": {
"query": str(last_block_id),
"path": "id" # The field in your documents you're matching against
}
}
}
results = self.embedding_collection.aggregate([search_query])
if not results.alive:
time.sleep(1)
return self.wait_for_search_index(last_block_id, start_time)
def search_index(self, query_embedding_vector, sample_count=10):
search_results = self.embedding_collection.aggregate([
{
"$vectorSearch": {
"index": self.collection_name,
"path": "eVector",
"queryVector": query_embedding_vector.tolist(),
"numCandidates": sample_count * 10, # Following recommendation here: https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/
"limit": sample_count
}
},
{
"$project": {
"_id": 0,
"id": 1,
"doc_ID": 1,
"score": { "$meta": "vectorSearchScore" }
}
}
])
block_list = []
for search_result in search_results:
_id = search_result["id"]
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
distance = 1 - search_result["score"] # Atlas returns a score from 0 to 1.0
block_list.append((block, distance))
return block_list
def delete_index(self, index_name):
self.embedding_db.drop_collection(index_name)
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 1
class EmbeddingRedis:
"""Implements the use of Redis as a vector database.
``EmbeddingRedis`` implements the interface to ``Redis``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_redis : EmbeddingRedis
A new ``EmbeddingRedis`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.library = library
self.library_name = library.library_name
self.account_name = library.account_name
# Connect to redis - use "localhost" & 6379 by default
redis_host = RedisConfig().get_config("host")
redis_port = RedisConfig().get_config("port")
self.r = redis.Redis(host=redis_host, port=redis_port, decode_responses=True)
# look up model card
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = self.model.embedding_dims
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="redis",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
self.DOC_PREFIX = self.collection_name # key prefix used for the index
try:
# check to see if index exists
self.r.ft(self.collection_name).info()
logging.info("update: embedding_handler - Redis - index already exists - %s", self.collection_name)
except:
# schema
schema = (
NumericField("id"),
TextField("text"),
TextField("block_mongo_id"),
NumericField("block_id"),
NumericField("block_doc_id"),
VectorField("vector", # Vector Field Name
"FLAT", { # Vector Index Type: FLAT or HNSW
"TYPE": "FLOAT32", # FLOAT32 or FLOAT64
"DIM": self.embedding_dims,
"DISTANCE_METRIC": "L2", # "COSINE" alternative
}
),
)
# index Definition
definition = IndexDefinition(prefix=[self.DOC_PREFIX], index_type=IndexType.HASH)
# create Index
self.r.ft(self.collection_name).create_index(fields=schema, definition=definition)
logging.info("update: embedding_handler - Redis - creating new index - %s ", self.collection_name)
def create_new_embedding(self, doc_ids=None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
obj_batch = []
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
# block = next(all_blocks_iter, None)
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
obj = {"block_mongo_id": str(block["_id"]),
"block_doc_id": int(block["doc_ID"]),
"block_id": int(block["block_ID"]),
"text": text_search
}
obj_batch.append(obj)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
pipe = self.r.pipeline()
for i, embedding in enumerate(vectors):
redis_dict = obj_batch[i]
embedding = np.array(embedding)
redis_dict.update({"vector": embedding.astype(np.float32).tobytes()})
key_name = f"{self.DOC_PREFIX}:{redis_dict['block_mongo_id']}"
pipe.hset(key_name, mapping=redis_dict)
res = pipe.execute()
obj_batch = []
# end - insert
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add configuration options to show/display
print(f"update: embedding_handler - Redis - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Redis - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
query_embedding_vector = np.array(query_embedding_vector)
query = (
Query(f"*=>[KNN {sample_count} @vector $vec as score]")
.sort_by("score")
.return_fields("score", "block_mongo_id", "block_doc_id", "block_id","text")
.paging(0, sample_count)
.dialect(2)
)
query_params = {
"vec": query_embedding_vector.astype(np.float32).tobytes()
}
results = self.r.ft(self.collection_name).search(query, query_params).docs
block_list = []
for j, res in enumerate(results):
# print("results: ", j, res)
_id = str(res["block_mongo_id"])
score = float(res["score"])
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, score))
return block_list
def delete_index(self):
# delete index
self.r.ft(self.collection_name).dropindex(delete_documents=True)
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 0
class EmbeddingQdrant:
"""Implements the Qdrant vector database.
``EmbeddingQdrant`` implements the interface to ``Qdrant``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_qdrant : EmbeddingQdrant
A new ``EmbeddingQdrant`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
self.library = library
self.library_name = library.library_name
self.account_name = library.account_name
self.qclient = QdrantClient(**QdrantConfig.get_config())
# look up model card
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = self.model.embedding_dims
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="qdrant",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# check if collection already exists, or if needs to be created
collections = self.qclient.get_collections()
collection_exists = False
for i, cols in enumerate(collections.collections):
if cols.name == self.collection_name:
collection_exists = True
break
if not collection_exists:
self.collection = (
self.qclient.create_collection(
collection_name=self.collection_name,
vectors_config=VectorParams(size=self.embedding_dims, distance=Distance.DOT), ))
logging.info("update: embedding_handler - QDRANT - creating new collection - %s",
self.collection_name)
else:
# if collection already exists, then 'get' collection
self.collection = self.qclient.get_collection(self.collection_name)
def create_new_embedding(self, doc_ids=None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
points_batch = []
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
for i, embedding in enumerate(vectors):
point_id = str(uuid.uuid4())
ps = PointStruct(id=point_id, vector=embedding,
payload={"block_doc_id": doc_ids[i], "sentences": sentences[i],
"block_mongo_id": block_ids[i]})
points_batch.append(ps)
# upsert a batch of points
self.qclient.upsert(collection_name=self.collection_name, wait=True,
points=points_batch)
points_batch = []
# end - insert
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add configuration options to show/display
print(
f"update: embedding_handler - Qdrant - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info("update: EmbeddingHandler - Qdrant - embedding_summary - %s", embedding_summary)
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
search_results = self.qclient.search(collection_name=self.collection_name,
query_vector=query_embedding_vector, limit=sample_count)
block_list = []
for j, res in enumerate(search_results):
# print("results: ", j, res)
_id = res.payload["block_mongo_id"]
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, res.score))
return block_list
def delete_index(self):
# delete index - need to add
self.qclient.delete_collection(collection_name=f"{self.collection_name}")
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 0
class EmbeddingPGVector:
"""Implements the interface to the PGVector vector database.
``EmbeddingPGVector`` implements the interface to ``PGVector``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_pgvector : EmbeddingPGVector
A new ``EmbeddingPGVector`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None, full_schema=False):
self.library = library
self.library_name = library.library_name
self.account_name = library.account_name
# look up model card
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = self.model.embedding_dims
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="pg_vector",
embedding_dims=self.embedding_dims)
self.collection_name = self.utils.create_safe_collection_name()
self.collection_key = self.utils.create_db_specific_key()
# Connect to postgres
postgres_host = PostgresConfig().get_config("host")
postgres_port = PostgresConfig().get_config("port")
postgres_db_name = PostgresConfig().get_config("db_name")
postgres_user_name = PostgresConfig().get_config("user_name")
postgres_pw = PostgresConfig().get_config("pw")
postgres_schema = PostgresConfig().get_config("pgvector_schema")
# default schema captures only minimum required for tracking vectors
if postgres_schema == "vector_only":
self.full_schema = False
else:
self.full_schema = True
# determines whether to use 'skinny' schema or 'full' schema
# --note: in future releases, we will be building out more support for PostGres
# self.full_schema = full_schema
# Session connection
# note: for initial connection, need to confirm that the database exists
self.conn = psycopg.connect(host=postgres_host, port=postgres_port, dbname=postgres_db_name,
user=postgres_user_name, password=postgres_pw)
# register vector extension
self.conn.execute('CREATE EXTENSION IF NOT EXISTS vector')
register_vector(self.conn)
if not self.full_schema:
table_create = (f"CREATE TABLE IF NOT EXISTS {self.collection_name} "
f"(id bigserial PRIMARY KEY, "
f"text text, "
f"embedding vector({self.embedding_dims}), "
f"block_mongo_id text, "
f"block_doc_id integer);")
else:
# full schema is a replica of the Mongo parsing output key structure
table_create = (f"CREATE TABLE IF NOT EXISTS {self.collection_name} "
f"(id bigserial PRIMARY KEY, "
f"embedding vector({self.embedding_dims}),"
f"block_mongo_id text, "
f"block_doc_id integer,"
f"block_ID integer, "
f"doc_ID integer, "
f"content_type text, "
f"file_type text, "
f"master_index integer, "
f"master_index2 integer, "
f"coords_x integer, "
f"coords_y integer, "
f"coords_cx integer, "
f"coords_cy integer, "
f"author_or_speaker text, "
f"modified_date text, "
f"created_date text, "
f"creator_tool text,"
f"added_to_collection text,"
f"table_block text,"
f"text text,"
f"external_files text,"
f"file_source text,"
f"header_text text,"
f"text_search text,"
f"user_tags text,"
f"special_field1 text,"
f"special_field2 text,"
f"special_field3 text,"
f"graph_status text,"
f"embedding_flags json,"
f"dialog text);")
# execute the creation of the table, if needed
self.conn.execute(table_create)
self.conn.commit()
def create_new_embedding(self, doc_ids=None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = iter(all_blocks_cursor)
obj_batch = []
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
# block = next(all_blocks_iter, None)
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if not self.full_schema:
obj = {"block_mongo_id": str(block["_id"]),
"block_doc_id": int(block["doc_ID"]),
"text": text_search}
else:
obj = {}
for keys in block:
if keys == "_id":
value = str(block["_id"])
obj.update({"block_mongo_id": value})
else:
value = block[keys]
obj.update({keys:value})
obj.update({"block_doc_id": int(block["doc_ID"])})
obj_batch.append(obj)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
for i, embedding in enumerate(vectors):
if not self.full_schema:
insert_command=(f"INSERT INTO {self.collection_name} (text, embedding, block_mongo_id,"
f"block_doc_id) VALUES (%s, %s, %s, %s)")
insert_array=(obj_batch[i]["text"], embedding,
obj_batch[i]["block_mongo_id"], obj_batch[i]["block_doc_id"],)
else:
insert_command=(f"INSERT INTO {self.collection_name} "
f"(embedding, block_mongo_id, block_doc_id,"
f"block_ID, doc_ID, content_type, file_type, master_index,"
f"master_index2, coords_x, coords_y,coords_cx, coords_cy,"
f"author_or_speaker, modified_date, created_date, creator_tool,"
f"added_to_collection, table_block, text, external_files,file_source,"
f"header_text, text_search, user_tags, special_field1, special_field2,"
f"special_field3, graph_status, dialog) "
f"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, "
f"%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, "
f"%s, %s, %s, %s)")
insert_array=(embedding, obj_batch[i]["block_mongo_id"],
obj_batch[i]["block_doc_id"], obj_batch[i]["block_ID"],
obj_batch[i]["doc_ID"], obj_batch[i]["content_type"],
obj_batch[i]["file_type"], obj_batch[i]["master_index"],
obj_batch[i]["master_index2"], obj_batch[i]["coords_x"],
obj_batch[i]["coords_y"], obj_batch[i]["coords_cx"],
obj_batch[i]["coords_cy"], obj_batch[i]["author_or_speaker"],
obj_batch[i]["modified_date"], obj_batch[i]["created_date"],
obj_batch[i]["creator_tool"], obj_batch[i]["added_to_collection"],
obj_batch[i]["table"], obj_batch[i]["text"], obj_batch[i]["external_files"],
obj_batch[i]["file_source"], obj_batch[i]["header_text"],
obj_batch[i]["text_search"], obj_batch[i]["user_tags"],
obj_batch[i]["special_field1"], obj_batch[i]["special_field2"], obj_batch[i]["special_field3"],
obj_batch[i]["graph_status"], obj_batch[i]["dialog"])
self.conn.execute(insert_command, insert_array)
self.conn.commit()
obj_batch = []
# end - insert
current_index = self.utils.update_text_index(block_ids,current_index)
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
# will add configuration options to show/display
print(f"update: embedding_handler - PGVector - Embeddings Created: "
f"{embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
embedded_blocks = embedding_summary["embedded_blocks"]
logging.info("update: EmbeddingHandler - PG_Vector - embedding_summary - %s", embedding_summary)
# safety check on output
if not isinstance(embedded_blocks, int):
if len(embedded_blocks) > 0:
embedded_blocks = embedded_blocks[0]
else:
embedded_blocks = embeddings_created
# create index
lists = max(embedded_blocks // 1000, 10)
create_index_command = (f"CREATE INDEX ON {self.collection_name} "
f"USING ivfflat(embedding vector_l2_ops) WITH(lists={lists});")
self.conn.execute(create_index_command)
self.conn.commit()
# tbd - next steps - will create text index and options to query directly against PG
# Closing the connection
self.conn.close()
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
# note: converting to np.array is 'safety' for postgres vector type
query_embedding_vector = np.array(query_embedding_vector)
q = (f"SELECT id, block_mongo_id, embedding <-> %s AS distance, text "
f"FROM {self.collection_name} ORDER BY distance LIMIT %s")
"""
# look to generalize the query
q = (f"SELECT embedding <-> %s AS distance, * FROM {self.collection_name} ORDER BY "
f"distance LIMIT %s")
"""
cursor = self.conn.cursor()
results = cursor.execute(q, (query_embedding_vector,sample_count))
block_list = []
for j, res in enumerate(results):
pg_id = res[0]
_id = res[1]
distance = res[2]
text = res[3]
block_result_list = self.utils.lookup_text_index(_id)
for block in block_result_list:
block_list.append((block, distance))
# Closing the connection
self.conn.close()
return block_list
def delete_index(self, collection_name=None):
# delete index - drop table
if collection_name:
self.collection_name = collection_name
drop_command = f'''DROP TABLE {self.collection_name} '''
# Executing the query
cursor = self.conn.cursor()
cursor.execute(drop_command)
logging.info("update: embedding_handler - PG Vector - table dropped - %s", self.collection_name)
# Commit your changes in the database
self.conn.commit()
# Closing the connection
self.conn.close()
# remove emb key - 'unset' the blocks in the text collection
self.utils.unset_text_index()
return 0
class EmbeddingNeo4j:
"""Implements the interface to Neo4j as a vector database.
``EmbeddingNeo4j`` implements the interface to ``Neo4j``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_Neo4j : EmbeddingNeo4j
A new ``EmbeddingNeo4j`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.library = library
self.library_name = library.library_name
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
self.account_name = library.account_name
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = model.embedding_dims
# user and password names are taken from environmen variables
# Names for user and password are taken from the link below
# https://neo4j.com/docs/operations-manual/current/tools/neo4j-admin/upload-to-aura/#_options
uri = Neo4jConfig.get_config('uri')
user = Neo4jConfig.get_config('user')
password = Neo4jConfig.get_config('password')
database = Neo4jConfig.get_config('database')
# Connect to Neo4J and verify connection.
# Code taken from the code below
# https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/vectorstores/neo4j_vector.py#L165C9-L177C14
try:
self.driver = GraphDatabase.driver(uri, auth=(user, password))
self.driver.verify_connectivity()
except neo4j.exceptions.ServiceUnavailable:
raise ValueError(
"Could not connect to Neo4j database. "
"Please ensure that the url is correct and that Neo4j is up and running.")
except neo4j.exceptions.AuthError:
raise ValueError(
"Could not connect to Neo4j database. "
"Please ensure that the username and password are correct.")
except Exception as err:
# We raise here any other excpetion that happend.
# This is usefull for debugging when some other error occurs.
raise
# Make sure that the Neo4j version supports vector indexing.
neo4j_version = self._query('call dbms.components() '
'yield name, versions, edition '
'unwind versions as version '
'return version')[0]['version']
neo4j_version = tuple(map(int, neo4j_version.split('.')))
target_version = (5, 11, 0)
if neo4j_version < target_version:
raise ValueError('Vector indexing requires a Neo4j version >= 5.11.0')
# If the index does not exist, then we create the vector search index.
neo4j_indexes = self._query('SHOW INDEXES yield name')
neo4j_indexes = [neo4j_index['name'] for neo4j_index in neo4j_indexes]
if 'vectorIndex' not in neo4j_indexes:
self._query(
query='CALL '
'db.index.vector.createNodeIndex('
'$indexName, '
'$label, '
'$propertyKey, '
'toInteger($vectorDimension), '
'"euclidean"'
')',
parameters={
'indexName': 'vectorIndex',
'label': 'Chunk',
'propertyKey': 'embedding',
'vectorDimension': int(self.model.embedding_dims)
})
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="neo4j",
embedding_dims=self.embedding_dims)
def create_new_embedding(self, doc_ids=None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = all_blocks_cursor.pull_one()
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
data = [block_ids, doc_ids, vectors]
# Insert into Neo4J
insert_query = (
"UNWIND $data AS row "
"CALL "
"{ "
"WITH row "
"MERGE (c:Chunk {id: row.doc_id, block_id: row.block_id}) "
"WITH c, row "
"CALL db.create.setVectorProperty(c, 'embedding', row.embedding) "
"YIELD node "
"SET c.sentence = row.sentence "
"} "
f"IN TRANSACTIONS OF {batch_size} ROWS"
)
parameters = {
"data": [
{"block_id": block_id, "doc_id": doc_id, "sentence": sentences, "embedding": vector}
for block_id, doc_id, sentence, vector in zip(
block_ids, doc_ids, sentences, vectors
)
]
}
self._query(query=insert_query, parameters=parameters)
current_index = self.utils.update_text_index(block_ids, current_index)
# Update statistics
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
print(f"update: embedding_handler - Neo4j - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info(f'update: EmbeddingHandler - Neo4j - embedding_summary - {embedding_summary}')
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
block_list = []
search_query = 'CALL db.index.vector.queryNodes("vectorIndex" , $sample_count, $query_embedding_vector) '\
'YIELD node, score '
parameters = {'sample_count': sample_count, 'query_embedding_vector': query_embedding_vector}
results = self._query(query=search_query, parameters=parameters)
for result in results:
block_id = result['node']['block_id']
block_result_list = self.utils.lookup_text_index(block_id)
for block in block_result_list:
block_list.append((block, result["score"]))
return block_list
def delete_index(self, index_name):
try:
self._query(f"DROP INDEX $index_name", {'index_name': index_name})
except DatabaseError: # Index did not exist yet
pass
self.utils.unset_text_index()
def _query(self, query, parameters=None):
from neo4j.exceptions import CypherSyntaxError
parameters = parameters or {}
with self.driver.session(database='neo4j') as session:
try:
data = session.run(query, parameters)
return [d.data() for d in data]
except CypherSyntaxError as e:
raise ValueError(f'Cypher Statement is not valid\n{e}')
class EmbeddingChromaDB:
"""Implements the interface to the ChromaDB vector database.
``EmbeddingChromaDB`` implements the interface to ``ChromaDB``. It is used by the
``EmbeddingHandler``.
Parameters
----------
library : object
A ``Library`` object.
model : object
A model object. See :mod:`models` for available models.
model_name : str, default=None
Name of the model.
embedding_dims : int, default=None
Dimension of the embedding.
Returns
-------
embedding_chromadb : EmbeddingChromaDB
A new ``EmbeddingPGVector`` object.
"""
def __init__(self, library, model=None, model_name=None, embedding_dims=None):
#
# General llmware set up code
#
# look up model card
if not model and not model_name:
raise EmbeddingModelNotFoundException("no-model-or-model-name-provided")
self.library = library
self.library_name = library.library_name
self.model = model
self.model_name = model_name
self.embedding_dims = embedding_dims
self.account_name = library.account_name
# if model passed (not None), then use model name
if self.model:
self.model_name = self.model.model_name
self.embedding_dims = model.embedding_dims
#
# ChromaDB instantiation
#
# Get environment variables to decide which client to use.
persistent_path = ChromaDBConfig.get_config('persistent_path')
host = ChromaDBConfig.get_config('host')
# Instantiate client.
if host is None and persistent_path is None:
self.client = chromadb.EphemeralClient()
if persistent_path is not None:
self.client = chromadb.PersistentClient(path=persistent_path)
if host is not None:
self.client = chromadb.HttpClient(host=host,
port=ChromaDBConfig.get_config('port'),
ssl=ChromaDBConfig.get_config('ssl'),
headers=ChromaDBConfig.get_config('headers'))
collection_name = ChromaDBConfig.get_config('collection')
# If the collection already exists, it is returned.
self._collection = self.client.create_collection(name=collection_name, get_or_create=True)
#
# Embedding utils
#
self.utils = _EmbeddingUtils(library_name=self.library_name,
model_name=self.model_name,
account_name=self.account_name,
db_name="chromadb",
embedding_dims=self.embedding_dims)
def create_new_embedding(self, doc_ids=None, batch_size=500):
all_blocks_cursor, num_of_blocks = self.utils.get_blocks_cursor(doc_ids=doc_ids)
# Initialize a new status
status = Status(self.library.account_name)
status.new_embedding_status(self.library.library_name, self.model_name, num_of_blocks)
embeddings_created = 0
current_index = 0
finished = False
# all_blocks_iter = all_blocks_cursor.pull_one()
while not finished:
block_ids, doc_ids, sentences = [], [], []
# Build the next batch
for i in range(batch_size):
block = all_blocks_cursor.pull_one()
if not block:
finished = True
break
text_search = block["text_search"].strip()
if not text_search or len(text_search) < 1:
continue
block_ids.append(str(block["_id"]))
doc_ids.append(int(block["doc_ID"]))
sentences.append(text_search)
if len(sentences) > 0:
# Process the batch
vectors = self.model.embedding(sentences)
# Insert into ChromaDB
ids = [f'{doc_id}-{block_id}' for doc_id, block_id in zip(doc_ids, block_ids)]
metadatas = [{'doc_id': doc_id, 'block_id': block_id, 'sentence': sentence}
for doc_id, block_id, sentence in zip(doc_ids, block_ids, sentences)]
self._collection.add(ids=ids,
documents=doc_ids,
embeddings=vectors,
metadatas=metadatas)
current_index = self.utils.update_text_index(block_ids, current_index)
# Update statistics
embeddings_created += len(sentences)
status.increment_embedding_status(self.library.library_name, self.model_name, len(sentences))
print(f"update: embedding_handler - ChromaDB - Embeddings Created: {embeddings_created} of {num_of_blocks}")
embedding_summary = self.utils.generate_embedding_summary(embeddings_created)
logging.info(f'update: EmbeddingHandler - ChromaDB - embedding_summary - {embedding_summary}')
return embedding_summary
def search_index(self, query_embedding_vector, sample_count=10):
block_list = []
# add one dimension because chroma expects two dimensions - a list of lists
query_embedding_vector = query_embedding_vector.reshape(1, -1)
results = self._collection.query(query_embeddings=query_embedding_vector, n_results=sample_count)
for idx_result, _ in enumerate(results['ids'][0]):
block_id = results['metadatas'][0][idx_result]['block_id']
block_result_list = self.utils.lookup_text_index(block_id)
for block in block_result_list:
block_list.append((block, results['distances'][0][idx_result]))
return block_list
def delete_index(self):
self.client.delete_collection(self._collection.name)
self.utils.unset_text_index()
| [
"lancedb.connect"
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"""LanceDB vector store."""
import logging
from typing import Any, List, Optional
import numpy as np
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.schema import (
BaseNode,
MetadataMode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
from llama_index.core.vector_stores.types import (
MetadataFilters,
BasePydanticVectorStore,
VectorStoreQuery,
VectorStoreQueryResult,
)
from llama_index.core.vector_stores.utils import (
DEFAULT_DOC_ID_KEY,
DEFAULT_TEXT_KEY,
legacy_metadata_dict_to_node,
metadata_dict_to_node,
node_to_metadata_dict,
)
from pandas import DataFrame
import lancedb
_logger = logging.getLogger(__name__)
def _to_lance_filter(standard_filters: MetadataFilters) -> Any:
"""Translate standard metadata filters to Lance specific spec."""
filters = []
for filter in standard_filters.legacy_filters():
if isinstance(filter.value, str):
filters.append(filter.key + ' = "' + filter.value + '"')
else:
filters.append(filter.key + " = " + str(filter.value))
return " AND ".join(filters)
def _to_llama_similarities(results: DataFrame) -> List[float]:
keys = results.keys()
normalized_similarities: np.ndarray
if "score" in keys:
normalized_similarities = np.exp(results["score"] - np.max(results["score"]))
elif "_distance" in keys:
normalized_similarities = np.exp(-results["_distance"])
else:
normalized_similarities = np.linspace(1, 0, len(results))
return normalized_similarities.tolist()
class LanceDBVectorStore(BasePydanticVectorStore):
"""
The LanceDB Vector Store.
Stores text and embeddings in LanceDB. The vector store will open an existing
LanceDB dataset or create the dataset if it does not exist.
Args:
uri (str, required): Location where LanceDB will store its files.
table_name (str, optional): The table name where the embeddings will be stored.
Defaults to "vectors".
vector_column_name (str, optional): The vector column name in the table if different from default.
Defaults to "vector", in keeping with lancedb convention.
nprobes (int, optional): The number of probes used.
A higher number makes search more accurate but also slower.
Defaults to 20.
refine_factor: (int, optional): Refine the results by reading extra elements
and re-ranking them in memory.
Defaults to None
Raises:
ImportError: Unable to import `lancedb`.
Returns:
LanceDBVectorStore: VectorStore that supports creating LanceDB datasets and
querying it.
"""
stores_text = True
flat_metadata: bool = True
_connection: Any = PrivateAttr()
uri: Optional[str]
table_name: Optional[str]
vector_column_name: Optional[str]
nprobes: Optional[int]
refine_factor: Optional[int]
text_key: Optional[str]
doc_id_key: Optional[str]
def __init__(
self,
uri: Optional[str],
table_name: str = "vectors",
vector_column_name: str = "vector",
nprobes: int = 20,
refine_factor: Optional[int] = None,
text_key: str = DEFAULT_TEXT_KEY,
doc_id_key: str = DEFAULT_DOC_ID_KEY,
**kwargs: Any,
) -> None:
"""Init params."""
self._connection = lancedb.connect(uri)
super().__init__(
uri=uri,
table_name=table_name,
vector_column_name=vector_column_name,
nprobes=nprobes,
refine_factor=refine_factor,
text_key=text_key,
doc_id_key=doc_id_key,
**kwargs,
)
@property
def client(self) -> None:
"""Get client."""
return self._connection
@classmethod
def from_params(
cls,
uri: Optional[str],
table_name: str = "vectors",
vector_column_name: str = "vector",
nprobes: int = 20,
refine_factor: Optional[int] = None,
text_key: str = DEFAULT_TEXT_KEY,
doc_id_key: str = DEFAULT_DOC_ID_KEY,
**kwargs: Any,
) -> "LanceDBVectorStore":
"""Create instance from params."""
_connection_ = cls._connection
return cls(
_connection=_connection_,
uri=uri,
table_name=table_name,
vector_column_name=vector_column_name,
nprobes=nprobes,
refine_factor=refine_factor,
text_key=text_key,
doc_id_key=doc_id_key,
**kwargs,
)
def add(
self,
nodes: List[BaseNode],
**add_kwargs: Any,
) -> List[str]:
if not nodes:
_logger.debug("No nodes to add. Skipping the database operation.")
return []
data = []
ids = []
for node in nodes:
metadata = node_to_metadata_dict(
node, remove_text=False, flat_metadata=self.flat_metadata
)
append_data = {
"id": node.node_id,
"doc_id": node.ref_doc_id,
"vector": node.get_embedding(),
"text": node.get_content(metadata_mode=MetadataMode.NONE),
"metadata": metadata,
}
data.append(append_data)
ids.append(node.node_id)
if self.table_name in self._connection.table_names():
tbl = self._connection.open_table(self.table_name)
tbl.add(data)
else:
self._connection.create_table(self.table_name, data)
return ids
def delete(self, ref_doc_id: str, **delete_kwargs: Any) -> None:
"""
Delete nodes using with ref_doc_id.
Args:
ref_doc_id (str): The doc_id of the document to delete.
"""
table = self._connection.open_table(self.table_name)
table.delete('doc_id = "' + ref_doc_id + '"')
def query(
self,
query: VectorStoreQuery,
**kwargs: Any,
) -> VectorStoreQueryResult:
"""Query index for top k most similar nodes."""
if query.filters is not None:
if "where" in kwargs:
raise ValueError(
"Cannot specify filter via both query and kwargs. "
"Use kwargs only for lancedb specific items that are "
"not supported via the generic query interface."
)
where = _to_lance_filter(query.filters)
else:
where = kwargs.pop("where", None)
table = self._connection.open_table(self.table_name)
lance_query = (
table.search(
query=query.query_embedding,
vector_column_name=self.vector_column_name,
)
.limit(query.similarity_top_k)
.where(where)
.nprobes(self.nprobes)
)
if self.refine_factor is not None:
lance_query.refine_factor(self.refine_factor)
results = lance_query.to_pandas()
nodes = []
for _, item in results.iterrows():
try:
node = metadata_dict_to_node(item.metadata)
node.embedding = list(item[self.vector_column_name])
except Exception:
# deprecated legacy logic for backward compatibility
_logger.debug(
"Failed to parse Node metadata, fallback to legacy logic."
)
if "metadata" in item:
metadata, node_info, _relation = legacy_metadata_dict_to_node(
item.metadata, text_key=self.text_key
)
else:
metadata, node_info = {}, {}
node = TextNode(
text=item[self.text_key] or "",
id_=item.id,
metadata=metadata,
start_char_idx=node_info.get("start", None),
end_char_idx=node_info.get("end", None),
relationships={
NodeRelationship.SOURCE: RelatedNodeInfo(
node_id=item[self.doc_id_key]
),
},
)
nodes.append(node)
return VectorStoreQueryResult(
nodes=nodes,
similarities=_to_llama_similarities(results),
ids=results["id"].tolist(),
)
| [
"lancedb.connect"
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# Copyright [2024] [Holosun ApS]
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from ..logging import setup_logging
from ..utils.config import load_config_key
logging = setup_logging()
def pick_vectorstore(vs_str, vector_name, embeddings):
logging.debug('Picking vectorstore')
if vs_str == 'supabase':
from supabase import Client, create_client
from langchain.vectorstores import SupabaseVectorStore
from ..database.database import setup_supabase
logging.debug(f"Initiating Supabase store: {vector_name}")
setup_supabase(vector_name)
# init embedding and vector store
supabase_url = os.getenv('SUPABASE_URL')
supabase_key = os.getenv('SUPABASE_KEY')
logging.debug(f"Supabase URL: {supabase_url} vector_name: {vector_name}")
supabase: Client = create_client(supabase_url, supabase_key)
vectorstore = SupabaseVectorStore(supabase,
embeddings,
table_name=vector_name,
query_name=f'match_documents_{vector_name}')
logging.debug("Chose Supabase")
return vectorstore
elif vs_str == 'cloudsql':
from langchain.vectorstores.pgvector import PGVector
logging.debug("Inititaing CloudSQL pgvector")
#setup_cloudsql(vector_name)
# https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/pgvector
CONNECTION_STRING = os.environ.get("PGVECTOR_CONNECTION_STRING")
# postgresql://brainuser:[email protected]:5432/brain
from ..database.database import get_vector_size
vector_size = get_vector_size(vector_name)
os.environ["PGVECTOR_VECTOR_SIZE"] = str(vector_size)
vectorstore = PGVector(connection_string=CONNECTION_STRING,
embedding_function=embeddings,
collection_name=vector_name,
#pre_delete_collection=True # for testing purposes
)
logging.debug("Chose CloudSQL")
return vectorstore
elif vs_str == 'alloydb':
from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBVectorStore
from google.cloud.alloydb.connector import IPTypes
from ..database.alloydb import create_alloydb_table
alloydb_config = load_config_key(
'alloydb_config',
vector_name=vector_name,
filename = "config/llm_config.yaml"
)
if alloydb_config is None:
logging.error("No alloydb_config was found")
ALLOYDB_DB = os.environ.get("ALLOYDB_DB")
if ALLOYDB_DB is None:
logging.error(f"Could not locate ALLOYDB_DB environment variable for {vector_name}")
logging.info(f"ALLOYDB_DB environment variable found for {vector_name} - {ALLOYDB_DB}")
logging.info("Inititaing AlloyDB Langchain")
engine = AlloyDBEngine.from_instance(
project_id=alloydb_config["project_id"],
region=alloydb_config["region"],
cluster=alloydb_config["cluster"],
instance=alloydb_config["instance"],
database=alloydb_config.get("database") or ALLOYDB_DB,
ip_type=alloydb_config.get("ip_type") or IPTypes.PRIVATE
)
create_alloydb_table(vector_name, engine)
logging.info("Chose AlloyDB")
vectorstore = AlloyDBVectorStore.create_sync(
engine=engine,
table_name=vector_name,
embedding_service=embeddings,
metadata_columns=["source"]
#metadata_columns=["source", "eventTime"]
)
return vectorstore
elif vs_str == "lancedb":
from ..patches.langchain.lancedb import LanceDB
import lancedb
LANCEDB_BUCKET = os.environ.get("LANCEDB_BUCKET")
if LANCEDB_BUCKET is None:
logging.error(f"Could not locate LANCEDB_BUCKET environment variable for {vector_name}")
logging.info(f"LANCEDB_BUCKET environment variable found for {vector_name} - {LANCEDB_BUCKET}")
db = lancedb.connect(LANCEDB_BUCKET)
logging.info(f"LanceDB Tables: {db.table_names()} using {LANCEDB_BUCKET}")
logging.info(f"Opening LanceDB table: {vector_name} using {LANCEDB_BUCKET}")
try:
table = db.open_table(vector_name)
except FileNotFoundError as err:
logging.info(f"{err} - Could not open table for {vector_name} - creating new table")
init = f"Creating new table for {vector_name}"
table = db.create_table(
vector_name,
data=[
{
"vector": embeddings.embed_query(init),
"text": init,
"id": "1",
}
],
mode="overwrite",
)
logging.info(f"Inititaing LanceDB object for {vector_name} using {LANCEDB_BUCKET}")
vectorstore = LanceDB(
connection=table,
embedding=embeddings,
)
logging.info(f"Chose LanceDB for {vector_name} using {LANCEDB_BUCKET}")
return vectorstore
else:
raise NotImplementedError(f'No llm implemented for {vs_str}')
| [
"lancedb.connect"
] | [((1190, 1215), 'os.getenv', 'os.getenv', (['"""SUPABASE_URL"""'], {}), "('SUPABASE_URL')\n", (1199, 1215), False, 'import os\n'), ((1239, 1264), 'os.getenv', 'os.getenv', (['"""SUPABASE_KEY"""'], {}), "('SUPABASE_KEY')\n", (1248, 1264), False, 'import os\n'), ((1376, 1417), 'supabase.create_client', 'create_client', (['supabase_url', 'supabase_key'], {}), '(supabase_url, supabase_key)\n', (1389, 1417), False, 'from supabase import Client, create_client\n'), ((1441, 1555), 'langchain.vectorstores.SupabaseVectorStore', 'SupabaseVectorStore', (['supabase', 'embeddings'], {'table_name': 'vector_name', 'query_name': 'f"""match_documents_{vector_name}"""'}), "(supabase, embeddings, table_name=vector_name,\n query_name=f'match_documents_{vector_name}')\n", (1460, 1555), False, 'from langchain.vectorstores import SupabaseVectorStore\n'), ((2064, 2108), 'os.environ.get', 'os.environ.get', (['"""PGVECTOR_CONNECTION_STRING"""'], {}), "('PGVECTOR_CONNECTION_STRING')\n", (2078, 2108), False, 'import os\n'), ((2365, 2474), 'langchain.vectorstores.pgvector.PGVector', 'PGVector', ([], {'connection_string': 'CONNECTION_STRING', 'embedding_function': 'embeddings', 'collection_name': 'vector_name'}), '(connection_string=CONNECTION_STRING, embedding_function=embeddings,\n collection_name=vector_name)\n', (2373, 2474), False, 'from langchain.vectorstores.pgvector import PGVector\n'), ((3162, 3190), 'os.environ.get', 'os.environ.get', (['"""ALLOYDB_DB"""'], {}), "('ALLOYDB_DB')\n", (3176, 3190), False, 'import os\n'), ((3985, 4117), 'langchain_google_alloydb_pg.AlloyDBVectorStore.create_sync', 'AlloyDBVectorStore.create_sync', ([], {'engine': 'engine', 'table_name': 'vector_name', 'embedding_service': 'embeddings', 'metadata_columns': "['source']"}), "(engine=engine, table_name=vector_name,\n embedding_service=embeddings, metadata_columns=['source'])\n", (4015, 4117), False, 'from langchain_google_alloydb_pg import AlloyDBEngine, AlloyDBVectorStore\n'), ((4421, 4453), 'os.environ.get', 'os.environ.get', (['"""LANCEDB_BUCKET"""'], {}), "('LANCEDB_BUCKET')\n", (4435, 4453), False, 'import os\n'), ((4708, 4739), 'lancedb.connect', 'lancedb.connect', (['LANCEDB_BUCKET'], {}), '(LANCEDB_BUCKET)\n', (4723, 4739), False, 'import lancedb\n')] |
import uuid
from ragna.core import Config, Document, PackageRequirement, Requirement, Source
from ._vector_database import VectorDatabaseSourceStorage
class LanceDB(VectorDatabaseSourceStorage):
"""[LanceDB vector database](https://lancedb.com/)
!!! info "Required packages"
- `chromadb>=0.4.13`
- `lancedb>=0.2`
- `pyarrow`
"""
@classmethod
def requirements(cls) -> list[Requirement]:
return [
*super().requirements(),
PackageRequirement("lancedb>=0.2"),
PackageRequirement(
"pyarrow",
# See https://github.com/apache/arrow/issues/38167
exclude_modules=["__dummy__"],
),
]
def __init__(self, config: Config) -> None:
super().__init__(config)
import lancedb
import pyarrow as pa
self._db = lancedb.connect(config.local_cache_root / "lancedb")
self._schema = pa.schema(
[
pa.field("id", pa.string()),
pa.field("document_id", pa.string()),
pa.field("page_numbers", pa.string()),
pa.field("text", pa.string()),
pa.field(
self._VECTOR_COLUMN_NAME,
pa.list_(pa.float32(), self._embedding_dimensions),
),
pa.field("num_tokens", pa.int32()),
]
)
_VECTOR_COLUMN_NAME = "embedded_text"
def store(
self,
documents: list[Document],
*,
chat_id: uuid.UUID,
chunk_size: int = 500,
chunk_overlap: int = 250,
) -> None:
table = self._db.create_table(name=str(chat_id), schema=self._schema)
for document in documents:
for chunk in self._chunk_pages(
document.extract_pages(),
chunk_size=chunk_size,
chunk_overlap=chunk_overlap,
):
table.add(
[
{
"id": str(uuid.uuid4()),
"document_id": str(document.id),
"page_numbers": self._page_numbers_to_str(
chunk.page_numbers
),
"text": chunk.text,
self._VECTOR_COLUMN_NAME: self._embedding_function(
[chunk.text]
)[0],
"num_tokens": chunk.num_tokens,
}
]
)
def retrieve(
self,
documents: list[Document],
prompt: str,
*,
chat_id: uuid.UUID,
chunk_size: int = 500,
num_tokens: int = 1024,
) -> list[Source]:
table = self._db.open_table(str(chat_id))
# We cannot retrieve source by a maximum number of tokens. Thus, we estimate how
# many sources we have to query. We overestimate by a factor of two to avoid
# retrieving to few sources and needed to query again.
limit = int(num_tokens * 2 / chunk_size)
results = (
table.search(vector_column_name=self._VECTOR_COLUMN_NAME)
.limit(limit)
.to_arrow()
)
document_map = {str(document.id): document for document in documents}
return self._take_sources_up_to_max_tokens(
(
Source(
id=result["id"],
document=document_map[result["document_id"]],
# For some reason adding an empty string during store() results
# in this field being None. Thus, we need to parse it back here.
# TODO: See if there is a configuration option for this
location=result["page_numbers"] or "",
content=result["text"],
num_tokens=result["num_tokens"],
)
for result in results.to_pylist()
),
max_tokens=num_tokens,
)
| [
"lancedb.connect"
] | [((892, 944), 'lancedb.connect', 'lancedb.connect', (["(config.local_cache_root / 'lancedb')"], {}), "(config.local_cache_root / 'lancedb')\n", (907, 944), False, 'import lancedb\n'), ((503, 537), 'ragna.core.PackageRequirement', 'PackageRequirement', (['"""lancedb>=0.2"""'], {}), "('lancedb>=0.2')\n", (521, 537), False, 'from ragna.core import Config, Document, PackageRequirement, Requirement, Source\n'), ((551, 611), 'ragna.core.PackageRequirement', 'PackageRequirement', (['"""pyarrow"""'], {'exclude_modules': "['__dummy__']"}), "('pyarrow', exclude_modules=['__dummy__'])\n", (569, 611), False, 'from ragna.core import Config, Document, PackageRequirement, Requirement, Source\n'), ((3496, 3669), 'ragna.core.Source', 'Source', ([], {'id': "result['id']", 'document': "document_map[result['document_id']]", 'location': "(result['page_numbers'] or '')", 'content': "result['text']", 'num_tokens': "result['num_tokens']"}), "(id=result['id'], document=document_map[result['document_id']],\n location=result['page_numbers'] or '', content=result['text'],\n num_tokens=result['num_tokens'])\n", (3502, 3669), False, 'from ragna.core import Config, Document, PackageRequirement, Requirement, Source\n'), ((1024, 1035), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1033, 1035), True, 'import pyarrow as pa\n'), ((1078, 1089), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1087, 1089), True, 'import pyarrow as pa\n'), ((1133, 1144), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1142, 1144), True, 'import pyarrow as pa\n'), ((1180, 1191), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1189, 1191), True, 'import pyarrow as pa\n'), ((1396, 1406), 'pyarrow.int32', 'pa.int32', ([], {}), '()\n', (1404, 1406), True, 'import pyarrow as pa\n'), ((1295, 1307), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (1305, 1307), True, 'import pyarrow as pa\n'), ((2072, 2084), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (2082, 2084), False, 'import uuid\n')] |
import argparse
from pathlib import Path
from random import sample
from typing import Any, Optional
import lancedb
import pandas as pd
from schema import Myntra
def create_table(
database: str,
table_name: str,
data_path: str,
schema: Any = Myntra,
mode: str = "overwrite",
) -> None:
"""
Create a table in the specified vector database and add data to it.
Args:
database (str): The name of the database to connect to.
table_name (str): The name of the table to create.
data_path (str): The path to the data directory.
schema (Schema, optional): The schema to use for the table. Defaults to Myntra.
mode (str, optional): The mode for creating the table. Defaults to "overwrite".
Returns:
None
Usage:
>>> create_table(database="~/.lancedb"", table_name="myntra", data_path="input")
"""
# Connect to the lancedb database
db = lancedb.connect(database)
# Check if the table already exists in the database
if table_name in db:
print(f"Table {table_name} already exists in the database")
table = db[table_name]
# if it does not exist then create a new table
else:
print(f"Creating table {table_name} in the database")
# Create the table with the given schema
table = db.create_table(table_name, schema=schema, mode=mode)
# Define the Path of the images and obtain the Image uri
p = Path(data_path).expanduser()
uris = [str(f) for f in p.glob("*.jpg")]
print(f"Found {len(uris)} images in {p}")
# Sample 1000 images from the data
# Increase this value for more accurate results but
# it will take more time to process embeddings
uris = sample(uris, 1000)
# Add the data to the table
print(f"Adding {len(uris)} images to the table")
table.add(pd.DataFrame({"image_uri": uris}))
print(f"Added {len(uris)} images to the table")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Create a table in lancedb with Myntra data"
)
parser.add_argument(
"database", help="Path to the lancedb database", default="path/to/database"
)
parser.add_argument(
"table_name", help="Name of the table to be created", default="my_table"
)
parser.add_argument(
"data_path", help="Path to the Myntra data images", default="path/to/data"
)
args = parser.parse_args()
create_table(args.database, args.table_name, args.data_path)
| [
"lancedb.connect"
] | [((936, 961), 'lancedb.connect', 'lancedb.connect', (['database'], {}), '(database)\n', (951, 961), False, 'import lancedb\n'), ((2032, 2118), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Create a table in lancedb with Myntra data"""'}), "(description=\n 'Create a table in lancedb with Myntra data')\n", (2055, 2118), False, 'import argparse\n'), ((1768, 1786), 'random.sample', 'sample', (['uris', '(1000)'], {}), '(uris, 1000)\n', (1774, 1786), False, 'from random import sample\n'), ((1899, 1932), 'pandas.DataFrame', 'pd.DataFrame', (["{'image_uri': uris}"], {}), "({'image_uri': uris})\n", (1911, 1932), True, 'import pandas as pd\n'), ((1466, 1481), 'pathlib.Path', 'Path', (['data_path'], {}), '(data_path)\n', (1470, 1481), False, 'from pathlib import Path\n')] |
from openai import OpenAI
import streamlit as st
import os
from trubrics import Trubrics
import lancedb
from langchain_community.vectorstores import LanceDB
from langchain_openai import OpenAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain_community.callbacks import TrubricsCallbackHandler
import os
import time
os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
os.environ["TRUBRICS_EMAIL"] = st.secrets["TRUBRICS_EMAIL"]
os.environ["TRUBRICS_PASSWORD"] = st.secrets["TRUBRICS_PASSWORD"]
db = lancedb.connect("/mnt/d/LLM-Project/my-app/lancedb_meta_data")
table = db.open_table("EIC_archive")
embeddings = OpenAIEmbeddings()
vectorstore = LanceDB(connection = table, embedding = embeddings)
retriever = vectorstore.as_retriever(search_type = "similarity", search_kwargs={"k" : 100})
with st.sidebar:
with st.form("User Name"):
st.info("By providing you name, you agree that all the prompts and responses will be recorded and will be used to further improve RAG methods")
name = st.text_input("What's your name?")
submitted = st.form_submit_button("Submit and start")
if submitted:
for key in st.session_state:
del st.session_state[key]
st.session_state["user_name"] = name
if "user_name" not in st.session_state:
st.stop()
llm = ChatOpenAI(model_name="gpt-3.5-turbo-1106", temperature=0,
callbacks=[
TrubricsCallbackHandler(
project="EIC-RAG-TestRun",
tags = ["EIC-RAG-TestRun"],
user_id = st.session_state["user_name"],
)
],
max_tokens=4096)
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
def format_docs(docs):
return f"\n\n".join(f'{i+1}. ' + doc.page_content.strip("\n") + f"<ARXIV_ID> {doc.metadata['arxiv_id']} <ARXIV_ID/>" for i, doc in enumerate(docs))
from langchain.prompts import PromptTemplate
response = """\
You are an expert in providing up to date information about the Electron Ion Collider (EIC), tasked with answering any question. You greet people when greeted. \
about EIC based only on the provided context. You shall strictly not answer questions anything other than EIC related questions. \
Refrain any other topics by saying you will not answer questions about them and Exit right away here. DO NOT PROCEED. \
You are not allowed to use any other sources other than the provided search results. \
Generate a comprehensive, and informative answer strictly within 200 words or less for the \
given question based solely on the provided search results (URL and content). You must \
only use information from the provided search results. Use an unbiased and \
journalistic tone. Combine search results together into a coherent answer. Do not \
repeat text. You should use bullet points in your answer for readability. Make sure to break down your answer into bullet points.\
You should not hallicunate nor build up any references, Use only the `context` html block below and do not use any text within <ARXIV_ID> and </ARXIV_ID> except when citing in the end.
Make sure not to repeat the same context. Be specific to the exact question asked for.\
Here is the response template:
---
# Response template
- Start with a greeting and a summary of the user's query
- Use bullet points to list the main points or facts that answer the query using the information within the tags <context> and <context/>.
- After answering, analyze the respective source links provided within <ARXIV_ID> and </ARXIV_ID> and keep only the unique links for the next step. Try to minimize the total number of unique links with no more than 10 unique links for the answer.
- You will strictly use no more than 10 most unique links for the answer.
- Use bulleted list of superscript numbers within square brackets to cite the sources for each point or fact. The numbers should correspond to the order of the sources which will be provided in the end of this reponse. Note that for every source, you must provide a URL.
- End with a closing remark and a list of sources with their respective URLs as a bullet list explicitly with full links which are enclosed in the tag <ARXIV_ID> and </ARXIV_ID> respectively.\
---
Here is how an response would look like. Reproduce the same format for your response:
---
# Example response
Hello, thank you for your question about Retrieval Augmented Generation. Here are some key points about RAG:
- Retrieval Augmented Generation is a technique that combines the strengths of pre-trained language models and information retrieval systems to generate responses or content by leveraging external knowledge[^1^] [^2^]
- RAG can be useful when the pre-trained language model alone may not have the necessary information to generate accurate or sufficiently detailed responses, since standard language models like GPT-4 are not capable of accessing real-time or post-training external information directly[^1^] [^3^]
- RAG uses a vector database such as Milvus to index and retrieve relevant documents or text snippets from a knowledge source, and provides them as additional context for the language model[^4^] [^5^]
- RAG can benefit from adding citations to the generated outputs, as it can improve their factual correctness, verifiability, and trustworthiness[^6^] [^7^]
I hope this helps you understand more about RAG.
## Sources
* [^1^][1]: http://arxiv.org/abs/2308.03393v1
* [^2^][2]: http://arxiv.org/abs/2308.03393v1
* [^3^][3]: http://arxiv.org/abs/2307.08593v1
* [^4^][4]: http://arxiv.org/abs/2202.05981v2
* [^5^][5]: http://arxiv.org/abs/2210.09287v1
* [^6^][6]: http://arxiv.org/abs/2242.05981v2
* [^7^][7]: http://arxiv.org/abs/2348.05293v1
---
Where each of the references are taken from the corresponding <ARXIV_ID> in the context. Strictly do not provide title for the references \
Strictly do not repeat the same links. Use the numbers to cite the sources. \
If there is nothing in the context relevant to the question at hand, just say "Hmm, \
I'm not sure." or greet back. Don't try to make up an answer. Write the answer in the form of markdown bullet points.\
Make sure to highlight the most important key words in bold font. Dot repeat any context nor points in the answer.\
Anything between the following `context` html blocks is retrieved from a knowledge \
bank, not part of the conversation with the user. The context are numbered based on its knowledge retrival and increasing cosine similarity index. \
Make sure to consider the order in which they appear context appear. It is an increasing order of cosine similarity index.\
The contents are formatted in latex, you need to remove any special characters and latex formatting before cohercing the points to build your answer.\
Write your answer in the form of markdown bullet points. You can use latex commands if necessary.
You will strictly cite no more than 10 unqiue citations at maximum from the context below.\
Make sure these citations have to be relavant and strictly do not repeat the context in the answer.
<context>
{context}
<context/>
REMEMBER: If there is no relevant information within the context, just say "Hmm, I'm \
not sure." or greet back. Don't try to make up an answer. Anything between the preceding 'context' \
html blocks is retrieved from a knowledge bank, not part of the conversation with the \
user.\
Question: {question}
"""
rag_prompt_custom = PromptTemplate.from_template(response)
from operator import itemgetter
from langchain.schema.runnable import RunnableMap
rag_chain_from_docs = (
{
"context": lambda input: format_docs(input["documents"]),
"question": itemgetter("question"),
}
| rag_prompt_custom
| llm
| StrOutputParser()
)
rag_chain_with_source = RunnableMap(
{"documents": retriever, "question": RunnablePassthrough()}
) | {
"answer": rag_chain_from_docs,
}
st.warning("This project is being continuously developed. Please report any feedback to [email protected]")
col1, col2 = st.columns(2)
with col1:
st.image("https://indico.bnl.gov/event/19560/logo-410523303.png")
with col2:
st.title("AI4EIC Agent")
st.sidebar.title("Data Collection")
if "openai_model" not in st.session_state:
st.session_state["openai_model"] = "gpt-3.5-turbo"
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("What is up?"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
full_response = ""
allchunks = None
with st.spinner("Gathering info from Knowledge Bank and writing response..."):
allchunks = rag_chain_with_source.stream(prompt)
message_placeholder = st.empty()
for chunk in allchunks:
full_response += (chunk.get("answer") or "")
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
st.session_state.messages.append({"role": "assistant", "content": full_response})
| [
"lancedb.connect"
] | [((526, 588), 'lancedb.connect', 'lancedb.connect', (['"""/mnt/d/LLM-Project/my-app/lancedb_meta_data"""'], {}), "('/mnt/d/LLM-Project/my-app/lancedb_meta_data')\n", (541, 588), False, 'import lancedb\n'), ((639, 657), 'langchain_openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (655, 657), False, 'from langchain_openai import OpenAIEmbeddings\n'), ((672, 719), 'langchain_community.vectorstores.LanceDB', 'LanceDB', ([], {'connection': 'table', 'embedding': 'embeddings'}), '(connection=table, embedding=embeddings)\n', (679, 719), False, 'from langchain_community.vectorstores import LanceDB\n'), ((7605, 7643), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_template', (['response'], {}), '(response)\n', (7633, 7643), False, 'from langchain.prompts import PromptTemplate\n'), ((8080, 8196), 'streamlit.warning', 'st.warning', (['"""This project is being continuously developed. Please report any feedback to [email protected]"""'], {}), "(\n 'This project is being continuously developed. 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import os # noqa: D100
import sys
import lancedb
import pyarrow as pa
from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import UnstructuredMarkdownLoader
from langchain_community.vectorstores.lancedb import LanceDB
from langchain_openai.embeddings import OpenAIEmbeddings
load_dotenv()
def initialize_vectorstore() -> LanceDB: # noqa: D103
db = lancedb.connect(os.environ["LANCEDB_DB"])
try:
table = db.open_table(os.environ["LANCEDB_TABLE"])
except FileNotFoundError:
schema = pa.schema(
[
pa.field("vector", pa.list_(pa.float32(), list_size=1536)),
pa.field("id", pa.string()),
pa.field("text", pa.string()),
pa.field("source", pa.string()),
],
)
table = db.create_table(
os.environ["LANCEDB_TABLE"],
schema=schema,
)
embeddings = OpenAIEmbeddings()
return LanceDB(
table,
embeddings,
)
if __name__ == "__main__":
file_path = sys.argv[1]
loader = UnstructuredMarkdownLoader(file_path)
raw_docs = loader.load()
text_splitter = CharacterTextSplitter(
chunk_size=300,
chunk_overlap=30,
)
docs = text_splitter.split_documents(raw_docs)
vectorstore = initialize_vectorstore()
vectorstore.add_documents(docs)
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"lancedb.connect"
] | [((356, 369), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (367, 369), False, 'from dotenv import load_dotenv\n'), ((436, 477), 'lancedb.connect', 'lancedb.connect', (["os.environ['LANCEDB_DB']"], {}), "(os.environ['LANCEDB_DB'])\n", (451, 477), False, 'import lancedb\n'), ((989, 1007), 'langchain_openai.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (1005, 1007), False, 'from langchain_openai.embeddings import OpenAIEmbeddings\n'), ((1020, 1046), 'langchain_community.vectorstores.lancedb.LanceDB', 'LanceDB', (['table', 'embeddings'], {}), '(table, embeddings)\n', (1027, 1046), False, 'from langchain_community.vectorstores.lancedb import LanceDB\n'), ((1140, 1177), 'langchain_community.document_loaders.UnstructuredMarkdownLoader', 'UnstructuredMarkdownLoader', (['file_path'], {}), '(file_path)\n', (1166, 1177), False, 'from langchain_community.document_loaders import UnstructuredMarkdownLoader\n'), ((1228, 1283), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(300)', 'chunk_overlap': '(30)'}), '(chunk_size=300, chunk_overlap=30)\n', (1249, 1283), False, 'from langchain.text_splitter import CharacterTextSplitter\n'), ((725, 736), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (734, 736), True, 'import pyarrow as pa\n'), ((772, 783), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (781, 783), True, 'import pyarrow as pa\n'), ((821, 832), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (830, 832), True, 'import pyarrow as pa\n'), ((662, 674), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (672, 674), True, 'import pyarrow as pa\n')] |
import lancedb
import torch
import pyarrow as pa
import pandas as pd
from pathlib import Path
import tqdm
import numpy as np
import cohere
from os import getenv
from sentence_transformers import SentenceTransformer
# from gradio_app.constants import (DB_TABLE_NAME, VECTOR_COLUMN_NAME, TEXT_COLUMN_NAME, FILES_DUMP_FOLDER)
cohere_embedding_dimensions = {
"embed-english-v3.0": 1024,
"embed-multilingual-v3.0": 1024,
"embed-english-light-v3.0": 384,
"embed-multilingual-light-v3.0": 384,
"embed-english-v2.0": 4096,
"embed-english-light-v2.0": 1024,
"embed-multilingual-v2.0": 768,
}
EMB_MODEL_NAME = "paraphrase-albert-small-v2"
EMB_MODEL_NAME = "thenlper/gte-large"
EMB_MODEL_NAME = "all-MiniLM-L6-v2"
# EMB_MODEL_NAME = "embed-english-v3.0"
# EMB_MODEL_NAME = "all-mpnet-base-v2"
if EMB_MODEL_NAME in ["paraphrase-albert-small-v2", "all-MiniLM-L6-v2", "all-mpnet-base-v2"]:
mode = 'ST'
elif EMB_MODEL_NAME in list(cohere_embedding_dimensions.keys()):
mode = 'COHERE'
else:
mode = None
DB_TABLE_NAME = "split_files_db"
VECTOR_COLUMN_NAME = "vctr"
TEXT_COLUMN_NAME = "txt"
FILES_DUMP_FOLDER = "split_files_dump"
INPUT_DIR = FILES_DUMP_FOLDER
db = lancedb.connect("gradio_app/.lancedb")
batch_size = 32
if mode == 'ST':
model = SentenceTransformer(EMB_MODEL_NAME)
model.eval()
embedding_size = model.get_sentence_embedding_dimension()
elif mode == 'COHERE':
co = cohere.Client(getenv('COHERE_API_KEY'))
embedding_size = cohere_embedding_dimensions[EMB_MODEL_NAME]
else:
embedding_size = None
if torch.backends.mps.is_available():
device = "mps"
elif torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
schema = pa.schema(
[
pa.field(VECTOR_COLUMN_NAME, pa.list_(pa.float32(), embedding_size)),
pa.field(TEXT_COLUMN_NAME, pa.string())
])
tbl = db.create_table(DB_TABLE_NAME, schema=schema, mode="overwrite")
input_dir = Path(INPUT_DIR)
files = list(input_dir.rglob("*"))
sentences = []
for file in files:
with open(file) as f:
sentences.append(f.read())
for i in tqdm.tqdm(range(0, int(np.ceil(len(sentences) / batch_size)))):
try:
batch = [sent for sent in sentences[i * batch_size:(i + 1) * batch_size] if len(sent) > 0]
if mode == 'ST':
encoded = model.encode(batch, normalize_embeddings=True, device=device)
elif mode == 'COHERE':
encoded = np.array(co.embed(batch, input_type="search_document", model="embed-english-v3.0").embeddings)
else:
encoded = None
encoded_lst = [list(vec) for vec in encoded]
df = pd.DataFrame({
VECTOR_COLUMN_NAME: encoded_lst,
TEXT_COLUMN_NAME: batch
})
tbl.add(df)
except Exception as e:
print(f"batch {i} raised an exception: {str(e)}")
'''
create ivf-pd index https://lancedb.github.io/lancedb/ann_indexes/
with the size of the transformer docs, index is not really needed
but we'll do it for demonstrational purposes
'''
tbl.create_index(num_partitions=256, num_sub_vectors=96, vector_column_name=VECTOR_COLUMN_NAME)
| [
"lancedb.connect"
] | [((1193, 1231), 'lancedb.connect', 'lancedb.connect', (['"""gradio_app/.lancedb"""'], {}), "('gradio_app/.lancedb')\n", (1208, 1231), False, 'import lancedb\n'), ((1566, 1599), 'torch.backends.mps.is_available', 'torch.backends.mps.is_available', ([], {}), '()\n', (1597, 1599), False, 'import torch\n'), ((1933, 1948), 'pathlib.Path', 'Path', (['INPUT_DIR'], {}), '(INPUT_DIR)\n', (1937, 1948), False, 'from pathlib import Path\n'), ((1278, 1313), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['EMB_MODEL_NAME'], {}), '(EMB_MODEL_NAME)\n', (1297, 1313), False, 'from sentence_transformers import SentenceTransformer\n'), ((1625, 1650), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (1648, 1650), False, 'import torch\n'), ((2628, 2700), 'pandas.DataFrame', 'pd.DataFrame', (['{VECTOR_COLUMN_NAME: encoded_lst, TEXT_COLUMN_NAME: batch}'], {}), '({VECTOR_COLUMN_NAME: encoded_lst, TEXT_COLUMN_NAME: batch})\n', (2640, 2700), True, 'import pandas as pd\n'), ((1439, 1463), 'os.getenv', 'getenv', (['"""COHERE_API_KEY"""'], {}), "('COHERE_API_KEY')\n", (1445, 1463), False, 'from os import getenv\n'), ((1832, 1843), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1841, 1843), True, 'import pyarrow as pa\n'), ((1767, 1779), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (1777, 1779), True, 'import pyarrow as pa\n')] |
# %%
import hashlib
import pickle
import pandas as pd
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import LanceDB
# from langchain.document_loaders import TextLoader
from langchain_community.document_loaders import UnstructuredMarkdownLoader
from langchain_core.documents import Document
import lancedb
# %%
csv_file = "data/Rob_Burbea_Transcripts.2023-12-31.csv"
df = pd.read_csv(csv_file)
df.columns = df.columns.str.replace(" ", "_").str.lower()
df = df.drop(df.index[0])
# split the transcript_or_writing column into pdf name and create new column
# remove .pdf from pdf_name
df["name"] = df.transcript_or_writing.str.split("/").str[-1].str.replace(".pdf", "")
cols = [
"name",
"date",
"title_of_event",
"title_of_talk_or_writing",
"broad_topics",
"detailed_topics",
"length_of_recording",
"type_of_recording",
]
# %%
def process_documents(
df, cols, start_row=0, text_splitter=None, md_path="data/md_parts/"
):
docs = []
not_processed = []
total_rows = len(df[cols])
for i, row in enumerate(df[cols][start_row:].iterrows(), start=start_row):
fields_from_df = dict(row[1])
markdown_path = f"{md_path}{fields_from_df['name']}.md"
try:
loader = UnstructuredMarkdownLoader(markdown_path, mode="elements")
data = loader.load()
text_chunks = [chunk.page_content for chunk in data]
id = fields_from_df["name"]
hash_value = hashlib.sha1(id.encode()).hexdigest()
fields = {
"id": hash_value,
"chunks": text_chunks,
"title_of_talk_or_writing": fields_from_df["title_of_talk_or_writing"],
}
try:
for x in range(len(data)):
data[x].metadata = fields_from_df
except IndexError:
error_msg = f"File {markdown_path} is empty."
print(error_msg)
not_processed.append(error_msg)
except FileNotFoundError:
error_msg = f"File {markdown_path} not found."
print(error_msg)
not_processed.append(error_msg)
print(f"Processing {i}/{total_rows} md files")
docs.append(fields)
print("Documents created ✨")
return docs, not_processed
# %%
docs, not_processed = process_documents(df, cols)
# %%
# # show the first document structure
for k, v in docs[0].items():
print(k, type(v))
# %%
# right now we have a list of chunks
# we need to separate them into individual documents
def separate_chunks(docs):
separated_docs = []
for doc in docs:
for chunk in doc["chunks"]:
separated_doc = {
"id": doc["id"],
"title_of_talk_or_writing": doc["title_of_talk_or_writing"],
"text": chunk,
}
separated_docs.append(separated_doc)
return separated_docs
separated_docs = separate_chunks(docs)
# %%
# to use the lancedb we need to transform dicts into Document objects
def transform_dicts_to_docs(docs):
documents = []
for item in range(len(docs)):
page = Document(
page_content=docs[item]["text"],
metadata={"title": docs[item]["title_of_talk_or_writing"]},
)
documents.append(page)
return documents
documents = transform_dicts_to_docs(separated_docs)
# %%
# save the documents to disk
with open("data/processed_docs.pickle", "wb") as f:
pickle.dump(documents, f)
# %%
# load the documents from disk
with open("data/processed_docs.pickle", "rb") as f:
documents = pickle.load(f)
# %%
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
# %%
db = lancedb.connect("lancedb")
table = db.create_table(
"dharma_qa",
data=[
{
"vector": embeddings.embed_query("Hello World"),
"text": "Hello World",
# "title": "Doc Title", TODO
# https://lancedb.github.io/lancedb/notebooks/code_qa_bot/
"id": "1",
}
],
mode="overwrite",
)
# %%
docsearch = LanceDB.from_documents(
documents=documents, embedding=embeddings, connection=table
)
# %%
# %%
for k, v in documents[0]:
print(k, type(v))
# %%
documents[0].metadata
# %%
# %%
| [
"lancedb.connect"
] | [((411, 432), 'pandas.read_csv', 'pd.read_csv', (['csv_file'], {}), '(csv_file)\n', (422, 432), True, 'import pandas as pd\n'), ((3679, 3737), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': '"""BAAI/bge-small-en-v1.5"""'}), "(model_name='BAAI/bge-small-en-v1.5')\n", (3700, 3737), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((3748, 3774), 'lancedb.connect', 'lancedb.connect', (['"""lancedb"""'], {}), "('lancedb')\n", (3763, 3774), False, 'import lancedb\n'), ((4129, 4216), 'langchain.vectorstores.LanceDB.from_documents', 'LanceDB.from_documents', ([], {'documents': 'documents', 'embedding': 'embeddings', 'connection': 'table'}), '(documents=documents, embedding=embeddings,\n connection=table)\n', (4151, 4216), False, 'from langchain.vectorstores import LanceDB\n'), ((3516, 3541), 'pickle.dump', 'pickle.dump', (['documents', 'f'], {}), '(documents, f)\n', (3527, 3541), False, 'import pickle\n'), ((3646, 3660), 'pickle.load', 'pickle.load', (['f'], {}), '(f)\n', (3657, 3660), False, 'import pickle\n'), ((3183, 3289), 'langchain_core.documents.Document', 'Document', ([], {'page_content': "docs[item]['text']", 'metadata': "{'title': docs[item]['title_of_talk_or_writing']}"}), "(page_content=docs[item]['text'], metadata={'title': docs[item][\n 'title_of_talk_or_writing']})\n", (3191, 3289), False, 'from langchain_core.documents import Document\n'), ((1285, 1343), 'langchain_community.document_loaders.UnstructuredMarkdownLoader', 'UnstructuredMarkdownLoader', (['markdown_path'], {'mode': '"""elements"""'}), "(markdown_path, mode='elements')\n", (1311, 1343), False, 'from langchain_community.document_loaders import UnstructuredMarkdownLoader\n')] |
import lancedb
import sys
uri = "~/_data/vector/data/sample-lancedb"
db = lancedb.connect(uri)
tnames = db.table_names()
print(sys.path)
table = db.open_table("my_table")
#table = db.create_table("my_table",
# data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
# {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
result = table.search([100, 100]).limit(2).to_df()
print(result) | [
"lancedb.connect"
] | [((75, 95), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (90, 95), False, 'import lancedb\n')] |
import lancedb
import re
import pickle
import requests
import zipfile
import pandas as pd
from pathlib import Path
from langchain.document_loaders import BSHTMLLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import LanceDB
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-MiniLM-L6-v2')
def get_document_title(document):
m = str(document.metadata["source"])
title = re.findall("pandas.documentation(.*).html", m)
if title[0] is not None:
return(title[0])
return ''
def embed_fun(text):
return [model.encode(x) for x in text]
if __name__ == "__main__":
query = ""
docs_path = Path("docs.pkl")
docs = []
if not docs_path.exists():
pandas_docs = requests.get("https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip")
with open('./tmp/pandas.documentation.zip', 'wb') as f:
f.write(pandas_docs.content)
file = zipfile.ZipFile("./tmp/pandas.documentation.zip")
file.extractall(path="./tmp/pandas_docs")
for p in Path("./tmp/pandas_docs/pandas.documentation").rglob("*.html"):
print(p)
if p.is_dir():
continue
loader = BSHTMLLoader(p, open_encoding="utf8")
raw_document = loader.load()
m = {}
m["title"] = get_document_title(raw_document[0])
m["version"] = "2.0rc0"
raw_document[0].metadata = raw_document[0].metadata | m
raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"])
docs = docs + raw_document
with docs_path.open("wb") as fh:
pickle.dump(docs, fh)
else:
with docs_path.open("rb") as fh:
docs = pickle.load(fh)
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
documents = text_splitter.split_documents(docs)
db = lancedb.connect('./tmp/lancedb')
data = [doc.page_content for doc in documents]
data = pd.DataFrame(data, columns=["text"])
print(data)
table = db.create_table("pandas_docs", data, embed_fun)
table = db.open_table("pandas_docs")
print(table.to_pandas())
| [
"lancedb.connect"
] | [((341, 380), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['"""all-MiniLM-L6-v2"""'], {}), "('all-MiniLM-L6-v2')\n", (360, 380), False, 'from sentence_transformers import SentenceTransformer\n'), ((469, 515), 're.findall', 're.findall', (['"""pandas.documentation(.*).html"""', 'm'], {}), "('pandas.documentation(.*).html', m)\n", (479, 515), False, 'import re\n'), ((710, 726), 'pathlib.Path', 'Path', (['"""docs.pkl"""'], {}), "('docs.pkl')\n", (714, 726), False, 'from pathlib import Path\n'), ((1875, 1941), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(1000)', 'chunk_overlap': '(200)'}), '(chunk_size=1000, chunk_overlap=200)\n', (1905, 1941), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((2027, 2059), 'lancedb.connect', 'lancedb.connect', (['"""./tmp/lancedb"""'], {}), "('./tmp/lancedb')\n", (2042, 2059), False, 'import lancedb\n'), ((2123, 2159), 'pandas.DataFrame', 'pd.DataFrame', (['data'], {'columns': "['text']"}), "(data, columns=['text'])\n", (2135, 2159), True, 'import pandas as pd\n'), ((795, 912), 'requests.get', 'requests.get', (['"""https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip"""'], {}), "(\n 'https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip'\n )\n", (807, 912), False, 'import requests\n'), ((1024, 1073), 'zipfile.ZipFile', 'zipfile.ZipFile', (['"""./tmp/pandas.documentation.zip"""'], {}), "('./tmp/pandas.documentation.zip')\n", (1039, 1073), False, 'import zipfile\n'), ((1300, 1337), 'langchain.document_loaders.BSHTMLLoader', 'BSHTMLLoader', (['p'], {'open_encoding': '"""utf8"""'}), "(p, open_encoding='utf8')\n", (1312, 1337), False, 'from langchain.document_loaders import BSHTMLLoader\n'), ((1746, 1767), 'pickle.dump', 'pickle.dump', (['docs', 'fh'], {}), '(docs, fh)\n', (1757, 1767), False, 'import pickle\n'), ((1838, 1853), 'pickle.load', 'pickle.load', (['fh'], {}), '(fh)\n', (1849, 1853), False, 'import pickle\n'), ((1142, 1188), 'pathlib.Path', 'Path', (['"""./tmp/pandas_docs/pandas.documentation"""'], {}), "('./tmp/pandas_docs/pandas.documentation')\n", (1146, 1188), False, 'from pathlib import Path\n')] |
from __future__ import annotations
import json
import os
import time
from typing import Optional
import lancedb
import requests
from openai import OpenAI
# TODO: import this from a shared location (with main.py)
OPENAI_API_KEY = os.environ['OPENAI_API_KEY']
OPENAI_EMBEDDING_MODEL = os.environ['OPENAI_EMBEDDING_MODEL']
LANCEDB_DATA_PATH = os.environ.get('LANCEDB_DATA_PATH')
BUCKET_NAME = os.environ.get('BUCKET_NAME')
# TODO: package this as its own lambda function with it's own dockerfile
# etc - since it doens't need FastAPI/Mangum, etc
db = lancedb.connect(f's3://{BUCKET_NAME}/{LANCEDB_DATA_PATH}')
table = db.open_table('agrifood')
client = OpenAI(api_key=OPENAI_API_KEY)
# TODO: adding typing to function parameters + output
# Function to get use case details
def get_use_case_details(use_case_id):
url = 'https://search.worldbank.org/api/v2/projects'
params = {'id': use_case_id}
response = requests.post(url, params=params)
return response.json() if response.status_code == 200 else None
# Function to get data details
def get_data_details(data_unique_id):
url = 'https://datacatalogapi.worldbank.org/ddhxext/DatasetView'
params = {'dataset_unique_id': data_unique_id}
response = requests.post(url, params=params)
return response.json() if response.status_code == 200 else None
# Function to get data file details
def get_data_file_details(data_file_unique_id):
url = 'https://datacatalogapi.worldbank.org/ddhxext/ResourceView'
params = {'resource_unique_id': data_file_unique_id}
response = requests.post(url, params=params)
return response.json() if response.status_code == 200 else None
# Function to download data file
def download_data_file(data_file_unique_id, version_id):
url = 'https://datacatalogapi.worldbank.org/ddhxext/DownloadResource'
params = {'resource_unique_id': data_file_unique_id, 'version_id': version_id}
response = requests.post(url, params=params)
return response.json() if response.status_code == 200 else None
# Function to open data file
def open_data_file(data_file_unique_id):
url = 'https://datacatalogapi.worldbank.org/ddhxext/OpenResource'
params = {'resource_unique_id': data_file_unique_id}
response = requests.post(url, params=params)
return response.json() if response.status_code == 200 else None
# Function to get embeddings
def get_embedding(text: str):
return (
client.embeddings.create(input=text, model=OPENAI_EMBEDDING_MODEL)
.data[0]
.embedding
)
# Function to get top 10 query results from Pinecone
def get_rag_matches(query: str, datatype: Optional[str] = None, num_results: int = 5):
query_embedding = get_embedding(query)
search_query = table.search(query_embedding).metric('cosine').limit(num_results)
if datatype:
search_query = search_query.where(f"type = '{datatype}'", prefilter=True)
query_response = [
{k: v for k, v in r.items() if (k != 'vector' and v is not None)}
for r in search_query.to_list()
]
print(f'Num results: {len(query_response)}')
print(f'Query response: {query_response}')
return query_response
# rag_matches = [
# r["text_to_embed"] + " Unique ID: " + str(r["id"]) for r in query_response
# ]
# return rag_matches
# TODO: do we need re-ranking?
# # Function to rerank the top 10 query results from Pinecone
# def rerank_rag_matches(query, documents):
# reranked_results = co.rerank(
# query=query, documents=documents, top_n=5, model="rerank-multilingual-v2.0"
# )
# return reranked_results
# Function to search a knowledge base
def search_knowledge_base(query: str, datatype: Optional[str] = None):
# return "\n".join(get_rag_matches(query, datatype))
return get_rag_matches(query, datatype)
# Function to submit tool outputs
def submit_tool_outputs(thread_id, run_id, tool_call_id, output):
client.beta.threads.runs.submit_tool_outputs(
thread_id=thread_id,
run_id=run_id,
tool_outputs=[{'tool_call_id': tool_call_id, 'output': json.dumps(output)}],
)
def format_response(knowledge_base_result, explanations):
if not len(knowledge_base_result) == len(explanations):
raise ValueError('Results and explanations must be the same length')
return [
{
**{k: v for k, v in result.items() if v is not None},
'explanation': explanation,
}
for result, explanation in zip(knowledge_base_result, explanations)
]
# Function mapping
function_mapping = {
'search_knowledge_base': search_knowledge_base,
'format_response': format_response,
'get_use_case_details': get_use_case_details,
'get_data_details': get_data_details,
'get_data_file_details': get_data_file_details,
'download_data_file': download_data_file,
'open_data_file': open_data_file,
}
def process_thread_run(thread_id: str, run_id: str):
run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)
while run.status != 'completed':
print(f'Run status: {run.status}')
if run.status == 'requires_action':
for tool_call in run.required_action.submit_tool_outputs.tool_calls: # type: ignore
# Eventually tool_call.type may be other than
# `function`, at which point we'll need to handle
function_name = tool_call.function.name
arguments = json.loads(tool_call.function.arguments)
if function_name not in function_mapping.keys():
raise Exception(f'Function requested: {function_name} unknown')
print(f'Calling function {function_name} with args: {arguments}')
response = function_mapping[function_name](**arguments) # type: ignore
print(f'Function response: {response}')
submit_tool_outputs(thread_id, run.id, tool_call.id, response)
time.sleep(1)
run = client.beta.threads.runs.retrieve(thread_id=thread_id, run_id=run_id)
print(f'Run status: {run.status}')
def handler(event, context):
# TODO: validate event contains thread_id and run_id
print(f"Processing thread run: {event['thread_id'], event['run_id']}")
process_thread_run(event['thread_id'], event['run_id'])
| [
"lancedb.connect"
] | [((343, 378), 'os.environ.get', 'os.environ.get', (['"""LANCEDB_DATA_PATH"""'], {}), "('LANCEDB_DATA_PATH')\n", (357, 378), False, 'import os\n'), ((393, 422), 'os.environ.get', 'os.environ.get', (['"""BUCKET_NAME"""'], {}), "('BUCKET_NAME')\n", (407, 422), False, 'import os\n'), ((553, 611), 'lancedb.connect', 'lancedb.connect', (['f"""s3://{BUCKET_NAME}/{LANCEDB_DATA_PATH}"""'], {}), "(f's3://{BUCKET_NAME}/{LANCEDB_DATA_PATH}')\n", (568, 611), False, 'import lancedb\n'), ((656, 686), 'openai.OpenAI', 'OpenAI', ([], {'api_key': 'OPENAI_API_KEY'}), '(api_key=OPENAI_API_KEY)\n', (662, 686), False, 'from openai import OpenAI\n'), ((922, 955), 'requests.post', 'requests.post', (['url'], {'params': 'params'}), '(url, params=params)\n', (935, 955), False, 'import requests\n'), ((1230, 1263), 'requests.post', 'requests.post', (['url'], {'params': 'params'}), '(url, params=params)\n', (1243, 1263), False, 'import requests\n'), ((1560, 1593), 'requests.post', 'requests.post', (['url'], {'params': 'params'}), '(url, params=params)\n', (1573, 1593), False, 'import requests\n'), ((1926, 1959), 'requests.post', 'requests.post', (['url'], {'params': 'params'}), '(url, params=params)\n', (1939, 1959), False, 'import requests\n'), ((2242, 2275), 'requests.post', 'requests.post', (['url'], {'params': 'params'}), '(url, params=params)\n', (2255, 2275), False, 'import requests\n'), ((5984, 5997), 'time.sleep', 'time.sleep', (['(1)'], {}), '(1)\n', (5994, 5997), False, 'import time\n'), ((5476, 5516), 'json.loads', 'json.loads', (['tool_call.function.arguments'], {}), '(tool_call.function.arguments)\n', (5486, 5516), False, 'import json\n'), ((4092, 4110), 'json.dumps', 'json.dumps', (['output'], {}), '(output)\n', (4102, 4110), False, 'import json\n')] |
from django.db import models
import os
import openai
import lancedb
import pandas as pd
import pyarrow as pa
from langchain.embeddings import OpenAIEmbeddings
class QABot:
def __init__(self):
self.init_openai()
self.init_database()
def init_openai(self):
os.environ["OPENAI_API_KEY"] = "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"
if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = "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"
openai.api_key = os.environ["OPENAI_API_KEY"]
def init_database(self):
self.ID_COLUMN_NAME = "id"
self.VECTOR_COLUMN_NAME = "vector"
self.QUESTION_COLUMN_NAME = "question"
self.ANSWER_COLUMN_NAME = "answer"
self.table_id_counter = 0
self.embeddings = OpenAIEmbeddings()
self.table = self.create_table()
def create_table(self, name="chat", mode="overwrite"):
db = lancedb.connect("data/lance-cache")
vector_size = 1536
schema = pa.schema([
pa.field(self.ID_COLUMN_NAME, pa.int64()),
pa.field(self.VECTOR_COLUMN_NAME, lancedb.vector(vector_size)),
pa.field(self.QUESTION_COLUMN_NAME, pa.string()),
pa.field(self.ANSWER_COLUMN_NAME, pa.string())])
table = db.create_table(name, schema=schema, mode=mode)
return table
def add_new(self, embeding, question, answer=None):
self.table.add([{self.ID_COLUMN_NAME: self.table_id_counter, self.VECTOR_COLUMN_NAME: embeding, self.QUESTION_COLUMN_NAME: question, self.ANSWER_COLUMN_NAME: answer}])
self.table_id_counter += 1
def delete(self, id):
query = self.ID_COLUMN_NAME + " = " + id
self.table.delete(query)
def search(self, embeding, limit=1, distance=0.15):
if not self.table.to_pandas().empty:
df = self.table.search(embeding).limit(limit).to_df()
if not df.empty and df['_distance'][0] <= distance:
print("Found with distance of " + str(df['_distance'][0]))
return df
return None
def llm_prompt(self, question, engine="text-davinci-003", max_tokens=50):
response = openai.Completion.create(
engine=engine,
prompt=question,
max_tokens=max_tokens
)
answer = response.choices[0].text.strip()
return answer
def get_answer(self, question):
embeding = self.embeddings.embed_query(question)
result = self.search(embeding)
if result is not None:
print(result)
print("Search Result from Cache")
return result[self.ANSWER_COLUMN_NAME].values[0]
else:
answer = self.llm_prompt(question)
self.add_new(embeding, question, answer)
print(question)
print("Search Result from LLM")
return answer
# Define a Django model that incorporates the QABot class
class QABotCache(models.Model):
bot = QABot()
class Meta:
abstract = True # So this model doesn't create a physical database table
@staticmethod
def get_answer(question):
return QABotCache.bot.get_answer(question)
| [
"lancedb.connect",
"lancedb.vector"
] | [((830, 848), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (846, 848), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((963, 998), 'lancedb.connect', 'lancedb.connect', (['"""data/lance-cache"""'], {}), "('data/lance-cache')\n", (978, 998), False, 'import lancedb\n'), ((2222, 2301), 'openai.Completion.create', 'openai.Completion.create', ([], {'engine': 'engine', 'prompt': 'question', 'max_tokens': 'max_tokens'}), '(engine=engine, prompt=question, max_tokens=max_tokens)\n', (2246, 2301), False, 'import openai\n'), ((1097, 1107), 'pyarrow.int64', 'pa.int64', ([], {}), '()\n', (1105, 1107), True, 'import pyarrow as pa\n'), ((1156, 1183), 'lancedb.vector', 'lancedb.vector', (['vector_size'], {}), '(vector_size)\n', (1170, 1183), False, 'import lancedb\n'), ((1234, 1245), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1243, 1245), True, 'import pyarrow as pa\n'), ((1294, 1305), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (1303, 1305), True, 'import pyarrow as pa\n')] |
import lancedb
import pyarrow as pa
import pandas as pd
import numpy as np
import datetime
from time import time, sleep
from uuid import uuid4
import tensorflow_hub as hub
# Load the Universal Sentence Encoder
encoder = hub.load('https://tfhub.dev/google/universal-sentence-encoder/4')
def timestamp_to_datetime(unix_time):
return datetime.datetime.fromtimestamp(unix_time).strftime("%A, %B %d, %Y at %I:%M%p %Z")
initialization_data = {
'unique_id': '2c9a93d5-3631-4faa-8eac-a99b92e45d50',
'vector': [-0.07254597, -0.00345811, 0.038447 , 0.025837 , -0.01153462,
0.05443505, 0.04415885, -0.03636164, 0.04025393, 0.07552634,
0.05359982, 0.00822271, -0.01921194, 0.09719925, -0.05354664,
0.06897003, 0.01113722, 0.06425729, 0.04223888, -0.05898998,
-0.01620383, 0.01389384, 0.02873985, -0.00392985, -0.02874645,
0.02680893, -0.01051578, -0.0792539 , -0.03293172, -0.00302758,
-0.03745122, -0.02573149, -0.00473748, -0.04199643, -0.03275133,
0.00779039, 0.00624639, 0.06108246, -0.03870484, 0.06269313,
-0.06609031, -0.01554973, -0.04453023, -0.00073963, 0.01021871,
-0.02984073, 0.00474442, 0.00195324, -0.02518238, -0.00426692,
0.00750736, 0.10541135, 0.08878568, 0.05580394, -0.01232905,
-0.04016594, 0.04829635, -0.05689557, -0.01863352, 0.03308525,
0.06468356, -0.03367596, 0.03575945, -0.02212196, -0.01714826,
-0.00585904, -0.09612011, -0.00102483, 0.06920582, 0.05855923,
-0.04266937, -0.03763324, -0.02187943, -0.00141346, -0.086646 ,
0.02106668, 0.00786448, 0.04093482, -0.00187637, 0.02952651,
-0.03702659, -0.02844533, 0.00322303, -0.02380866, -0.05954637,
0.07149482, -0.0065098 , 0.06807149, -0.00099369, 0.05040864,
0.04761266, 0.01862198, -0.05431763, 0.00940712, -0.00970824,
-0.02216387, 0.024306 , 0.03772607, -0.01540066, 0.03771403,
0.01400787, -0.09354229, -0.06321603, -0.09549774, 0.00895245,
-0.01175102, 0.03934404, 0.00956635, -0.04152715, 0.04295438,
0.02825363, 0.02063269, 0.02212336, -0.06888197, 0.01428573,
0.04887657, 0.00304061, 0.03196091, 0.03902192, 0.02360773,
-0.02807535, 0.01558309, 0.02165642, 0.01129555, 0.0567826 ,
-0.00659211, -0.01081236, 0.01809447, 0.00318123, -0.01214105,
-0.05691559, -0.01717793, 0.05293235, 0.01663713, 0.04678147,
-0.02094 , -0.05482098, 0.05463412, 0.00163532, 0.00956752,
-0.03624124, -0.02359207, 0.01571903, -0.01502842, 0.03324307,
0.01896691, 0.02235259, 0.02551061, -0.02953271, 0.05505196,
-0.03115846, -0.01975026, -0.05484571, -0.01757487, -0.01038232,
-0.06098176, -0.01663185, -0.06602633, -0.00643233, 0.00167366,
-0.04243006, 0.01024193, -0.02288529, -0.06190364, 0.03787598,
0.03914008, -0.04915332, 0.0182827 , 0.0136188 , 0.02917461,
0.03118066, -0.03110682, -0.04193405, -0.01370175, -0.03901035,
0.00850587, 0.01056607, -0.00084098, -0.01737773, 0.00836137,
0.01500763, 0.00917414, -0.07946376, 0.02008886, 0.04600394,
0.01271509, -0.01654603, -0.04405601, 0.01442427, 0.00967625,
0.01212494, 0.01189141, 0.03507042, -0.00291006, 0.04226362,
-0.0958102 , 0.04722575, -0.02520623, -0.00780957, -0.01983704,
-0.02350736, -0.03137485, 0.00325953, 0.10679087, -0.08251372,
0.02922777, -0.05723861, -0.05683867, -0.04093323, -0.04769454,
-0.02704669, -0.04450696, 0.03854201, 0.05599346, -0.07225747,
-0.01060745, -0.01285277, -0.02004824, 0.00567907, -0.01130959,
0.03845671, -0.06483931, -0.00013804, 0.00342195, -0.00497795,
0.03194252, 0.06014316, 0.07774884, -0.02778566, -0.06470748,
0.02103901, 0.02202238, 0.02044025, 0.10802107, 0.00356093,
-0.01817842, 0.09661267, -0.05937773, -0.08208849, -0.05190327,
-0.0302214 , 0.05572621, -0.06395542, -0.03078226, 0.00083952,
0.09572925, -0.04516173, -0.0123177 , 0.09613901, -0.05666108,
-0.00537586, 0.04220096, 0.00019196, 0.00295547, -0.07350546,
-0.00707971, -0.01553643, -0.05214835, 0.00311794, 0.00742682,
-0.02943217, 0.06675503, 0.04113274, -0.0809793 , 0.03398148,
0.01721729, 0.03014007, -0.04178908, 0.01025263, 0.03336379,
0.05700357, 0.10388609, 0.00663307, -0.05146715, -0.02173147,
-0.02297893, -0.01923811, 0.03292958, 0.0521661 , 0.03923552,
0.01330443, 0.02524009, 0.06507587, -0.01531762, -0.04601574,
0.0499142 , 0.06374968, 0.06080135, -0.08060206, 0.03382473,
-0.03596291, -0.06714796, -0.08815136, 0.02092835, 0.10282409,
0.07779143, -0.01839681, -0.03541641, 0.00666599, 0.0029895 ,
-0.08307225, -0.06535257, 0.01114002, -0.06142527, -0.01779631,
0.04441926, 0.02008377, 0.03211711, -0.02073815, -0.01346437,
0.02578364, -0.01888524, 0.03310522, -0.02017466, 0.0198052 ,
-0.01019527, -0.02200533, -0.02650121, -0.02987311, -0.04946938,
-0.05915657, -0.0779579 , 0.03368903, 0.01859711, 0.02692219,
0.04209578, -0.01279042, -0.00151735, -0.03290961, 0.00719433,
-0.05409581, 0.04818217, -0.00339916, 0.01444317, -0.04898094,
-0.02065373, -0.04324449, -0.01409152, -0.02882394, 0.0129813 ,
-0.03886433, -0.08824961, 0.02457459, -0.03383131, 0.04405662,
0.03947931, 0.02983763, 0.00124698, 0.01098392, 0.05948395,
0.08565806, 0.02848131, -0.00725272, -0.04415287, -0.03293212,
-0.01364554, -0.09744117, -0.05662472, 0.03124948, -0.04624591,
-0.00605065, -0.06229377, 0.08636316, -0.03645795, 0.08642905,
0.03093746, -0.08031843, 0.01407037, 0.09892832, 0.03219265,
0.02964027, -0.00517425, -0.03442131, -0.01141241, -0.06644958,
-0.07285954, 0.00890575, -0.01360151, 0.00057073, -0.08988309,
0.00797763, 0.0176619 , 0.00745209, -0.07096376, 0.07894821,
-0.08301938, 0.0990236 , 0.03789177, -0.01905026, 0.0547296 ,
-0.06224509, 0.01964617, 0.08179896, -0.0852924 , 0.00475453,
-0.01451678, 0.03582037, -0.04732088, -0.041508 , 0.05553002,
-0.00753875, -0.02849884, 0.04659286, -0.05146529, -0.0661836 ,
-0.00761966, 0.01581906, 0.02444271, -0.01438573, -0.03466942,
-0.06876651, -0.02311521, -0.00312491, 0.03457906, -0.04614082,
0.03010868, 0.0206049 , 0.08378315, -0.03001363, -0.00827654,
0.01580172, -0.04855691, 0.00014473, -0.01702366, 0.06371997,
0.00924862, -0.01441237, 0.0184262 , 0.03586025, 0.07453281,
-0.01822053, 0.00263505, -0.07093351, -0.02956585, 0.0937797 ,
-0.03792839, 0.03657963, -0.01717029, 0.0077794 , 0.06886019,
0.04470135, 0.04228634, 0.06212147, -0.05456647, -0.02041842,
0.02251387, 0.06653161, -0.00503211, 0.03463385, -0.02718318,
0.00118317, -0.02953942, -0.04361469, 0.01001209, 0.01472133,
-0.07398187, 0.00152049, -0.02058817, -0.03011479, -0.03247686,
-0.03999605, 0.00089937, 0.06058171, -0.1016895 , 0.07500667,
0.03293885, -0.05828201, -0.01353116, 0.06867946, -0.03266895,
-0.02314214, 0.03284731, 0.02857622, 0.05733896, 0.05395727,
0.06677917, -0.01256167, 0.01832761, 0.01509516, 0.08785269,
-0.01094873, -0.09930896, -0.00904166, 0.01920987, 0.01392063,
-0.03855692, 0.04157091, -0.05284394, 0.01217607, -0.00495155,
-0.02351189, 0.03753581, 0.03075539, 0.0635642 , 0.05873286,
0.00987345, 0.05255824, -0.08698288, 0.10400596, -0.00647114,
-0.00831464, 0.0055213 , 0.01613558, -0.10711982, 0.00563591,
0.03591603, 0.00221161, -0.01541905, -0.0879847 , -0.05289326,
-0.04107964, -0.04039652],
'speaker': 'USER',
'time': 1695146425.0193892,
'message': 'this is a test.',
'timestring': 'Tuesday, September 19, 2023 at 02:00PM '
}
database_schema = pa.schema([
pa.field("unique_id", pa.string()),
pa.field("vector", pa.list_(pa.float32())),
pa.field("speaker", pa.string()),
pa.field("time", pa.float64()),
pa.field("message", pa.string()),
pa.field("timestring", pa.string()),
])
vector_np = np.array( initialization_data[ "vector" ], dtype=np.float32 )
# flattened_input = original_list.flatten().tolist()
# original_list[ "vector" ] = flattened_input
dataframe = pd.DataFrame([ initialization_data ])
# arrow_table = pa.Table.from_pandas(dataframe, database_schema)
hi_user_input = encoder([ "hi" ])
initialization_data = {
'unique_id': '2c9a93d5-3631-4faa-8eac-a99b92e45d50',
'vector': dataframe,
'speaker': 'USER',
'time': 1695146425.0193892,
'message': 'this is a test.',
'timestring': 'Tuesday, September 19, 2023 at 02:00PM '
}
# Initialize lancedb
lance_database = lancedb.connect("/tmp/fresh-lancedb")
table_name = "lance-table" # Create the table with the defined schema
if table_name in lance_database.table_names():
print( "table %s already exists" % table_name )
lance_database.drop_table(table_name) # Drop the table if it already exists
# lance_table = lance_database.create_table( table_name, schema=database_schema )
dataframe = pd.DataFrame([ initialization_data ])
arrow_table = pa.Table.from_pandas(dataframe, schema=database_schema )
lance_table = lance_database.create_table( table_name, schema=database_schema, data=arrow_table )
else:
print( "creating table: %s" % table_name )
# lance_table = lance_database.create_table( table_name, schema=database_schema )
dataframe = pd.DataFrame([ initialization_data ])
arrow_table = pa.Table.from_pandas(dataframe, schema=database_schema )
lance_table = lance_database.create_table( table_name, schema=database_schema, data=arrow_table )
uri = "/tmp/fresh-lancedb"
lance_database = lancedb.connect(uri)
# user_input = input('\n\nUSER: ')
user_input = "hi"
timestamp = time()
timestring = timestamp_to_datetime(timestamp)
unique_id = str(uuid4())
embedded_user_input = encoder([ user_input ]).numpy() # Convert the text into vector form
# embedded_user_input = gpt3_embedding(ai_completion_text)
unique_id = str(uuid4())
speaker = 'RAVEN'
message = user_input
embedded_user_input = np.array( embedded_user_input )
flattened_input = [float(item) for item in embedded_user_input.flatten().tolist()]
result = lance_table.search( flattened_input ).limit(2).to_df()
print( result )
# stay open!!!!!!!!!!
# table = lance_database.create_table("my_table",
# data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
# {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
# lance_table = lance_database.open_table( "lance-table" ) | [
"lancedb.connect"
] | [((221, 286), 'tensorflow_hub.load', 'hub.load', (['"""https://tfhub.dev/google/universal-sentence-encoder/4"""'], {}), "('https://tfhub.dev/google/universal-sentence-encoder/4')\n", (229, 286), True, 'import tensorflow_hub as hub\n'), ((8432, 8489), 'numpy.array', 'np.array', (["initialization_data['vector']"], {'dtype': 'np.float32'}), "(initialization_data['vector'], dtype=np.float32)\n", (8440, 8489), True, 'import numpy as np\n'), ((8605, 8640), 'pandas.DataFrame', 'pd.DataFrame', (['[initialization_data]'], {}), '([initialization_data])\n', (8617, 8640), True, 'import pandas as pd\n'), ((9044, 9081), 'lancedb.connect', 'lancedb.connect', (['"""/tmp/fresh-lancedb"""'], {}), "('/tmp/fresh-lancedb')\n", (9059, 9081), False, 'import lancedb\n'), ((10078, 10098), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (10093, 10098), False, 'import lancedb\n'), ((10165, 10171), 'time.time', 'time', ([], {}), '()\n', (10169, 10171), False, 'from time import time, sleep\n'), ((10488, 10517), 'numpy.array', 'np.array', (['embedded_user_input'], {}), '(embedded_user_input)\n', (10496, 10517), True, 'import numpy as np\n'), ((9436, 9471), 'pandas.DataFrame', 'pd.DataFrame', (['[initialization_data]'], {}), '([initialization_data])\n', (9448, 9471), True, 'import pandas as pd\n'), ((9492, 9547), 'pyarrow.Table.from_pandas', 'pa.Table.from_pandas', (['dataframe'], {'schema': 'database_schema'}), '(dataframe, schema=database_schema)\n', (9512, 9547), True, 'import pyarrow as pa\n'), ((9806, 9841), 'pandas.DataFrame', 'pd.DataFrame', (['[initialization_data]'], {}), '([initialization_data])\n', (9818, 9841), True, 'import pandas as pd\n'), ((9862, 9917), 'pyarrow.Table.from_pandas', 'pa.Table.from_pandas', (['dataframe'], {'schema': 'database_schema'}), '(dataframe, schema=database_schema)\n', (9882, 9917), True, 'import pyarrow as pa\n'), ((10234, 10241), 'uuid.uuid4', 'uuid4', ([], {}), '()\n', (10239, 10241), False, 'from uuid import uuid4\n'), ((10415, 10422), 'uuid.uuid4', 'uuid4', ([], {}), '()\n', (10420, 10422), False, 'from uuid import uuid4\n'), ((337, 379), 'datetime.datetime.fromtimestamp', 'datetime.datetime.fromtimestamp', (['unix_time'], {}), '(unix_time)\n', (368, 379), False, 'import datetime\n'), ((8201, 8212), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (8210, 8212), True, 'import pyarrow as pa\n'), ((8287, 8298), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (8296, 8298), True, 'import pyarrow as pa\n'), ((8322, 8334), 'pyarrow.float64', 'pa.float64', ([], {}), '()\n', (8332, 8334), True, 'import pyarrow as pa\n'), ((8361, 8372), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (8370, 8372), True, 'import pyarrow as pa\n'), ((8402, 8413), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (8411, 8413), True, 'import pyarrow as pa\n'), ((8247, 8259), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (8257, 8259), True, 'import pyarrow as pa\n')] |
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from functools import lru_cache
import lancedb
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from sentence_transformers import SentenceTransformer
from api.config import Settings
from api.routers.rest import router
@lru_cache()
def get_settings():
# Use lru_cache to avoid loading .env file for every request
return Settings()
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Async context manager for lancedb connection."""
settings = get_settings()
model_checkpoint = settings.embedding_model_checkpoint
app.model = SentenceTransformer(model_checkpoint)
# Define LanceDB client
db = lancedb.connect("./winemag")
app.table = db.open_table("wines")
print("Successfully connected to LanceDB")
yield
print("Successfully closed LanceDB connection and released resources")
app = FastAPI(
title="REST API for wine reviews on LanceDB",
description=(
"Query from a LanceDB database of 130k wine reviews from the Wine Enthusiast magazine"
),
version=get_settings().tag,
lifespan=lifespan,
)
@app.get("/", include_in_schema=False)
async def root():
return {
"message": "REST API for querying LanceDB database of 130k wine reviews from the Wine Enthusiast magazine"
}
# Attach routes
app.include_router(router, prefix="/wine", tags=["wine"])
# Add CORS middleware
app.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:8000"],
allow_methods=["GET"],
allow_headers=["*"],
)
| [
"lancedb.connect"
] | [((339, 350), 'functools.lru_cache', 'lru_cache', ([], {}), '()\n', (348, 350), False, 'from functools import lru_cache\n'), ((447, 457), 'api.config.Settings', 'Settings', ([], {}), '()\n', (455, 457), False, 'from api.config import Settings\n'), ((706, 743), 'sentence_transformers.SentenceTransformer', 'SentenceTransformer', (['model_checkpoint'], {}), '(model_checkpoint)\n', (725, 743), False, 'from sentence_transformers import SentenceTransformer\n'), ((781, 809), 'lancedb.connect', 'lancedb.connect', (['"""./winemag"""'], {}), "('./winemag')\n", (796, 809), False, 'import lancedb\n')] |
# Ultralytics YOLO 🚀, AGPL-3.0 license
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import cv2
import numpy as np
import torch
from PIL import Image
from matplotlib import pyplot as plt
from pandas import DataFrame
from tqdm import tqdm
from ultralytics.data.augment import Format
from ultralytics.data.dataset import YOLODataset
from ultralytics.data.utils import check_det_dataset
from ultralytics.models.yolo.model import YOLO
from ultralytics.utils import LOGGER, IterableSimpleNamespace, checks, USER_CONFIG_DIR
from .utils import get_sim_index_schema, get_table_schema, plot_query_result, prompt_sql_query, sanitize_batch
class ExplorerDataset(YOLODataset):
def __init__(self, *args, data: dict = None, **kwargs) -> None:
super().__init__(*args, data=data, **kwargs)
def load_image(self, i: int) -> Union[Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]], Tuple[None, None, None]]:
"""Loads 1 image from dataset index 'i' without any resize ops."""
im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
if im is None: # not cached in RAM
if fn.exists(): # load npy
im = np.load(fn)
else: # read image
im = cv2.imread(f) # BGR
if im is None:
raise FileNotFoundError(f"Image Not Found {f}")
h0, w0 = im.shape[:2] # orig hw
return im, (h0, w0), im.shape[:2]
return self.ims[i], self.im_hw0[i], self.im_hw[i]
def build_transforms(self, hyp: IterableSimpleNamespace = None):
"""Creates transforms for dataset images without resizing."""
return Format(
bbox_format="xyxy",
normalize=False,
return_mask=self.use_segments,
return_keypoint=self.use_keypoints,
batch_idx=True,
mask_ratio=hyp.mask_ratio,
mask_overlap=hyp.overlap_mask,
)
class Explorer:
def __init__(
self,
data: Union[str, Path] = "coco128.yaml",
model: str = "yolov8n.pt",
uri: str = USER_CONFIG_DIR / "explorer",
) -> None:
checks.check_requirements(["lancedb>=0.4.3", "duckdb"])
import lancedb
self.connection = lancedb.connect(uri)
self.table_name = Path(data).name.lower() + "_" + model.lower()
self.sim_idx_base_name = (
f"{self.table_name}_sim_idx".lower()
) # Use this name and append thres and top_k to reuse the table
self.model = YOLO(model)
self.data = data # None
self.choice_set = None
self.table = None
self.progress = 0
def create_embeddings_table(self, force: bool = False, split: str = "train") -> None:
"""
Create LanceDB table containing the embeddings of the images in the dataset. The table will be reused if it
already exists. Pass force=True to overwrite the existing table.
Args:
force (bool): Whether to overwrite the existing table or not. Defaults to False.
split (str): Split of the dataset to use. Defaults to 'train'.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
```
"""
if self.table is not None and not force:
LOGGER.info("Table already exists. Reusing it. Pass force=True to overwrite it.")
return
if self.table_name in self.connection.table_names() and not force:
LOGGER.info(f"Table {self.table_name} already exists. Reusing it. Pass force=True to overwrite it.")
self.table = self.connection.open_table(self.table_name)
self.progress = 1
return
if self.data is None:
raise ValueError("Data must be provided to create embeddings table")
data_info = check_det_dataset(self.data)
if split not in data_info:
raise ValueError(
f"Split {split} is not found in the dataset. Available keys in the dataset are {list(data_info.keys())}"
)
choice_set = data_info[split]
choice_set = choice_set if isinstance(choice_set, list) else [choice_set]
self.choice_set = choice_set
dataset = ExplorerDataset(img_path=choice_set, data=data_info, augment=False, cache=False, task=self.model.task)
# Create the table schema
batch = dataset[0]
vector_size = self.model.embed(batch["im_file"], verbose=False)[0].shape[0]
table = self.connection.create_table(self.table_name, schema=get_table_schema(vector_size), mode="overwrite")
table.add(
self._yield_batches(
dataset,
data_info,
self.model,
exclude_keys=["img", "ratio_pad", "resized_shape", "ori_shape", "batch_idx"],
)
)
self.table = table
def _yield_batches(self, dataset: ExplorerDataset, data_info: dict, model: YOLO, exclude_keys: List[str]):
"""Generates batches of data for embedding, excluding specified keys."""
for i in tqdm(range(len(dataset))):
self.progress = float(i + 1) / len(dataset)
batch = dataset[i]
for k in exclude_keys:
batch.pop(k, None)
batch = sanitize_batch(batch, data_info)
batch["vector"] = model.embed(batch["im_file"], verbose=False)[0].detach().tolist()
yield [batch]
def query(
self, imgs: Union[str, np.ndarray, List[str], List[np.ndarray]] = None, limit: int = 25
) -> Any: # pyarrow.Table
"""
Query the table for similar images. Accepts a single image or a list of images.
Args:
imgs (str or list): Path to the image or a list of paths to the images.
limit (int): Number of results to return.
Returns:
(pyarrow.Table): An arrow table containing the results. Supports converting to:
- pandas dataframe: `result.to_pandas()`
- dict of lists: `result.to_pydict()`
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.query(img='https://ultralytics.com/images/zidane.jpg')
```
"""
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
if isinstance(imgs, str):
imgs = [imgs]
assert isinstance(imgs, list), f"img must be a string or a list of strings. Got {type(imgs)}"
embeds = self.model.embed(imgs)
# Get avg if multiple images are passed (len > 1)
embeds = torch.mean(torch.stack(embeds), 0).cpu().numpy() if len(embeds) > 1 else embeds[0].cpu().numpy()
return self.table.search(embeds).limit(limit).to_arrow()
def sql_query(
self, query: str, return_type: str = "pandas"
) -> Union[DataFrame, Any, None]: # pandas.dataframe or pyarrow.Table
"""
Run a SQL-Like query on the table. Utilizes LanceDB predicate pushdown.
Args:
query (str): SQL query to run.
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
(pyarrow.Table): An arrow table containing the results.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
query = "SELECT * FROM 'table' WHERE labels LIKE '%person%'"
result = exp.sql_query(query)
```
"""
assert return_type in {
"pandas",
"arrow",
}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
import duckdb
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
# Note: using filter pushdown would be a better long term solution. Temporarily using duckdb for this.
table = self.table.to_arrow() # noqa NOTE: Don't comment this. This line is used by DuckDB
if not query.startswith("SELECT") and not query.startswith("WHERE"):
raise ValueError(
f"Query must start with SELECT or WHERE. You can either pass the entire query or just the WHERE clause. found {query}"
)
if query.startswith("WHERE"):
query = f"SELECT * FROM 'table' {query}"
LOGGER.info(f"Running query: {query}")
rs = duckdb.sql(query)
if return_type == "arrow":
return rs.arrow()
elif return_type == "pandas":
return rs.df()
def plot_sql_query(self, query: str, labels: bool = True) -> Image.Image:
"""
Plot the results of a SQL-Like query on the table.
Args:
query (str): SQL query to run.
labels (bool): Whether to plot the labels or not.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
query = "SELECT * FROM 'table' WHERE labels LIKE '%person%'"
result = exp.plot_sql_query(query)
```
"""
result = self.sql_query(query, return_type="arrow")
if len(result) == 0:
LOGGER.info("No results found.")
return None
img = plot_query_result(result, plot_labels=labels)
return Image.fromarray(img)
def get_similar(
self,
img: Union[str, np.ndarray, List[str], List[np.ndarray]] = None,
idx: Union[int, List[int]] = None,
limit: int = 25,
return_type: str = "pandas",
) -> Union[DataFrame, Any]: # pandas.dataframe or pyarrow.Table
"""
Query the table for similar images. Accepts a single image or a list of images.
Args:
img (str or list): Path to the image or a list of paths to the images.
idx (int or list): Index of the image in the table or a list of indexes.
limit (int): Number of results to return. Defaults to 25.
return_type (str): Type of the result to return. Can be either 'pandas' or 'arrow'. Defaults to 'pandas'.
Returns:
(pandas.DataFrame): A dataframe containing the results.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.get_similar(img='https://ultralytics.com/images/zidane.jpg')
```
"""
assert return_type in {
"pandas",
"arrow",
}, f"Return type should be either `pandas` or `arrow`, but got {return_type}"
img = self._check_imgs_or_idxs(img, idx)
similar = self.query(img, limit=limit)
if return_type == "arrow":
return similar
elif return_type == "pandas":
return similar.to_pandas()
def plot_similar(
self,
img: Union[str, np.ndarray, List[str], List[np.ndarray]] = None,
idx: Union[int, List[int]] = None,
limit: int = 25,
labels: bool = True,
) -> Image.Image:
"""
Plot the similar images. Accepts images or indexes.
Args:
img (str or list): Path to the image or a list of paths to the images.
idx (int or list): Index of the image in the table or a list of indexes.
labels (bool): Whether to plot the labels or not.
limit (int): Number of results to return. Defaults to 25.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similar = exp.plot_similar(img='https://ultralytics.com/images/zidane.jpg')
```
"""
similar = self.get_similar(img, idx, limit, return_type="arrow")
if len(similar) == 0:
LOGGER.info("No results found.")
return None
img = plot_query_result(similar, plot_labels=labels)
return Image.fromarray(img)
def similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> DataFrame:
"""
Calculate the similarity index of all the images in the table. Here, the index will contain the data points that
are max_dist or closer to the image in the embedding space at a given index.
Args:
max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
top_k (float): Percentage of the closest data points to consider when counting. Used to apply limit when running
vector search. Defaults: None.
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
(pandas.DataFrame): A dataframe containing the similarity index. Each row corresponds to an image, and columns
include indices of similar images and their respective distances.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
sim_idx = exp.similarity_index()
```
"""
if self.table is None:
raise ValueError("Table is not created. Please create the table first.")
sim_idx_table_name = f"{self.sim_idx_base_name}_thres_{max_dist}_top_{top_k}".lower()
if sim_idx_table_name in self.connection.table_names() and not force:
LOGGER.info("Similarity matrix already exists. Reusing it. Pass force=True to overwrite it.")
return self.connection.open_table(sim_idx_table_name).to_pandas()
if top_k and not (1.0 >= top_k >= 0.0):
raise ValueError(f"top_k must be between 0.0 and 1.0. Got {top_k}")
if max_dist < 0.0:
raise ValueError(f"max_dist must be greater than 0. Got {max_dist}")
top_k = int(top_k * len(self.table)) if top_k else len(self.table)
top_k = max(top_k, 1)
features = self.table.to_lance().to_table(columns=["vector", "im_file"]).to_pydict()
im_files = features["im_file"]
embeddings = features["vector"]
sim_table = self.connection.create_table(sim_idx_table_name, schema=get_sim_index_schema(), mode="overwrite")
def _yield_sim_idx():
"""Generates a dataframe with similarity indices and distances for images."""
for i in tqdm(range(len(embeddings))):
sim_idx = self.table.search(embeddings[i]).limit(top_k).to_pandas().query(f"_distance <= {max_dist}")
yield [
{
"idx": i,
"im_file": im_files[i],
"count": len(sim_idx),
"sim_im_files": sim_idx["im_file"].tolist(),
}
]
sim_table.add(_yield_sim_idx())
self.sim_index = sim_table
return sim_table.to_pandas()
def plot_similarity_index(self, max_dist: float = 0.2, top_k: float = None, force: bool = False) -> Image:
"""
Plot the similarity index of all the images in the table. Here, the index will contain the data points that are
max_dist or closer to the image in the embedding space at a given index.
Args:
max_dist (float): maximum L2 distance between the embeddings to consider. Defaults to 0.2.
top_k (float): Percentage of closest data points to consider when counting. Used to apply limit when
running vector search. Defaults to 0.01.
force (bool): Whether to overwrite the existing similarity index or not. Defaults to True.
Returns:
(PIL.Image): Image containing the plot.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
similarity_idx_plot = exp.plot_similarity_index()
similarity_idx_plot.show() # view image preview
similarity_idx_plot.save('path/to/save/similarity_index_plot.png') # save contents to file
```
"""
sim_idx = self.similarity_index(max_dist=max_dist, top_k=top_k, force=force)
sim_count = sim_idx["count"].tolist()
sim_count = np.array(sim_count)
indices = np.arange(len(sim_count))
# Create the bar plot
plt.bar(indices, sim_count)
# Customize the plot (optional)
plt.xlabel("data idx")
plt.ylabel("Count")
plt.title("Similarity Count")
buffer = BytesIO()
plt.savefig(buffer, format="png")
buffer.seek(0)
# Use Pillow to open the image from the buffer
return Image.fromarray(np.array(Image.open(buffer)))
def _check_imgs_or_idxs(
self, img: Union[str, np.ndarray, List[str], List[np.ndarray], None], idx: Union[None, int, List[int]]
) -> List[np.ndarray]:
if img is None and idx is None:
raise ValueError("Either img or idx must be provided.")
if img is not None and idx is not None:
raise ValueError("Only one of img or idx must be provided.")
if idx is not None:
idx = idx if isinstance(idx, list) else [idx]
img = self.table.to_lance().take(idx, columns=["im_file"]).to_pydict()["im_file"]
return img if isinstance(img, list) else [img]
def ask_ai(self, query):
"""
Ask AI a question.
Args:
query (str): Question to ask.
Returns:
(pandas.DataFrame): A dataframe containing filtered results to the SQL query.
Example:
```python
exp = Explorer()
exp.create_embeddings_table()
answer = exp.ask_ai('Show images with 1 person and 2 dogs')
```
"""
result = prompt_sql_query(query)
try:
df = self.sql_query(result)
except Exception as e:
LOGGER.error("AI generated query is not valid. Please try again with a different prompt")
LOGGER.error(e)
return None
return df
def visualize(self, result):
"""
Visualize the results of a query. TODO.
Args:
result (pyarrow.Table): Table containing the results of a query.
"""
pass
def generate_report(self, result):
"""
Generate a report of the dataset.
TODO
"""
pass
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"""
FastAPI app to serve search endpoints
"""
import asyncio
from collections.abc import AsyncGenerator
from concurrent.futures import ThreadPoolExecutor
from contextlib import asynccontextmanager
from functools import lru_cache
from config import Settings
from fastapi import FastAPI, HTTPException, Query, Request
from schemas.wine import SearchResult
from sentence_transformers import SentenceTransformer
import lancedb
executor = ThreadPoolExecutor(max_workers=4)
@lru_cache()
def get_settings():
# Use lru_cache to avoid loading .env file for every request
return Settings()
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Async context manager for lancedb connection."""
settings = get_settings()
model_checkpoint = settings.embedding_model_checkpoint
app.model = SentenceTransformer(model_checkpoint)
# Define LanceDB client
db = lancedb.connect("./winemag")
app.table = db.open_table("wines")
print("Successfully connected to LanceDB")
yield
print("Successfully closed LanceDB connection and released resources")
app = FastAPI(
title="REST API for wine reviews on LanceDB",
description=(
"Query from a LanceDB database of 130k wine reviews from the Wine Enthusiast magazine"
),
version="0.1.0",
lifespan=lifespan,
)
# --- app ---
@app.get("/", include_in_schema=False)
async def root():
return {
"message": "REST API for querying LanceDB database of 130k wine reviews from the Wine Enthusiast magazine"
}
# --- Search functions ---
def _fts_search(request: Request, terms: str) -> list[SearchResult] | None:
# In FTS, we limit to a max of 10K points to be more in line with Elasticsearch
search_result = (
request.app.table.search(terms, vector_column_name="description")
.select(["id", "title", "description", "country", "variety", "price", "points"])
.limit(10)
).to_pydantic(SearchResult)
if not search_result:
return None
return search_result
def _vector_search(
request: Request,
terms: str,
) -> list[SearchResult] | None:
query_vector = request.app.model.encode(terms.lower())
search_result = (
request.app.table.search(query_vector)
.metric("cosine")
.nprobes(20)
.select(["id", "title", "description", "country", "variety", "price", "points"])
.limit(10)
).to_pydantic(SearchResult)
if not search_result:
return None
return search_result
# --- Endpoints ---
@app.get(
"/fts_search",
response_model=list[SearchResult],
response_description="Search for wines via full-text keywords",
)
async def fts_search(
request: Request,
query: str = Query(
description="Specify terms to search for in the variety, title and description"
),
) -> list[SearchResult] | None:
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(executor, _fts_search, request, query)
if not result:
raise HTTPException(
status_code=404,
detail=f"No wine with the provided terms '{query}' found in database - please try again",
)
return result
@app.get(
"/vector_search",
response_model=list[SearchResult],
response_description="Search for wines via semantically similar terms",
)
async def vector_search(
request: Request,
query: str = Query(
description="Specify terms to search for in the variety, title and description"
),
) -> list[SearchResult] | None:
loop = asyncio.get_running_loop()
result = await loop.run_in_executor(executor, _vector_search, request, query)
if not result:
raise HTTPException(
status_code=404,
detail=f"No wine with the provided terms '{query}' found in database - please try again",
)
return result
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"lancedb.connect"
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from flask import Flask, jsonify, make_response, abort, request, send_file
import requests
import json
from langchain.vectorstores import LanceDB
import lancedb
import langchain
from YaGPT import YaGPTEmbeddings, YandexLLM
import uuid
from speechkit import model_repository, configure_credentials, creds
from speechkit.stt import AudioProcessingType
cert = "/home/vmuser/cert/fullchain.pem"
cert_key = "/home/vmuser/cert/privkey.pem"
temp_dir = "/home/vmuser/temp"
db_dir = "/home/vmuser/store"
send_audio = True
app = Flask(__name__)
def synth(txt):
model = model_repository.synthesis_model()
model.voice = 'jane'
model.role = 'good'
result = model.synthesize(txt,raw_format=False)
fn = f"/home/vmuser/temp/{uuid.uuid4().urn}.mp3"
result.export(fn, 'mp3')
return fn
def reco(bin):
model = model_repository.recognition_model()
model.model = 'general'
model.language = 'ru-RU'
model.audio_processing_type = AudioProcessingType.Full
result = model.transcribe_file(bin)
return ' '.join(x.normalized_text for x in result)
def tg_send(chat_id, text):
url = f"https://api.telegram.org/bot{telegram_token}/sendMessage"
data = {"chat_id": chat_id, "text": text}
requests.post(url, data=data)
def tg_send_audio(chat_id, text, file):
url = f"https://api.telegram.org/bot{telegram_token}/sendAudio"
data = { "chat_id": chat_id, "caption": text }
files = { "audio" : open(file,'rb') }
requests.post(url, data=data, files=files)
def do_search(chat_id,txt):
print(f"Doing search on {txt}")
res = retriever.get_relevant_documents(txt)
res = chain.run(input_documents=res,query=txt)
if send_audio:
fn = synth(res)
tg_send_audio(chat_id,res,fn)
else:
tg_send(chat_id,res)
def process(post):
print(post)
msg = post['message']
chat_id = msg['chat']['id']
txt = None
if 'text' in msg:
do_search(chat_id,msg['text'])
if 'voice' in msg:
url = f"https://api.telegram.org/bot{telegram_token}/getFile"
data = { "file_id": msg['voice']['file_id'] }
resp = requests.post(url, data=data).json()
url = f"https://api.telegram.org/file/bot{telegram_token}/{resp['result']['file_path']}"
fn = f"/home/vmuser/temp/{uuid.uuid4().urn}.mp3"
bin = requests.get(url).content
with open(fn,'wb') as f:
f.write(bin)
res = reco(fn)
tg_send(chat_id,f'Вы спросили: {res}')
do_search(chat_id,res)
@app.route('/',methods=['GET'])
def home():
return "<h1>Hello</h1>"
@app.route('/tghook',methods=['GET','POST'])
def telegram_hook():
if request.method=='POST':
post = request.json
process(post)
return { "ok" : True }
print(" + Reading config")
with open('config.json') as f:
config = json.load(f)
self_url = config['self_url']
api_key = config['api_key']
telegram_token = config['telegram_token']
folder_id = config['folder_id']
print(" + Initializing LanceDB Vector Store")
embedding = YaGPTEmbeddings(folder_id,api_key)
lance_db = lancedb.connect(db_dir)
table = lance_db.open_table("vector_index")
vec_store = LanceDB(table, embedding)
retriever = vec_store.as_retriever(
search_kwargs={"k": 5}
)
print(" + Initializing LLM Chains")
instructions = """
Представь себе, что ты сотрудник Yandex Cloud. Твоя задача - вежливо и по мере своих сил отвечать на все вопросы собеседника.
"""
llm = YandexLLM(api_key=api_key, folder_id=folder_id,
instruction_text = instructions)
document_prompt = langchain.prompts.PromptTemplate(
input_variables=["page_content"], template="{page_content}"
)
# Промпт для языковой модели
document_variable_name = "context"
stuff_prompt_override = """
Пожалуйста, посмотри на текст ниже и ответь на вопрос, используя информацию из этого текста.
Текст:
-----
{context}
-----
Вопрос:
{query}"""
prompt = langchain.prompts.PromptTemplate(
template=stuff_prompt_override, input_variables=["context", "query"]
)
# Создаём цепочку
llm_chain = langchain.chains.LLMChain(llm=llm, prompt=prompt)
chain = langchain.chains.StuffDocumentsChain(
llm_chain=llm_chain,
document_prompt=document_prompt,
document_variable_name=document_variable_name,
)
print(" + Configuring speech")
configure_credentials(yandex_credentials=creds.YandexCredentials(api_key=api_key))
#print(" + Registering telegram hook")
#res = requests.post(f"https://api.telegram.org/bot{telegram_token}/setWebhook",json={ "url" : f"{self_url}/tghook" })
#print(res.json())
app.run(host="0.0.0.0",port=8443,ssl_context=(cert,cert_key)) | [
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# @Copyright: CEA-LIST/DIASI/SIALV/LVA (2023)
# @Author: CEA-LIST/DIASI/SIALV/LVA <[email protected]>
# @License: CECILL-C
#
# This software is a collaborative computer program whose purpose is to
# generate and explore labeled data for computer vision applications.
# This software is governed by the CeCILL-C license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/ or redistribute the software under the terms of the CeCILL-C
# license as circulated by CEA, CNRS and INRIA at the following URL
#
# http://www.cecill.info
from collections import defaultdict
from pathlib import Path
from typing import Optional
import duckdb
import lancedb
import pyarrow as pa
import pyarrow.dataset as pa_ds
from pydantic import BaseModel, ConfigDict
from s3path import S3Path
from pixano.core import Image
from pixano.data.dataset.dataset_info import DatasetInfo
from pixano.data.dataset.dataset_item import DatasetItem
from pixano.data.dataset.dataset_stat import DatasetStat
from pixano.data.dataset.dataset_table import DatasetTable
from pixano.data.fields import Fields, field_to_pyarrow
from pixano.data.item.item_feature import FeaturesValues, FeatureValues, ItemFeature
from pixano.data.item.item_object import ItemObject
class Dataset(BaseModel):
"""Dataset
Attributes:
path (Path | S3Path): Dataset path
info (DatasetInfo, optional): Dataset info
stats (list[DatasetStat], optional): Dataset stats
thumbnail (str, optional): Dataset thumbnail base 64 URL
"""
path: Path | S3Path
info: Optional[DatasetInfo] = None
stats: Optional[list[DatasetStat]] = None
thumbnail: Optional[str] = None
# Allow arbitrary types because of S3 Path
model_config = ConfigDict(arbitrary_types_allowed=True)
def __init__(
self,
path: Path | S3Path,
):
"""Initialize dataset
Args:
path (Path | S3Path): Dataset path
"""
info_file = path / "db.json"
stats_file = path / "stats.json"
thumb_file = path / "preview.png"
# Define public attributes through Pydantic BaseModel
super().__init__(
path=path,
info=DatasetInfo.from_json(info_file),
stats=DatasetStat.from_json(stats_file) if stats_file.is_file() else None,
thumbnail=(
Image(uri=thumb_file.absolute().as_uri()).url
if thumb_file.is_file()
else None
),
)
@property
def media_dir(self) -> Path | S3Path:
"""Return dataset media directory
Returns:
Path | S3Path: Dataset media directory
"""
return self.path / "media"
@property
def num_rows(self) -> int:
"""Return number of rows in dataset
Returns:
int: Number of rows
"""
ds = self.connect()
# Return number of rows of main table
return len(ds.open_table("db"))
def get_item_uuid(self, original_id: str) -> str:
"""Get id (uuid) from original data item id, if exist
Args:
original_id (str): original data item id
Returns:
str: unique item id, or original_id if not found
"""
ds_tables = self.open_tables()
# pylint: disable=unused-variable
lance_table = ds_tables["main"]["db"].to_lance()
result = (
duckdb.query(
f"SELECT id FROM lance_table WHERE original_id = '{original_id}'"
)
.to_arrow_table()
.to_pylist()
)
if result and len(result) == 1:
return result[0]["id"]
return original_id
def get_object_uuid(self, original_id: str) -> str:
"""Get id (uuid) from original data object id, if exist
Args:
original_id (str): original data object id
Returns:
str: unique item id, or original_id if not found
"""
ds_tables = self.open_tables()
# pylint: disable=unused-variable
lance_table = ds_tables["objects"]["objects"].to_lance()
result = (
duckdb.query(
f"SELECT id FROM lance_table WHERE original_id = '{original_id}'"
)
.to_arrow_table()
.to_pylist()
)
if result and len(result) == 1:
return result[0]["id"]
return original_id
def load_info(
self,
load_stats: bool = False,
load_thumbnail: bool = False,
load_features_values: bool = False,
) -> DatasetInfo:
"""Return dataset info with thumbnail and stats inside
Args:
load_stats (bool, optional): Load dataset stats. Defaults to False.
load_thumbnail (bool, optional): Load dataset thumbnail. Defaults to False.
load_features_values (bool, optional): Load available values. Defaults to False.
Returns:
DatasetInfo: Dataset info
"""
info = DatasetInfo.from_json(
self.path / "db.json",
load_stats=load_stats,
load_thumbnail=load_thumbnail,
)
if load_features_values:
info.features_values = self.get_features_values(info.features_values)
return info
def save_info(self):
"""Save updated dataset info"""
self.info.save(self.path)
@staticmethod
def create(path: Path | S3Path, info: DatasetInfo) -> "Dataset":
"""Create dataset
Args:
path (Path | S3Path): Path to create dataset in
info (DatasetInfo): Dataset info
Returns:
Dataset: Created dataset
"""
# Create DatasetInfo file
path.mkdir(parents=True, exist_ok=True)
info.save(path)
# Load dataset
dataset = Dataset(path)
# Create dataset tables
for group_name, table_group in dataset.info.tables.items():
for table in table_group:
dataset.create_table(table, group_name, add_to_info=False)
return dataset
def connect(self) -> lancedb.db.DBConnection:
"""Connect to dataset with LanceDB
Returns:
lancedb.db.DBConnection: Dataset LanceDB connection
"""
if isinstance(self.path, S3Path):
return lancedb.connect(self.path.as_uri())
return lancedb.connect(self.path)
def open_tables(self) -> dict[str, dict[str, lancedb.db.LanceTable]]:
"""Open dataset tables with LanceDB
Returns:
dict[str, dict[str, lancedb.db.LanceTable]]: Dataset tables
"""
ds = self.connect()
ds_tables: dict[str, dict[str, lancedb.db.LanceTable]] = defaultdict(dict)
for group_name, table_group in self.info.tables.items():
for table in table_group:
try:
ds_tables[group_name][table.name] = ds.open_table(table.name)
except FileNotFoundError as e:
# If optional table, remove from DatasetInfo
if group_name in ["objects", "embeddings", "active_learning"]:
self.info.tables[group_name].remove(table)
self.save_info()
else:
raise FileNotFoundError from e
return ds_tables
def create_table(
self,
table: DatasetTable,
table_group: str,
add_to_info: bool = True,
):
"""Create a new table in the dataset
Args:
table (DatasetTable): Table to create
table_group (str): Table group
add_to_info (bool, optional): Add table to DatasetInfo. Defaults to True.
"""
# Create Lance table
ds = self.connect()
# pylint: disable=unexpected-keyword-arg
ds.create_table(
table.name,
schema=Fields(table.fields).to_schema(),
mode="overwrite",
)
# Save table to DatasetInfo
if add_to_info:
if table_group in self.info.tables:
self.info.tables[table_group].append(table)
else:
self.info.tables[table_group] = [table]
self.save_info()
def update_table(
self,
element: DatasetItem | ItemObject,
table: lancedb.db.LanceTable,
table_group: str,
table_name: str,
):
"""Update a table with new features or base fields
Args:
element (DatasetItem | ItemObject): Table element (item or object)
table (lancedb.db.LanceTable): Table to update
table_group (str): Table group
table_name (str): Table name
"""
new_columns: list[ItemFeature] = []
# Check for new base fields
base_fields = {"review_state": "str", "bbox": "bbox", "mask": "compressedrle"}
new_columns.extend(
[
ItemFeature(name=field_name, dtype=field_type, value=None)
for field_name, field_type in base_fields.items()
if hasattr(element, field_name) and field_name not in table.schema.names
]
)
# Check for new features
if element.features is not None:
new_columns.extend(
[
feat
for feat in element.features.values()
if feat.name not in table.schema.names
]
)
# Add new columns
if len(new_columns) > 0:
new_columns_table = table.to_lance().to_table(columns=["id"])
for col in new_columns:
# None should be suported for booleans with pylance 0.9.1
# None is not supported for integers and floats yet
none_value = (
False
if col.dtype == "bool"
else 0 if col.dtype in ("int", "float") else None
)
col_array = pa.array(
[none_value] * len(table),
type=field_to_pyarrow(col.dtype),
)
new_columns_table = new_columns_table.append_column(
pa.field(col.name, field_to_pyarrow(col.dtype)), col_array
)
# Update DatasetInfo
for info_table in self.info.tables[table_group]:
if info_table.name == table_name:
info_table.fields[col.name] = col.dtype
# Merge with main table
table.to_lance().merge(new_columns_table, "id")
self.save_info()
def load_items(
self,
limit: int,
offset: int,
load_active_learning: bool = True,
) -> list[DatasetItem]:
"""Load dataset items in selected tables
Args:
limit (int): Items limit
offset (int): Items offset
load_active_learning (bool, optional): Load items active learning info. Defaults to True.
Returns:
list[DatasetItem]: List of dataset items
"""
# Update info in case of change
self.info = self.load_info()
# Load tables
ds_tables = self.open_tables()
# Load PyArrow items from tables
pyarrow_items: dict[str, dict[str, pa.Table]] = defaultdict(dict)
# Load PyArrow items from main table
# pylint: disable=unused-variable
lance_table = ds_tables["main"]["db"].to_lance()
id_field = "original_id" if "original_id" in lance_table.schema.names else "id"
pyarrow_items["main"]["db"] = duckdb.query(
f"SELECT * FROM lance_table ORDER BY len({id_field}), {id_field} LIMIT {limit} OFFSET {offset}"
).to_arrow_table()
id_list = tuple(pyarrow_items["main"]["db"].to_pydict()["id"])
# Media tables
for media_source, media_table in ds_tables["media"].items():
lance_table = media_table.to_lance()
pyarrow_items["media"][media_source] = duckdb.query(
f"SELECT * FROM lance_table WHERE id IN {id_list}"
).to_arrow_table()
# Active Learning tables
if load_active_learning:
for al_source, al_table in ds_tables["active_learning"].items():
lance_table = al_table.to_lance()
pyarrow_items["active_learning"][al_source] = duckdb.query(
f"SELECT * FROM lance_table WHERE id IN {id_list}"
).to_arrow_table()
if pyarrow_items["main"]["db"].num_rows > 0:
# Split results
pyarrow_item_list = self._split_items(pyarrow_items, load_active_learning)
return [
DatasetItem.from_pyarrow(pyarrow_item, self.info, self.media_dir)
for pyarrow_item in pyarrow_item_list
]
return None
def search_items(
self,
limit: int,
offset: int,
query: dict[str, str],
load_active_learning: bool = True,
):
"""Search for dataset items in selected tables
Args:
limit (int): Items limit
offset (int): Items offset
query (dict[str, str]): Search query
load_active_learning (bool, optional): Load items active learning info. Defaults to True.
Returns:
list[DatasetItem]: List of dataset items
"""
# Update info in case of change
self.info = self.load_info()
# Load PyArrow items from tables
pyarrow_items: dict[str, dict[str, pa.Table]] = defaultdict(dict)
# Search items with selected method
if query["model"] in ["CLIP"]:
pyarrow_items = self._embeddings_search(limit, offset, query)
# NOTE: metadata search could go here
if pyarrow_items is not None and pyarrow_items["main"]["db"].num_rows > 0:
# Split results
pyarrow_item_list = self._split_items(pyarrow_items, load_active_learning)
return [
DatasetItem.from_pyarrow(pyarrow_item, self.info, self.media_dir)
for pyarrow_item in pyarrow_item_list
]
return None
def _embeddings_search(
self,
limit: int,
offset: int,
query: dict[str, str],
) -> dict[str, dict[str, pa.Table]]:
"""Perform item semantic search with embeddings
Args:
limit (int): Items limit
offset (int): Items offset
query (dict[str, str]): Search query
Raises:
ImportError: Required pixano-inference module could not be imported
Returns:
dict[str, dict[str, pa.Table]]: Search results
"""
# Load tables
ds_tables = self.open_tables()
# Create PyArrow items
pyarrow_items: dict[str, dict[str, pa.Table]] = defaultdict(dict)
# Find CLIP embeddings
if "embeddings" not in self.info.tables:
return None
for table in self.info.tables["embeddings"]:
if table.type == "search" and table.source == query["model"]:
sem_search_table = ds_tables["embeddings"][table.name]
sem_search_views = [
field_name
for field_name, field_type in table.fields.items()
if field_type == "vector(512)"
]
if query["model"] == "CLIP":
# Initialize CLIP model
try:
# pylint: disable=import-outside-toplevel
from pixano_inference.transformers import CLIP
except ImportError as e:
raise ImportError(
"Please install the pixano-inference module to perform semantic search with CLIP"
) from e
model = CLIP()
model_query = model.semantic_search(query["search"])
# Perform semantic search
# pylint: disable=unused-variable
results_table = (
sem_search_table.search(model_query, sem_search_views[0])
.limit(min(offset + limit, self.num_rows))
.to_arrow()
)
# If more than one view, search on all views and select the best results based on distance
if len(sem_search_views) > 1:
for view in sem_search_views[1:]:
view_results_table = (
sem_search_table.search(model_query, view)
.limit(min(offset + limit, self.num_rows))
.to_arrow()
)
results_table = duckdb.query(
"SELECT id, results_table._distance as distance_1, view_results_table._distance as distance_2 FROM results_table LEFT JOIN view_results_table USING (id)"
).to_arrow_table()
results_table = duckdb.query(
"SELECT (id), (SELECT Min(v) FROM (VALUES (distance_1), (distance_2)) AS value(v)) as _distance FROM results_table"
).to_arrow_table()
# Filter results to page
results_table = duckdb.query(
f"SELECT id, _distance as distance FROM results_table ORDER BY _distance ASC LIMIT {limit} OFFSET {offset}"
).to_arrow_table()
# Join with main table
main_table = ds_tables["main"]["db"].to_lance()
pyarrow_items["main"]["db"] = duckdb.query(
"SELECT * FROM results_table LEFT JOIN main_table USING (id) ORDER BY distance ASC"
).to_arrow_table()
return pyarrow_items
def _split_items(
self,
pyarrow_items: dict[str, dict[str, pa.Table]],
load_active_learning: bool,
) -> list[dict[str, dict[str, pa.Table]]]:
"""Split PyArrow tables into list of PyArrow tables
Args:
pyarrow_items (dict[str, dict[str, pa.Table]]): PyArrow tables
load_active_learning (bool): Load items active learning info
Returns:
list[dict[str, dict[str, pa.Table]]]: List of PyArrow tables
"""
# Load tables
ds_tables = self.open_tables()
# Create list of PyArrow tables
pyarrow_item_list: list[dict[str, dict[str, pa.Table]]] = []
for index in range(pyarrow_items["main"]["db"].num_rows):
pyarrow_item_list.append(defaultdict(dict))
# Main table
pyarrow_item_list[index]["main"]["db"] = pyarrow_items["main"]["db"].take(
[index]
)
item_id = pyarrow_item_list[index]["main"]["db"].to_pylist()[0]["id"]
# Media tables
for media_source, media_table in ds_tables["media"].items():
# If media table already created
if "media" in pyarrow_items:
pyarrow_item_list[index]["media"][media_source] = (
pa_ds.dataset(pyarrow_items["media"][media_source])
.scanner(filter=pa_ds.field("id") == item_id)
.to_table()
)
# Else, retrieve media items individually
else:
lance_scanner = media_table.to_lance().scanner(
filter=f"id in ('{item_id}')"
)
pyarrow_item_list[index]["media"][
media_source
] = lance_scanner.to_table()
# Active learning tables
if load_active_learning:
# If active learning table already created
if "active_learning" in pyarrow_items:
for al_source in ds_tables["active_learning"].keys():
pyarrow_item_list[index]["active_learning"][al_source] = (
pa_ds.dataset(pyarrow_items["active_learning"][al_source])
.scanner(filter=pa_ds.field("id") == item_id)
.to_table()
)
# Else, retrieve active learning items individually
else:
for al_source, al_table in ds_tables["active_learning"].items():
lance_scanner = al_table.to_lance().scanner(
filter=f"id in ('{item_id}')"
)
pyarrow_item_list[index]["active_learning"][
al_source
] = lance_scanner.to_table()
return pyarrow_item_list
def load_item(
self,
item_id: str,
load_media: bool = True,
load_objects: bool = False,
load_active_learning: bool = True,
load_embeddings: bool = False,
model_id: str = None,
) -> DatasetItem:
"""Find dataset item in selected tables
Args:
item_id (str): Dataset item ID
load_media (bool, optional): Load item media. Defaults to True.
load_objects (bool, optional): Load item objects. Defaults to False.
load_active_learning (bool, optional): Load item active learning info. Defaults to True.
load_embeddings (bool, optional): Load item embeddings. Defaults to False.
model_id (str, optional): Model ID (ONNX file path) of embeddings to load. Defaults to None.
Returns:
DatasetItem: Dataset item
"""
# Update info in case of change
self.info = self.load_info()
# Load tables
ds_tables = self.open_tables()
# Load PyArrow item from tables
pyarrow_item: dict[str, dict[str, pa.Table]] = defaultdict(dict)
# Load PyArrow item from main table
lance_scanner = (
ds_tables["main"]["db"].to_lance().scanner(filter=f"id in ('{item_id}')")
)
pyarrow_item["main"]["db"] = lance_scanner.to_table()
# Load PyArrow item from media tables
if load_media:
for table_name, media_table in ds_tables["media"].items():
lance_scanner = media_table.to_lance().scanner(
filter=f"id in ('{item_id}')"
)
pyarrow_item["media"][table_name] = lance_scanner.to_table()
# Load PyArrow item from objects tables
if load_objects:
for table_name, obj_table in ds_tables["objects"].items():
lance_scanner = obj_table.to_lance().scanner(
filter=f"item_id in ('{item_id}')"
)
pyarrow_item["objects"][table_name] = lance_scanner.to_table()
# Load PyArrow item from active learning tables
if load_active_learning:
for table_name, al_table in ds_tables["active_learning"].items():
lance_scanner = al_table.to_lance().scanner(
filter=f"id in ('{item_id}')"
)
pyarrow_item["active_learning"][table_name] = lance_scanner.to_table()
# Load PyArrow item from segmentation embeddings tables
found_embeddings = not load_embeddings
if load_embeddings and "embeddings" in self.info.tables:
for table in self.info.tables["embeddings"]:
if table.source.lower() in model_id.lower():
found_embeddings = True
emb_table = ds_tables["embeddings"][table.name]
lance_scanner = emb_table.to_lance().scanner(
filter=f"id in ('{item_id}')"
)
pyarrow_item["embeddings"][table.name] = lance_scanner.to_table()
if pyarrow_item["main"]["db"].num_rows > 0 and found_embeddings:
return DatasetItem.from_pyarrow(
pyarrow_item,
self.info,
self.media_dir,
media_features=True,
model_id=model_id,
)
return None
def save_item(self, item: DatasetItem):
"""Save dataset item features and objects
Args:
item (DatasetItem): Item to save
"""
# Update info in case of change
self.info = self.load_info()
# Load dataset tables
ds_tables = self.open_tables()
main_table = ds_tables["main"]["db"]
# Add new item columns
self.update_table(item, main_table, "main", "db")
# Reload dataset tables
ds_tables = self.open_tables()
main_table = ds_tables["main"]["db"]
# Update item
item.update(main_table)
# Add or update item objects
for obj in item.objects.values():
table_found = False
if "objects" in self.info.tables:
for table in self.info.tables["objects"]:
if table.source == obj.source_id:
# Load object table
table_found = True
obj_table = ds_tables["objects"][table.name]
# Add new object columns
self.update_table(obj, obj_table, "objects", table.name)
# Reload dataset tables
ds_tables = self.open_tables()
main_table = ds_tables["objects"][table.name]
# Add or update object
obj.add_or_update(obj_table)
# If first object
if not table_found and obj.source_id == "Ground Truth":
# Create table
table = DatasetTable(
name="objects",
source="Ground Truth",
fields={
"id": "str",
"item_id": "str",
"view_id": "str",
"bbox": "bbox",
"mask": "compressedrle",
},
)
for feat in obj.features.values():
table.fields[feat.name] = feat.dtype
self.create_table(table, "objects")
# Reload dataset tables
ds_tables = self.open_tables()
obj_table = ds_tables["objects"][table.name]
# Add object
obj.add_or_update(obj_table)
# Delete removed item objects
item.delete_objects(ds_tables)
def get_features_values(self, config_values: FeaturesValues) -> FeaturesValues:
"""get config values
merge with distinct existing values for each scene and object string features, if not restricted
Args:
config_values (FeaturesValues): features values from db.json
Returns:
FeaturesValues: existing values for each scene and object string feature
"""
# Load tables
ds_tables = self.open_tables()
def get_distinct_values(
table_name: str,
ignore_list: list[str],
config_vals: dict[str, FeatureValues],
) -> dict[str, FeatureValues]:
avail_values = defaultdict(FeatureValues)
for table in ds_tables[table_name].values():
table_arrow = table.to_arrow()
feats = [f for f in table_arrow.column_names if f not in ignore_list]
for feat in feats:
avail_values[feat] = FeatureValues(restricted=False, values=[])
if config_vals and feat in config_vals:
avail_values[feat].restricted = config_vals[feat].restricted
avail_values[feat].values.extend(config_vals[feat].values)
if not avail_values[feat].restricted:
v = (
duckdb.sql(f"select DISTINCT {feat} from table_arrow")
.to_arrow_table()
.to_pydict()
)
if v[feat] is not None and v[feat] != [None]:
avail_values[feat].values.extend(
[
val
for val in v[feat]
if val is not None and isinstance(val, str)
]
)
return avail_values
return FeaturesValues(
main=get_distinct_values(
"main",
["id", "split", "views", "original_id"],
config_values.main if config_values else None,
),
objects=get_distinct_values(
"objects",
["id", "item_id", "view_id", "bbox", "mask", "review_state"],
config_values.objects if config_values else None,
),
)
@staticmethod
def find(
dataset_id: str,
directory: Path | S3Path,
) -> "Dataset":
"""Find Dataset in directory
Args:
dataset_id (str): Dataset ID
directory (Path): Directory to search in
Returns:
Dataset: Dataset
"""
# Browse directory
for json_fp in directory.glob("*/db.json"):
info = DatasetInfo.from_json(json_fp)
if info.id == dataset_id:
# Return dataset
return Dataset(json_fp.parent)
return None
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import streamlit as st
import sqlite3
import streamlit_antd_components as sac
import pandas as pd
import os
import openai
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import LanceDB
from basecode.authenticate import return_api_key
from langchain.docstore.document import Document
import lancedb
import configparser
import ast
import json
class ConfigHandler:
def __init__(self):
self.config = configparser.ConfigParser()
self.config.read('config.ini')
def get_config_values(self, section, key):
value = self.config.get(section, key)
try:
# Try converting the string value to a Python data structure
return ast.literal_eval(value)
except (SyntaxError, ValueError):
# If not a data structure, return the plain string
return value
config_handler = ConfigHandler()
TCH = config_handler.get_config_values('constants', 'TCH')
STU = config_handler.get_config_values('constants', 'STU')
SA = config_handler.get_config_values('constants', 'SA')
AD = config_handler.get_config_values('constants', 'AD')
# Create or check for the 'database' directory in the current working directory
cwd = os.getcwd()
WORKING_DIRECTORY = os.path.join(cwd, "database")
if not os.path.exists(WORKING_DIRECTORY):
os.makedirs(WORKING_DIRECTORY)
if st.secrets["sql_ext_path"] == "None":
WORKING_DATABASE= os.path.join(WORKING_DIRECTORY , st.secrets["default_db"])
else:
WORKING_DATABASE= st.secrets["sql_ext_path"]
os.environ["OPENAI_API_KEY"] = return_api_key()
lancedb_path = os.path.join(WORKING_DIRECTORY, "lancedb")
db = lancedb.connect(lancedb_path)
def fetch_vectorstores_with_usernames():
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
query = '''
SELECT
Vector_Stores.vs_id,
Subject.subject_name,
Topic.topic_name,
Vector_Stores.vectorstore_name,
Users.username,
Vector_Stores.sharing_enabled
FROM Vector_Stores
JOIN Users ON Vector_Stores.user_id = Users.user_id
LEFT JOIN Subject ON Vector_Stores.subject = Subject.id
LEFT JOIN Topic ON Vector_Stores.topic = Topic.id;
'''
cursor.execute(query)
data = cursor.fetchall()
conn.close()
return data
def display_vectorstores():
data = fetch_vectorstores_with_usernames()
df = pd.DataFrame(data, columns=["vs_id", "subject_name", "topic_name", "vectorstore_name", "username", "sharing_enabled"])
# Convert the 'sharing_enabled' values
df["sharing_enabled"] = df["sharing_enabled"].apply(lambda x: '✔' if x == 1 else '')
st.dataframe(
df,
use_container_width=True,
column_order=["vs_id", "subject_name", "topic_name", "vectorstore_name", "username", "sharing_enabled"]
)
def fetch_all_files():
"""
Fetch all files either shared or based on user type
"""
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
# Construct the SQL query with JOINs for Subject, Topic, and Users tables
if st.session_state.user['profile_id'] == 'SA':
cursor.execute('''
SELECT Files.file_id, Files.file_name, Subject.subject_name, Topic.topic_name, Users.username
FROM Files
JOIN Subject ON Files.subject = Subject.id
JOIN Topic ON Files.topic = Topic.id
JOIN Users ON Files.user_id = Users.user_id
''')
else:
cursor.execute('''
SELECT Files.file_id, Files.file_name, Subject.subject_name, Topic.topic_name, Users.username
FROM Files
JOIN Subject ON Files.subject = Subject.id
JOIN Topic ON Files.topic = Topic.id
JOIN Users ON Files.user_id = Users.user_id
WHERE Files.sharing_enabled = 1
''')
files = cursor.fetchall()
formatted_files = [f"({file[0]}) {file[1]} ({file[4]})" for file in files]
conn.close()
return formatted_files
def fetch_file_data(file_id):
"""
Fetch file data given a file id
"""
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
cursor.execute("SELECT data, metadata FROM Files WHERE file_id = ?", (file_id,))
data = cursor.fetchone()
conn.close()
if data:
return data[0], data[1]
else:
return None, None
def insert_topic(org_id, topic_name):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
try:
cursor.execute('INSERT INTO Topic (org_id, topic_name) VALUES (?, ?);', (org_id, topic_name))
conn.commit()
return True # Indicates successful insertion
except sqlite3.IntegrityError:
# IntegrityError occurs if topic_name is not unique within the org
return False # Indicates topic_name is not unique within the org
finally:
conn.close()
def insert_subject(org_id, subject_name):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
try:
cursor.execute('INSERT INTO Subject (org_id, subject_name) VALUES (?, ?);', (org_id, subject_name))
conn.commit()
return True # Indicates successful insertion
except sqlite3.IntegrityError:
# IntegrityError occurs if subject_name is not unique within the org
return False # Indicates subject_name is not unique within the org
finally:
conn.close()
def select_organization():
with sqlite3.connect(WORKING_DATABASE) as conn:
cursor = conn.cursor()
# Org selection
org_query = "SELECT org_name FROM Organizations"
cursor.execute(org_query)
orgs = cursor.fetchall()
org_names = [org[0] for org in orgs]
# Use a Streamlit selectbox to choose an organization
selected_org_name = st.selectbox("Select an organization:", org_names)
# Retrieve the org_id for the selected organization
cursor.execute('SELECT org_id FROM Organizations WHERE org_name = ?;', (selected_org_name,))
result = cursor.fetchone()
if result:
org_id = result[0]
st.write(f"The org_id for {selected_org_name} is {org_id}.")
return org_id
else:
st.write(f"Organization '{selected_org_name}' not found in the database.")
return None
def fetch_subjects_by_org(org_id):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
# Check if the user is a super_admin (org_id is 0)
if org_id == 0:
cursor.execute('SELECT * FROM Subject;')
else:
cursor.execute('SELECT * FROM Subject WHERE org_id = ?;', (org_id,))
subjects = cursor.fetchall()
conn.close()
return subjects
def fetch_topics_by_org(org_id):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
# Check if the user is a super_admin (org_id is 0)
if org_id == 0:
cursor.execute('SELECT * FROM Topic;')
else:
cursor.execute('SELECT * FROM Topic WHERE org_id = ?;', (org_id,))
topics = cursor.fetchall()
conn.close()
return topics
def split_docs(file_path,meta):
#def split_meta_docs(file, source, tch_code):
loader = UnstructuredFileLoader(file_path)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
metadata = {"source": meta}
for doc in docs:
doc.metadata.update(metadata)
return docs
def create_lancedb_table(embeddings, meta, table_name):
lancedb_path = os.path.join(WORKING_DIRECTORY, "lancedb")
# LanceDB connection
db = lancedb.connect(lancedb_path)
table = db.create_table(
f"{table_name}",
data=[
{
"vector": embeddings.embed_query("Query Unsuccessful"),
"text": "Query Unsuccessful",
"id": "1",
"source": f"{meta}"
}
],
mode="overwrite",
)
return table
def save_to_vectorstores(vs, vstore_input_name, subject, topic, username, share_resource=False):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
# Fetch the user's details
cursor.execute('SELECT user_id FROM Users WHERE username = ?', (username,))
user_details = cursor.fetchone()
if not user_details:
st.error("Error: User not found.")
return
user_id = user_details[0]
# If Vector_Store instance exists in session state, then serialize and save
# vs is the documents in json format and vstore_input_name is the name of the table and vectorstore
if vs:
try:
cursor.execute('SELECT 1 FROM Vector_Stores WHERE vectorstore_name LIKE ? AND user_id = ?', (f"%{vstore_input_name}%", user_id))
exists = cursor.fetchone()
if exists:
st.error("Error: An entry with the same vectorstore_name and user_id already exists.")
return
if subject is None:
st.error("Error: Subject is missing.")
return
if topic is None:
st.error("Error: Topic is missing.")
return
# Get the subject and topic IDs
cursor.execute('SELECT id FROM Subject WHERE subject_name = ?', (subject,))
subject_id = cursor.fetchone()[0]
cursor.execute('SELECT id FROM Topic WHERE topic_name = ?', (topic,))
topic_id = cursor.fetchone()[0]
# Insert the new row
cursor.execute('''
INSERT INTO Vector_Stores (vectorstore_name, documents, user_id, subject, topic, sharing_enabled)
VALUES (?, ?, ?, ?, ?, ?)
''', (vstore_input_name, vs, user_id, subject_id, topic_id, share_resource))
conn.commit()
conn.close()
except Exception as e:
st.error(f"Error in storing documents and vectorstore: {e}")
return
def document_to_dict(doc):
# Assuming 'doc' has 'page_content' and 'metadata' attributes
return {
'page_content': doc.page_content,
'metadata': doc.metadata
}
def dict_to_document(doc_dict):
# Create a Document object from the dictionary
# Adjust this according to how your Document class is defined
return Document(page_content=doc_dict['page_content'],metadata=doc_dict['metadata'])
def create_vectorstore():
openai.api_key = return_api_key()
os.environ["OPENAI_API_KEY"] = return_api_key()
full_docs = []
st.subheader("Enter the topic and subject for your knowledge base")
embeddings = OpenAIEmbeddings()
if st.session_state.user['profile_id'] == SA:
org_id = select_organization()
if org_id is None:
return
else:
org_id = st.session_state.user["org_id"]
# Fetch all available subjects
subjects = fetch_subjects_by_org(st.session_state.user["org_id"])
subject_names = [sub[2] for sub in subjects] # Assuming index 2 holds the subject_name
selected_subject = st.selectbox("Select an existing subject or type a new one:", options=subject_names + ['New Subject'])
if selected_subject == 'New Subject':
subject = st.text_input("Please enter the new subject name:", max_chars=30)
if subject:
insert_subject(org_id, subject)
else:
subject = selected_subject
# Fetch all available topics
topics = fetch_topics_by_org(st.session_state.user["org_id"])
topic_names = [topic[2] for topic in topics] # Assuming index 2 holds the topic_name
selected_topic = st.selectbox("Select an existing topic or type a new one:", options=topic_names + ['New Topic'])
if selected_topic == 'New Topic':
topic = st.text_input("Please enter the new topic name:", max_chars=30)
if topic:
insert_topic(org_id, topic)
else:
topic = selected_topic
vectorstore_input = st.text_input("Please type in a name for your knowledge base:", max_chars=20)
vs_name = vectorstore_input + f"_({st.session_state.user['username']})"
share_resource = st.checkbox("Share this resource", value=True) # <-- Added this line
# Show the current build of files for the latest database
st.subheader("Select one or more files to build your knowledge base")
files = fetch_all_files()
if files:
selected_files = sac.transfer(items=files, label=None, index=None, titles=['Uploaded files', 'Select files for KB'], format_func='title', width='100%', height=None, search=True, pagination=False, oneway=False, reload=True, disabled=False, return_index=False)
# Alert to confirm the creation of knowledge base
st.warning("Building your knowledge base will take some time. Please be patient.")
build = sac.buttons([
dict(label='Build VectorStore', icon='check-circle-fill', color = 'green'),
dict(label='Cancel', icon='x-circle-fill', color='red'),
], label=None, index=1, format_func='title', align='center', position='top', size='default', direction='horizontal', shape='round', type='default', compact=False, return_index=False)
if build == 'Build VectorStore' and selected_files:
for s_file in selected_files:
file_id = int(s_file.split("(", 1)[1].split(")", 1)[0])
file_data, meta = fetch_file_data(file_id)
docs = split_docs(file_data, meta)
full_docs.extend(docs)
#convert full_docs to json to store in sqlite
full_docs_dicts = [document_to_dict(doc) for doc in full_docs]
docs_json = json.dumps(full_docs_dicts)
#db = LanceDB.from_documents(full_docs, OpenAIEmbeddings(), connection=create_lancedb_table(embeddings, meta, vs_name))
#table = create_lancedb_table(embeddings, meta, vs_name)
# lancedb_path = os.path.join(WORKING_DIRECTORY, "lancedb")
# LanceDB connection
# db = lancedb.connect(lancedb_path)
# st.session_state.test1 = table
# st.write("full_docs",full_docs)
#full_docs_dicts = [document_to_dict(doc) for doc in full_docs]
#docs_json = json.dumps(full_docs_dicts)
# st.write("docs_json",docs_json)
#retrieved_docs_dicts = get_docs() # Assuming this returns the list of dictionaries
# retrieved_docs_dicts = json.loads(docs_json)
# retrieved_docs = [dict_to_document(doc_dict) for doc_dict in retrieved_docs_dicts]
# st.write("retrieved_docs",retrieved_docs)
#st.session_state.test2 = json.loads(docs_json)
# st.session_state.vs = LanceDB.from_documents(retrieved_docs , OpenAIEmbeddings(), connection= db.open_table("_(super_admin)"))
# st.session_state.current_model = "test1"
# st.write(st.session_state.test1)
#st.write(st.session_state.test2)
#st.write(type(db))
#st.session_state.vs = load_vectorstore(documents, table_name)
create_lancedb_table(embeddings, meta, vs_name)
save_to_vectorstores(docs_json, vs_name, subject, topic, st.session_state.user["username"], share_resource) # Passing the share_resource to the function
st.success("Knowledge Base loaded")
else:
st.write("No files found in the database.")
def load_vectorstore(documents, table_name):
retrieved_docs_dicts = json.loads(documents)
retrieved_docs = [dict_to_document(doc_dict) for doc_dict in retrieved_docs_dicts]
vs = LanceDB.from_documents(retrieved_docs , OpenAIEmbeddings(), connection= db.open_table(f"{table_name}"))
return vs
def delete_lancedb_table(table_name):
lancedb_path = os.path.join(WORKING_DIRECTORY, "lancedb")
# LanceDB connection
db = lancedb.connect(lancedb_path)
db.drop_table(f"{table_name}")
def fetch_vectorstores_by_user_id(user_id):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
# Fetch vectorstores based on user_id
cursor.execute('SELECT vectorstore_name FROM Vector_Stores WHERE user_id = ?;', (user_id,))
vectorstores = cursor.fetchall()
conn.close()
return vectorstores
def delete_vectorstores():
st.subheader("Delete VectorStores in Database:")
user_vectorstores = fetch_vectorstores_by_user_id(st.session_state.user["id"])
if user_vectorstores:
vectorstore_names = [vs[0] for vs in user_vectorstores]
selected_vectorstores = st.multiselect("Select vectorstores to delete:", options=vectorstore_names)
confirm_delete = st.checkbox("I understand that this action cannot be undone.", value=False)
if st.button("Delete VectorStore"):
if confirm_delete and selected_vectorstores:
delete_vectorstores_from_db(selected_vectorstores, st.session_state.user["id"], st.session_state.user["profile_id"])
st.success(f"Deleted {len(selected_vectorstores)} vectorstores.")
else:
st.warning("Please confirm the deletion action.")
else:
st.write("No vectorstores found in the database.")
def delete_vectorstores_from_db(vectorstore_names, user_id, profile):
conn = sqlite3.connect(WORKING_DATABASE)
cursor = conn.cursor()
for vectorstore_name in vectorstore_names:
if profile in ['SA', 'AD']:
# Delete the corresponding LanceDB table
delete_lancedb_table(vectorstore_name)
# Delete vectorstore irrespective of the user_id associated with them
cursor.execute('DELETE FROM Vector_Stores WHERE vectorstore_name=?;', (vectorstore_name,))
else:
# Delete the corresponding LanceDB table
delete_lancedb_table(vectorstore_name)
# Delete only if the user_id matches
cursor.execute('DELETE FROM Vector_Stores WHERE vectorstore_name=? AND user_id=?;', (vectorstore_name, user_id))
# Check if the row was affected
if cursor.rowcount == 0:
st.error(f"Unable to delete vectorstore '{vectorstore_name}' that is not owned by you.")
conn.commit() # Commit the changes
conn.close() # Close the connection
| [
"lancedb.connect"
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import lancedb
import pyarrow as pa
import os
uri = "./data/sample-lancedb"
table_name = "my_table"
db = lancedb.connect(uri)
# check if the database exists and only proceed if it does not
if db.table_names() == []:
print("Creating table")
schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2)),
pa.field("item", pa.string()),
pa.field("price", pa.float32())])
#print(f'{schema=}')
table = db.create_table(table_name, schema=schema)
table.add(data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
else:
print("Opening table")
table = db.open_table(table_name)
print("Table:", table.name)
result = table.search([100, 100]).limit(2).to_df()
print(result)
| [
"lancedb.connect"
] | [((106, 126), 'lancedb.connect', 'lancedb.connect', (['uri'], {}), '(uri)\n', (121, 126), False, 'import lancedb\n'), ((367, 378), 'pyarrow.string', 'pa.string', ([], {}), '()\n', (376, 378), True, 'import pyarrow as pa\n'), ((423, 435), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (433, 435), True, 'import pyarrow as pa\n'), ((297, 309), 'pyarrow.float32', 'pa.float32', ([], {}), '()\n', (307, 309), True, 'import pyarrow as pa\n')] |
import sys
import os
# this is needed to import classes from the API. it will be removed when the worker is refactored
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../')))
import time
import pika
import json
import pinecone
import logging
import weaviate
import redis
import lancedb
import pymongo
import pyarrow as pa
import numpy as np
import worker.config as config
import services.database.batch_service as batch_service
import services.database.job_service as job_service
from services.database.database import get_db, safe_db_operation
from shared.job_status import JobStatus
from shared.batch_status import BatchStatus
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct
from pymilvus import Collection, connections
from shared.embeddings_type import EmbeddingsType
from shared.vector_db_type import VectorDBType
from shared.utils import generate_uuid_from_tuple
from urllib.parse import quote_plus
from services.rabbitmq.rabbit_service import create_connection_params
from pika.exceptions import AMQPConnectionError
logging.basicConfig(filename='./vdb-log.txt', level=logging.INFO)
logging.basicConfig(filename='./vdb-errors.txt', level=logging.ERROR)
def upload_batch(batch_id, chunks_with_embeddings):
batch = safe_db_operation(batch_service.get_batch, batch_id)
if batch.batch_status == BatchStatus.FAILED:
safe_db_operation(batch_service.update_batch_retry_count, batch.id, batch.retries+1)
logging.info(f"Retrying vector db upload of batch {batch.id}")
batch = safe_db_operation(batch_service.get_batch, batch_id)
vectors_uploaded = write_embeddings_to_vector_db(chunks_with_embeddings, batch.vector_db_metadata, batch.id, batch.job_id)
if vectors_uploaded:
status = safe_db_operation(batch_service.update_batch_status_with_successful_minibatch, batch.id)
update_batch_and_job_status(batch.job_id, status, batch.id)
else:
update_batch_and_job_status(batch.job_id, BatchStatus.FAILED, batch.id)
def write_embeddings_to_vector_db(chunks, vector_db_metadata, batch_id, job_id):
# NOTE: the legacy code expects a list of tuples, (text_chunk, embedding) of form (str, list[float])
text_embeddings_list = [(chunk['text'], chunk['vector']) for chunk in chunks]
job = safe_db_operation(job_service.get_job, job_id)
source_filename = job.source_filename
if vector_db_metadata.vector_db_type == VectorDBType.PINECONE:
upsert_list = create_pinecone_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_pinecone(upsert_list, vector_db_metadata)
elif vector_db_metadata.vector_db_type == VectorDBType.QDRANT:
upsert_list = create_qdrant_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_qdrant(upsert_list, vector_db_metadata)
elif vector_db_metadata.vector_db_type == VectorDBType.WEAVIATE:
return write_embeddings_to_weaviate(text_embeddings_list, vector_db_metadata, batch_id, job_id, source_filename)
elif vector_db_metadata.vector_db_type == VectorDBType.MILVUS:
upsert_list = create_milvus_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_milvus(upsert_list, vector_db_metadata)
elif vector_db_metadata.vector_db_type == VectorDBType.REDIS:
upsert_list = create_redis_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_redis(upsert_list, vector_db_metadata)
elif vector_db_metadata.vector_db_type == VectorDBType.LANCEDB:
upsert_list = create_lancedb_source_chunks(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_lancedb(upsert_list, batch_id)
elif vector_db_metadata.vector_db_type == VectorDBType.MONGODB:
upsert_list = create_mongodb_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename)
return write_embeddings_to_mongodb(upsert_list, vector_db_metadata)
else:
logging.error('Unsupported vector DB type: %s', vector_db_metadata.vector_db_type.value)
def create_mongodb_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename):
upsert_list = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
upsert_list.append(
{"_id": generate_uuid_from_tuple((job_id, batch_id, i)),
"values": embedding,
"source_text": source_text,
"source_document": source_filename
})
return upsert_list
def write_embeddings_to_mongodb(upsert_list, vector_db_metadata):
mongo_conn_uri = vector_db_metadata.environment
mongo_password = quote_plus(os.getenv('VECTOR_DB_KEY'))
mongo_conn_uri = mongo_conn_uri.replace("<password>", mongo_password)
mongo_client = pymongo.MongoClient(mongo_conn_uri)
db_name, collection = vector_db_metadata.index_name.split(".")
db = mongo_client[db_name]
try:
db.command("ping")
except Exception as e:
logging.error(f"Error connecting to MongoDB via python client: {e}")
return None
if collection not in db.list_collection_names():
logging.error(f"Index {vector_db_metadata.index_name} does not exist in environment {vector_db_metadata.environment}")
return None
index = db.get_collection(collection)
logging.info(f"Starting MongoDB upsert for {len(upsert_list)} vectors")
batch_size = config.PINECONE_BATCH_SIZE
vectors_uploaded = 0
for i in range(0,len(upsert_list), batch_size):
try:
upsert_batch = upsert_list[i:i+batch_size]
upsert_response = index.insert_many(upsert_batch)
vectors_uploaded += len(upsert_batch)
except Exception as e:
logging.error('Error writing embeddings to Mongo:', e)
return None
logging.info(f"Successfully uploaded {vectors_uploaded} vectors to MongoDB")
return vectors_uploaded
def create_pinecone_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename):
upsert_list = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
upsert_list.append(
{"id": generate_uuid_from_tuple((job_id, batch_id, i)),
"values": embedding,
"metadata": {"source_text": source_text, "source_document": source_filename}})
return upsert_list
def write_embeddings_to_pinecone(upsert_list, vector_db_metadata):
pinecone_api_key = os.getenv('VECTOR_DB_KEY')
pinecone.init(api_key=pinecone_api_key, environment=vector_db_metadata.environment)
index = pinecone.GRPCIndex(vector_db_metadata.index_name)
if not index:
logging.error(f"Index {vector_db_metadata.index_name} does not exist in environment {vector_db_metadata.environment}")
return None
logging.info(f"Starting pinecone upsert for {len(upsert_list)} vectors")
batch_size = config.PINECONE_BATCH_SIZE
vectors_uploaded = 0
for i in range(0,len(upsert_list), batch_size):
try:
upsert_response = index.upsert(vectors=upsert_list[i:i+batch_size])
vectors_uploaded += upsert_response.upserted_count
except Exception as e:
logging.error('Error writing embeddings to pinecone:', e)
return None
logging.info(f"Successfully uploaded {vectors_uploaded} vectors to pinecone")
return vectors_uploaded
def create_redis_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename):
ids = []
source_texts = []
source_documents = []
embeddings = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
ids.append(generate_uuid_from_tuple((job_id, batch_id, i)))
source_texts.append(source_text)
embeddings.append(embedding)
source_documents.append(source_filename)
return [ids, source_texts, embeddings, source_documents]
def write_embeddings_to_redis(upsert_list, vector_db_metadata):
redis_client = redis.from_url(url=vector_db_metadata.environment, password=os.getenv('VECTOR_DB_KEY'), decode_responses=True)
try:
redis_client.ft(vector_db_metadata.index_name).info()
except redis.exceptions.ResponseError as e:
if "Unknown Index name" in str(e):
logging.error(f"Index {vector_db_metadata.index_name} does not exist at redis URL {vector_db_metadata.environment}")
return None
logging.info(f"Starting redis upsert for {len(upsert_list)} vectors")
redis_pipeline = redis_client.pipeline()
for i in range(0,len(upsert_list[0])):
key = f'{vector_db_metadata.collection}:{upsert_list[0][i]}'
obj = {"source_data": upsert_list[1][i], "embeddings": np.array(upsert_list[2][i]).tobytes(), "source_document": upsert_list[3][i]}
redis_pipeline.hset(key, mapping=obj)
res = redis_pipeline.execute()
logging.info(f"Successfully uploaded {len(res)} vectors to redis")
return len(res)
def create_qdrant_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename):
upsert_list = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
upsert_list.append(
PointStruct(
id=generate_uuid_from_tuple((job_id, batch_id, i)),
vector=embedding,
payload={"source_text": source_text, "source_document": source_filename}
)
)
return upsert_list
def write_embeddings_to_qdrant(upsert_list, vector_db_metadata):
qdrant_client = QdrantClient(
url=vector_db_metadata.environment,
api_key=os.getenv('VECTOR_DB_KEY'),
grpc_port=6334,
prefer_grpc=True,
timeout=5
) if vector_db_metadata.environment != os.getenv('LOCAL_VECTOR_DB') else QdrantClient(os.getenv('LOCAL_VECTOR_DB'), port=6333)
index = qdrant_client.get_collection(collection_name=vector_db_metadata.index_name)
if not index:
logging.error(f"Collection {vector_db_metadata.index_name} does not exist at cluster URL {vector_db_metadata.environment}")
return None
logging.info(f"Starting qdrant upsert for {len(upsert_list)} vectors")
batch_size = config.PINECONE_BATCH_SIZE
for i in range(0, len(upsert_list), batch_size):
try:
qdrant_client.upsert(
collection_name=vector_db_metadata.index_name,
points=upsert_list[i:i+batch_size]
)
except Exception as e:
logging.error('Error writing embeddings to qdrant:', e)
return None
logging.info(f"Successfully uploaded {len(upsert_list)} vectors to qdrant")
return len(upsert_list)
def write_embeddings_to_weaviate(text_embeddings_list, vector_db_metadata, batch_id, job_id, source_filename):
client = weaviate.Client(
url=vector_db_metadata.environment,
auth_client_secret=weaviate.AuthApiKey(api_key=os.getenv('VECTOR_DB_KEY')),
) if vector_db_metadata.environment != os.getenv('LOCAL_VECTOR_DB') else weaviate.Client(url=vector_db_metadata.environment)
index = client.schema.get()
class_list = [class_dict["class"] for class_dict in index["classes"]]
if not index or not vector_db_metadata.index_name in class_list:
logging.error(f"Collection {vector_db_metadata.index_name} does not exist at cluster URL {vector_db_metadata.environment}")
return None
logging.info(f"Starting Weaviate upsert for {len(text_embeddings_list)} vectors")
try:
with client.batch(batch_size=config.PINECONE_BATCH_SIZE, dynamic=True, num_workers=2) as batch:
for i, (text, vector) in enumerate(text_embeddings_list):
properties = {
"source_data": text,
"vectoflow_id": generate_uuid_from_tuple((job_id, batch_id, i)),
"source_document": source_filename
}
client.batch.add_data_object(
properties,
vector_db_metadata.index_name,
vector=vector
)
except Exception as e:
logging.error('Error writing embeddings to weaviate: %s', e)
return None
logging.info(f"Successfully uploaded {len(text_embeddings_list)} vectors to Weaviate")
return len(text_embeddings_list)
def create_milvus_source_chunk_dict(text_embeddings_list, batch_id, job_id, source_filename):
ids = []
source_texts = []
embeddings = []
source_filenames = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
ids.append(generate_uuid_from_tuple((job_id, batch_id, i)))
source_texts.append(source_text)
embeddings.append(embedding)
source_filenames.append(source_filename)
return [ids, source_texts, embeddings, source_filenames]
def write_embeddings_to_milvus(upsert_list, vector_db_metadata):
if vector_db_metadata.environment != os.getenv('LOCAL_VECTOR_DB'):
connections.connect("default",
uri = vector_db_metadata.environment,
token = os.getenv('VECTOR_DB_KEY')
)
else:
connections.connect("default",
host = vector_db_metadata.environment
)
collection = Collection(vector_db_metadata.index_name)
if not collection:
logging.error(f"Index {vector_db_metadata.index_name} does not exist in environment {vector_db_metadata.environment}")
return None
logging.info(f"Starting Milvus insert for {len(upsert_list)} vectors")
batch_size = config.PINECONE_BATCH_SIZE
vectors_uploaded = 0
for i in range(0,len(upsert_list), batch_size):
try:
insert_response = collection.insert(upsert_list[i:i+batch_size])
vectors_uploaded += insert_response.insert_count
except Exception as e:
logging.error('Error writing embeddings to milvus: %s', e)
return None
logging.info(f"Successfully uploaded {vectors_uploaded} vectors to milvus")
return vectors_uploaded
def create_lancedb_source_chunks(text_embeddings_list, batch_id, job_id, source_filename):
upsert_list = []
for i, (source_text, embedding) in enumerate(text_embeddings_list):
upsert_list.append(
{
"id": generate_uuid_from_tuple((job_id, batch_id, i)),
"vector": embedding,
"source_text": source_text,
"source_document": source_filename
}
)
return upsert_list
def write_embeddings_to_lancedb(upsert_list, batch_id):
# right now only local connection, since its serverless and their cloud is in beta
batch = safe_db_operation(batch_service.get_batch, batch_id)
db = lancedb.connect(batch.vector_db_metadata.environment)
try:
table = db.open_table(batch.vector_db_metadata.index_name)
except FileNotFoundError as e:
logging.info(f"Table {batch.vector_db_metadata.index_name} does not exist in environment {batch.vector_db_metadata.environment}.")
if batch.embeddings_metadata.embeddings_type == EmbeddingsType.OPEN_AI:
schema = pa.schema(
[
pa.field("id", pa.string()),
pa.field("vector", pa.list_(pa.float32(), 1536)),
pa.field("source_text", pa.string()),
pa.field("source_document", pa.string()),
])
table = db.create_table(batch.vector_db_metadata.index_name, schema=schema)
logging.info(f"Created table {batch.vector_db_metadata.index_name} in environment {batch.vector_db_metadata.environment}.")
else:
logging.error(f"Embeddings type {batch.embeddings_metadata.embeddings_type} not supported for LanceDB. Only Open AI")
return None
logging.info(f"Starting LanceDB upsert for {len(upsert_list)} vectors")
batch_size = config.PINECONE_BATCH_SIZE
vectors_uploaded = 0
for i in range(0,len(upsert_list), batch_size):
try:
table.add(data=upsert_list[i:i+batch_size])
vectors_uploaded += batch_size
except Exception as e:
logging.error('Error writing embeddings to lance db:', e)
return None
logging.info(f"Successfully uploaded {vectors_uploaded} vectors to lance db")
return vectors_uploaded
# TODO: refactor into utils
def update_batch_and_job_status(job_id, batch_status, batch_id):
try:
if not job_id and batch_id:
job = safe_db_operation(batch_service.get_batch, batch_id)
job_id = job.job_id
updated_batch_status = safe_db_operation(batch_service.update_batch_status, batch_id, batch_status)
job = safe_db_operation(job_service.update_job_with_batch, job_id, updated_batch_status)
if job.job_status == JobStatus.COMPLETED:
logging.info(f"Job {job_id} completed successfully")
elif job.job_status == JobStatus.PARTIALLY_COMPLETED:
logging.info(f"Job {job_id} partially completed. {job.batches_succeeded} out of {job.total_batches} batches succeeded")
except Exception as e:
logging.error('Error updating job and batch status: %s', e)
safe_db_operation(job_service.update_job_status, job_id, JobStatus.FAILED)
def callback(ch, method, properties, body):
# do these outside the try-catch so it can update the batch status if there's an error
# if this parsing logic fails, the batch shouldn't be marked as failed
data = json.loads(body)
batch_id, chunks_with_embeddings, vector_db_key = data
if vector_db_key:
os.environ["VECTOR_DB_KEY"] = vector_db_key
else:
logging.info("No vector DB key provided")
try:
logging.info("Batch retrieved successfully")
upload_batch(batch_id, chunks_with_embeddings)
logging.info("Batch processed successfully")
except Exception as e:
logging.error('Error processing batch: %s', e)
update_batch_and_job_status(None, batch_id, BatchStatus.FAILED)
ch.basic_ack(delivery_tag=method.delivery_tag)
def start_connection(max_retries=5, retry_delay=5):
for attempt in range(max_retries):
try:
connection_params = create_connection_params()
connection = pika.BlockingConnection(connection_params)
channel = connection.channel()
queue_name = os.getenv('VDB_UPLOAD_QUEUE')
channel.queue_declare(queue=queue_name)
channel.basic_consume(queue=queue_name, on_message_callback=callback)
logging.info('Waiting for messages.')
channel.start_consuming()
return # If successful, exit the function
except AMQPConnectionError as e:
logging.error('AMQP Connection Error: %s', e)
except Exception as e:
logging.error('Unexpected error: %s', e)
finally:
if connection and not connection.is_closed:
connection.close()
logging.info('Retrying to connect in %s seconds (Attempt %s/%s)', retry_delay, attempt + 1, max_retries)
time.sleep(retry_delay)
if __name__ == "__main__":
while True:
try:
start_connection()
except Exception as e:
logging.error('Error in start_connection: %s', e)
logging.info('Restarting start_connection after encountering an error.')
time.sleep(config.PIKA_RETRY_INTERVAL)
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