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from pymilvus import MilvusClient as Client
from pymilvus import FieldSchema, DataType
import json
from typing import Optional
from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
from open_webui.config import (
MILVUS_URI,
)
class MilvusClient:
def __init__(self):
self.collection_prefix = "open_webui"
self.client = Client(uri=MILVUS_URI)
def _result_to_get_result(self, result) -> GetResult:
ids = []
documents = []
metadatas = []
for match in result:
_ids = []
_documents = []
_metadatas = []
for item in match:
_ids.append(item.get("id"))
_documents.append(item.get("data", {}).get("text"))
_metadatas.append(item.get("metadata"))
ids.append(_ids)
documents.append(_documents)
metadatas.append(_metadatas)
return GetResult(
**{
"ids": ids,
"documents": documents,
"metadatas": metadatas,
}
)
def _result_to_search_result(self, result) -> SearchResult:
ids = []
distances = []
documents = []
metadatas = []
for match in result:
_ids = []
_distances = []
_documents = []
_metadatas = []
for item in match:
_ids.append(item.get("id"))
_distances.append(item.get("distance"))
_documents.append(item.get("entity", {}).get("data", {}).get("text"))
_metadatas.append(item.get("entity", {}).get("metadata"))
ids.append(_ids)
distances.append(_distances)
documents.append(_documents)
metadatas.append(_metadatas)
return SearchResult(
**{
"ids": ids,
"distances": distances,
"documents": documents,
"metadatas": metadatas,
}
)
def _create_collection(self, collection_name: str, dimension: int):
schema = self.client.create_schema(
auto_id=False,
enable_dynamic_field=True,
)
schema.add_field(
field_name="id",
datatype=DataType.VARCHAR,
is_primary=True,
max_length=65535,
)
schema.add_field(
field_name="vector",
datatype=DataType.FLOAT_VECTOR,
dim=dimension,
description="vector",
)
schema.add_field(field_name="data", datatype=DataType.JSON, description="data")
schema.add_field(
field_name="metadata", datatype=DataType.JSON, description="metadata"
)
index_params = self.client.prepare_index_params()
index_params.add_index(
field_name="vector",
index_type="HNSW",
metric_type="COSINE",
params={"M": 16, "efConstruction": 100},
)
self.client.create_collection(
collection_name=f"{self.collection_prefix}_{collection_name}",
schema=schema,
index_params=index_params,
)
def has_collection(self, collection_name: str) -> bool:
# Check if the collection exists based on the collection name.
collection_name = collection_name.replace("-", "_")
return self.client.has_collection(
collection_name=f"{self.collection_prefix}_{collection_name}"
)
def delete_collection(self, collection_name: str):
# Delete the collection based on the collection name.
collection_name = collection_name.replace("-", "_")
return self.client.drop_collection(
collection_name=f"{self.collection_prefix}_{collection_name}"
)
def search(
self, collection_name: str, vectors: list[list[float | int]], limit: int
) -> Optional[SearchResult]:
# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
collection_name = collection_name.replace("-", "_")
result = self.client.search(
collection_name=f"{self.collection_prefix}_{collection_name}",
data=vectors,
limit=limit,
output_fields=["data", "metadata"],
)
return self._result_to_search_result(result)
def query(self, collection_name: str, filter: dict, limit: Optional[int] = None):
# Construct the filter string for querying
collection_name = collection_name.replace("-", "_")
if not self.has_collection(collection_name):
return None
filter_string = " && ".join(
[
f'metadata["{key}"] == {json.dumps(value)}'
for key, value in filter.items()
]
)
max_limit = 16383 # The maximum number of records per request
all_results = []
if limit is None:
limit = float("inf") # Use infinity as a placeholder for no limit
# Initialize offset and remaining to handle pagination
offset = 0
remaining = limit
try:
# Loop until there are no more items to fetch or the desired limit is reached
while remaining > 0:
print("remaining", remaining)
current_fetch = min(
max_limit, remaining
) # Determine how many items to fetch in this iteration
results = self.client.query(
collection_name=f"{self.collection_prefix}_{collection_name}",
filter=filter_string,
output_fields=["*"],
limit=current_fetch,
offset=offset,
)
if not results:
break
all_results.extend(results)
results_count = len(results)
remaining -= (
results_count # Decrease remaining by the number of items fetched
)
offset += results_count
# Break the loop if the results returned are less than the requested fetch count
if results_count < current_fetch:
break
print(all_results)
return self._result_to_get_result([all_results])
except Exception as e:
print(e)
return None
def get(self, collection_name: str) -> Optional[GetResult]:
# Get all the items in the collection.
collection_name = collection_name.replace("-", "_")
result = self.client.query(
collection_name=f"{self.collection_prefix}_{collection_name}",
filter='id != ""',
)
return self._result_to_get_result([result])
def insert(self, collection_name: str, items: list[VectorItem]):
# Insert the items into the collection, if the collection does not exist, it will be created.
collection_name = collection_name.replace("-", "_")
if not self.client.has_collection(
collection_name=f"{self.collection_prefix}_{collection_name}"
):
self._create_collection(
collection_name=collection_name, dimension=len(items[0]["vector"])
)
return self.client.insert(
collection_name=f"{self.collection_prefix}_{collection_name}",
data=[
{
"id": item["id"],
"vector": item["vector"],
"data": {"text": item["text"]},
"metadata": item["metadata"],
}
for item in items
],
)
def upsert(self, collection_name: str, items: list[VectorItem]):
# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
collection_name = collection_name.replace("-", "_")
if not self.client.has_collection(
collection_name=f"{self.collection_prefix}_{collection_name}"
):
self._create_collection(
collection_name=collection_name, dimension=len(items[0]["vector"])
)
return self.client.upsert(
collection_name=f"{self.collection_prefix}_{collection_name}",
data=[
{
"id": item["id"],
"vector": item["vector"],
"data": {"text": item["text"]},
"metadata": item["metadata"],
}
for item in items
],
)
def delete(
self,
collection_name: str,
ids: Optional[list[str]] = None,
filter: Optional[dict] = None,
):
# Delete the items from the collection based on the ids.
collection_name = collection_name.replace("-", "_")
if ids:
return self.client.delete(
collection_name=f"{self.collection_prefix}_{collection_name}",
ids=ids,
)
elif filter:
# Convert the filter dictionary to a string using JSON_CONTAINS.
filter_string = " && ".join(
[
f'metadata["{key}"] == {json.dumps(value)}'
for key, value in filter.items()
]
)
return self.client.delete(
collection_name=f"{self.collection_prefix}_{collection_name}",
filter=filter_string,
)
def reset(self):
# Resets the database. This will delete all collections and item entries.
collection_names = self.client.list_collections()
for collection_name in collection_names:
if collection_name.startswith(self.collection_prefix):
self.client.drop_collection(collection_name=collection_name)
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