github-actions[bot]
GitHub deploy: 45fae65ec18d0b265b7151dd469053c8494a72e8
ccaf67e
from fastapi import (
FastAPI,
Depends,
HTTPException,
status,
UploadFile,
File,
Form,
)
from fastapi.middleware.cors import CORSMiddleware
import requests
import os, shutil, logging, re
from datetime import datetime
from pathlib import Path
from typing import List, Union, Sequence, Iterator, Any
from chromadb.utils.batch_utils import create_batches
from langchain_core.documents import Document
from langchain_community.document_loaders import (
WebBaseLoader,
TextLoader,
PyPDFLoader,
CSVLoader,
BSHTMLLoader,
Docx2txtLoader,
UnstructuredEPubLoader,
UnstructuredWordDocumentLoader,
UnstructuredMarkdownLoader,
UnstructuredXMLLoader,
UnstructuredRSTLoader,
UnstructuredExcelLoader,
UnstructuredPowerPointLoader,
YoutubeLoader,
OutlookMessageLoader,
)
from langchain.text_splitter import RecursiveCharacterTextSplitter
import validators
import urllib.parse
import socket
from pydantic import BaseModel
from typing import Optional
import mimetypes
import uuid
import json
from apps.webui.models.documents import (
Documents,
DocumentForm,
DocumentResponse,
)
from apps.webui.models.files import (
Files,
)
from apps.rag.utils import (
get_model_path,
get_embedding_function,
query_doc,
query_doc_with_hybrid_search,
query_collection,
query_collection_with_hybrid_search,
)
from apps.rag.search.brave import search_brave
from apps.rag.search.google_pse import search_google_pse
from apps.rag.search.main import SearchResult
from apps.rag.search.searxng import search_searxng
from apps.rag.search.serper import search_serper
from apps.rag.search.serpstack import search_serpstack
from apps.rag.search.serply import search_serply
from apps.rag.search.duckduckgo import search_duckduckgo
from apps.rag.search.tavily import search_tavily
from apps.rag.search.jina_search import search_jina
from utils.misc import (
calculate_sha256,
calculate_sha256_string,
sanitize_filename,
extract_folders_after_data_docs,
)
from utils.utils import get_verified_user, get_admin_user
from config import (
AppConfig,
ENV,
SRC_LOG_LEVELS,
UPLOAD_DIR,
DOCS_DIR,
CONTENT_EXTRACTION_ENGINE,
TIKA_SERVER_URL,
RAG_TOP_K,
RAG_RELEVANCE_THRESHOLD,
RAG_EMBEDDING_ENGINE,
RAG_EMBEDDING_MODEL,
RAG_EMBEDDING_MODEL_AUTO_UPDATE,
RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
ENABLE_RAG_HYBRID_SEARCH,
ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
RAG_RERANKING_MODEL,
PDF_EXTRACT_IMAGES,
RAG_RERANKING_MODEL_AUTO_UPDATE,
RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
RAG_OPENAI_API_BASE_URL,
RAG_OPENAI_API_KEY,
DEVICE_TYPE,
CHROMA_CLIENT,
CHUNK_SIZE,
CHUNK_OVERLAP,
RAG_TEMPLATE,
ENABLE_RAG_LOCAL_WEB_FETCH,
YOUTUBE_LOADER_LANGUAGE,
ENABLE_RAG_WEB_SEARCH,
RAG_WEB_SEARCH_ENGINE,
RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
SEARXNG_QUERY_URL,
GOOGLE_PSE_API_KEY,
GOOGLE_PSE_ENGINE_ID,
BRAVE_SEARCH_API_KEY,
SERPSTACK_API_KEY,
SERPSTACK_HTTPS,
SERPER_API_KEY,
SERPLY_API_KEY,
TAVILY_API_KEY,
RAG_WEB_SEARCH_RESULT_COUNT,
RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
from constants import ERROR_MESSAGES
log = logging.getLogger(__name__)
log.setLevel(SRC_LOG_LEVELS["RAG"])
app = FastAPI()
app.state.config = AppConfig()
app.state.config.TOP_K = RAG_TOP_K
app.state.config.RELEVANCE_THRESHOLD = RAG_RELEVANCE_THRESHOLD
app.state.config.ENABLE_RAG_HYBRID_SEARCH = ENABLE_RAG_HYBRID_SEARCH
app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION
)
app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL
app.state.config.CHUNK_SIZE = CHUNK_SIZE
app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP
app.state.config.RAG_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
app.state.config.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE = RAG_EMBEDDING_OPENAI_BATCH_SIZE
app.state.config.RAG_RERANKING_MODEL = RAG_RERANKING_MODEL
app.state.config.RAG_TEMPLATE = RAG_TEMPLATE
app.state.config.OPENAI_API_BASE_URL = RAG_OPENAI_API_BASE_URL
app.state.config.OPENAI_API_KEY = RAG_OPENAI_API_KEY
app.state.config.PDF_EXTRACT_IMAGES = PDF_EXTRACT_IMAGES
app.state.config.YOUTUBE_LOADER_LANGUAGE = YOUTUBE_LOADER_LANGUAGE
app.state.YOUTUBE_LOADER_TRANSLATION = None
app.state.config.ENABLE_RAG_WEB_SEARCH = ENABLE_RAG_WEB_SEARCH
app.state.config.RAG_WEB_SEARCH_ENGINE = RAG_WEB_SEARCH_ENGINE
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST = RAG_WEB_SEARCH_DOMAIN_FILTER_LIST
app.state.config.SEARXNG_QUERY_URL = SEARXNG_QUERY_URL
app.state.config.GOOGLE_PSE_API_KEY = GOOGLE_PSE_API_KEY
app.state.config.GOOGLE_PSE_ENGINE_ID = GOOGLE_PSE_ENGINE_ID
app.state.config.BRAVE_SEARCH_API_KEY = BRAVE_SEARCH_API_KEY
app.state.config.SERPSTACK_API_KEY = SERPSTACK_API_KEY
app.state.config.SERPSTACK_HTTPS = SERPSTACK_HTTPS
app.state.config.SERPER_API_KEY = SERPER_API_KEY
app.state.config.SERPLY_API_KEY = SERPLY_API_KEY
app.state.config.TAVILY_API_KEY = TAVILY_API_KEY
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = RAG_WEB_SEARCH_RESULT_COUNT
app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = RAG_WEB_SEARCH_CONCURRENT_REQUESTS
def update_embedding_model(
embedding_model: str,
update_model: bool = False,
):
if embedding_model and app.state.config.RAG_EMBEDDING_ENGINE == "":
import sentence_transformers
app.state.sentence_transformer_ef = sentence_transformers.SentenceTransformer(
get_model_path(embedding_model, update_model),
device=DEVICE_TYPE,
trust_remote_code=RAG_EMBEDDING_MODEL_TRUST_REMOTE_CODE,
)
else:
app.state.sentence_transformer_ef = None
def update_reranking_model(
reranking_model: str,
update_model: bool = False,
):
if reranking_model:
import sentence_transformers
app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
get_model_path(reranking_model, update_model),
device=DEVICE_TYPE,
trust_remote_code=RAG_RERANKING_MODEL_TRUST_REMOTE_CODE,
)
else:
app.state.sentence_transformer_rf = None
update_embedding_model(
app.state.config.RAG_EMBEDDING_MODEL,
RAG_EMBEDDING_MODEL_AUTO_UPDATE,
)
update_reranking_model(
app.state.config.RAG_RERANKING_MODEL,
RAG_RERANKING_MODEL_AUTO_UPDATE,
)
app.state.EMBEDDING_FUNCTION = get_embedding_function(
app.state.config.RAG_EMBEDDING_ENGINE,
app.state.config.RAG_EMBEDDING_MODEL,
app.state.sentence_transformer_ef,
app.state.config.OPENAI_API_KEY,
app.state.config.OPENAI_API_BASE_URL,
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class CollectionNameForm(BaseModel):
collection_name: Optional[str] = "test"
class UrlForm(CollectionNameForm):
url: str
class SearchForm(CollectionNameForm):
query: str
@app.get("/")
async def get_status():
return {
"status": True,
"chunk_size": app.state.config.CHUNK_SIZE,
"chunk_overlap": app.state.config.CHUNK_OVERLAP,
"template": app.state.config.RAG_TEMPLATE,
"embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
"reranking_model": app.state.config.RAG_RERANKING_MODEL,
"openai_batch_size": app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
}
@app.get("/embedding")
async def get_embedding_config(user=Depends(get_admin_user)):
return {
"status": True,
"embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
"openai_config": {
"url": app.state.config.OPENAI_API_BASE_URL,
"key": app.state.config.OPENAI_API_KEY,
"batch_size": app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
},
}
@app.get("/reranking")
async def get_reraanking_config(user=Depends(get_admin_user)):
return {
"status": True,
"reranking_model": app.state.config.RAG_RERANKING_MODEL,
}
class OpenAIConfigForm(BaseModel):
url: str
key: str
batch_size: Optional[int] = None
class EmbeddingModelUpdateForm(BaseModel):
openai_config: Optional[OpenAIConfigForm] = None
embedding_engine: str
embedding_model: str
@app.post("/embedding/update")
async def update_embedding_config(
form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
):
log.info(
f"Updating embedding model: {app.state.config.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
)
try:
app.state.config.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
app.state.config.RAG_EMBEDDING_MODEL = form_data.embedding_model
if app.state.config.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
if form_data.openai_config is not None:
app.state.config.OPENAI_API_BASE_URL = form_data.openai_config.url
app.state.config.OPENAI_API_KEY = form_data.openai_config.key
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE = (
form_data.openai_config.batch_size
if form_data.openai_config.batch_size
else 1
)
update_embedding_model(app.state.config.RAG_EMBEDDING_MODEL)
app.state.EMBEDDING_FUNCTION = get_embedding_function(
app.state.config.RAG_EMBEDDING_ENGINE,
app.state.config.RAG_EMBEDDING_MODEL,
app.state.sentence_transformer_ef,
app.state.config.OPENAI_API_KEY,
app.state.config.OPENAI_API_BASE_URL,
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
return {
"status": True,
"embedding_engine": app.state.config.RAG_EMBEDDING_ENGINE,
"embedding_model": app.state.config.RAG_EMBEDDING_MODEL,
"openai_config": {
"url": app.state.config.OPENAI_API_BASE_URL,
"key": app.state.config.OPENAI_API_KEY,
"batch_size": app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
},
}
except Exception as e:
log.exception(f"Problem updating embedding model: {e}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=ERROR_MESSAGES.DEFAULT(e),
)
class RerankingModelUpdateForm(BaseModel):
reranking_model: str
@app.post("/reranking/update")
async def update_reranking_config(
form_data: RerankingModelUpdateForm, user=Depends(get_admin_user)
):
log.info(
f"Updating reranking model: {app.state.config.RAG_RERANKING_MODEL} to {form_data.reranking_model}"
)
try:
app.state.config.RAG_RERANKING_MODEL = form_data.reranking_model
update_reranking_model(app.state.config.RAG_RERANKING_MODEL), True
return {
"status": True,
"reranking_model": app.state.config.RAG_RERANKING_MODEL,
}
except Exception as e:
log.exception(f"Problem updating reranking model: {e}")
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=ERROR_MESSAGES.DEFAULT(e),
)
@app.get("/config")
async def get_rag_config(user=Depends(get_admin_user)):
return {
"status": True,
"pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
"content_extraction": {
"engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
"tika_server_url": app.state.config.TIKA_SERVER_URL,
},
"chunk": {
"chunk_size": app.state.config.CHUNK_SIZE,
"chunk_overlap": app.state.config.CHUNK_OVERLAP,
},
"youtube": {
"language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
"translation": app.state.YOUTUBE_LOADER_TRANSLATION,
},
"web": {
"ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
"search": {
"enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
"engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
"searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
"google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
"google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
"brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
"serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
"serpstack_https": app.state.config.SERPSTACK_HTTPS,
"serper_api_key": app.state.config.SERPER_API_KEY,
"serply_api_key": app.state.config.SERPLY_API_KEY,
"tavily_api_key": app.state.config.TAVILY_API_KEY,
"result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
"concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
},
},
}
class ContentExtractionConfig(BaseModel):
engine: str = ""
tika_server_url: Optional[str] = None
class ChunkParamUpdateForm(BaseModel):
chunk_size: int
chunk_overlap: int
class YoutubeLoaderConfig(BaseModel):
language: List[str]
translation: Optional[str] = None
class WebSearchConfig(BaseModel):
enabled: bool
engine: Optional[str] = None
searxng_query_url: Optional[str] = None
google_pse_api_key: Optional[str] = None
google_pse_engine_id: Optional[str] = None
brave_search_api_key: Optional[str] = None
serpstack_api_key: Optional[str] = None
serpstack_https: Optional[bool] = None
serper_api_key: Optional[str] = None
serply_api_key: Optional[str] = None
tavily_api_key: Optional[str] = None
result_count: Optional[int] = None
concurrent_requests: Optional[int] = None
class WebConfig(BaseModel):
search: WebSearchConfig
web_loader_ssl_verification: Optional[bool] = None
class ConfigUpdateForm(BaseModel):
pdf_extract_images: Optional[bool] = None
content_extraction: Optional[ContentExtractionConfig] = None
chunk: Optional[ChunkParamUpdateForm] = None
youtube: Optional[YoutubeLoaderConfig] = None
web: Optional[WebConfig] = None
@app.post("/config/update")
async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
app.state.config.PDF_EXTRACT_IMAGES = (
form_data.pdf_extract_images
if form_data.pdf_extract_images is not None
else app.state.config.PDF_EXTRACT_IMAGES
)
if form_data.content_extraction is not None:
log.info(f"Updating text settings: {form_data.content_extraction}")
app.state.config.CONTENT_EXTRACTION_ENGINE = form_data.content_extraction.engine
app.state.config.TIKA_SERVER_URL = form_data.content_extraction.tika_server_url
if form_data.chunk is not None:
app.state.config.CHUNK_SIZE = form_data.chunk.chunk_size
app.state.config.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
if form_data.youtube is not None:
app.state.config.YOUTUBE_LOADER_LANGUAGE = form_data.youtube.language
app.state.YOUTUBE_LOADER_TRANSLATION = form_data.youtube.translation
if form_data.web is not None:
app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
form_data.web.web_loader_ssl_verification
)
app.state.config.ENABLE_RAG_WEB_SEARCH = form_data.web.search.enabled
app.state.config.RAG_WEB_SEARCH_ENGINE = form_data.web.search.engine
app.state.config.SEARXNG_QUERY_URL = form_data.web.search.searxng_query_url
app.state.config.GOOGLE_PSE_API_KEY = form_data.web.search.google_pse_api_key
app.state.config.GOOGLE_PSE_ENGINE_ID = (
form_data.web.search.google_pse_engine_id
)
app.state.config.BRAVE_SEARCH_API_KEY = (
form_data.web.search.brave_search_api_key
)
app.state.config.SERPSTACK_API_KEY = form_data.web.search.serpstack_api_key
app.state.config.SERPSTACK_HTTPS = form_data.web.search.serpstack_https
app.state.config.SERPER_API_KEY = form_data.web.search.serper_api_key
app.state.config.SERPLY_API_KEY = form_data.web.search.serply_api_key
app.state.config.TAVILY_API_KEY = form_data.web.search.tavily_api_key
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT = form_data.web.search.result_count
app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS = (
form_data.web.search.concurrent_requests
)
return {
"status": True,
"pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
"content_extraction": {
"engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
"tika_server_url": app.state.config.TIKA_SERVER_URL,
},
"chunk": {
"chunk_size": app.state.config.CHUNK_SIZE,
"chunk_overlap": app.state.config.CHUNK_OVERLAP,
},
"youtube": {
"language": app.state.config.YOUTUBE_LOADER_LANGUAGE,
"translation": app.state.YOUTUBE_LOADER_TRANSLATION,
},
"web": {
"ssl_verification": app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
"search": {
"enabled": app.state.config.ENABLE_RAG_WEB_SEARCH,
"engine": app.state.config.RAG_WEB_SEARCH_ENGINE,
"searxng_query_url": app.state.config.SEARXNG_QUERY_URL,
"google_pse_api_key": app.state.config.GOOGLE_PSE_API_KEY,
"google_pse_engine_id": app.state.config.GOOGLE_PSE_ENGINE_ID,
"brave_search_api_key": app.state.config.BRAVE_SEARCH_API_KEY,
"serpstack_api_key": app.state.config.SERPSTACK_API_KEY,
"serpstack_https": app.state.config.SERPSTACK_HTTPS,
"serper_api_key": app.state.config.SERPER_API_KEY,
"serply_api_key": app.state.config.SERPLY_API_KEY,
"tavily_api_key": app.state.config.TAVILY_API_KEY,
"result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
"concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
},
},
}
@app.get("/template")
async def get_rag_template(user=Depends(get_verified_user)):
return {
"status": True,
"template": app.state.config.RAG_TEMPLATE,
}
@app.get("/query/settings")
async def get_query_settings(user=Depends(get_admin_user)):
return {
"status": True,
"template": app.state.config.RAG_TEMPLATE,
"k": app.state.config.TOP_K,
"r": app.state.config.RELEVANCE_THRESHOLD,
"hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
}
class QuerySettingsForm(BaseModel):
k: Optional[int] = None
r: Optional[float] = None
template: Optional[str] = None
hybrid: Optional[bool] = None
@app.post("/query/settings/update")
async def update_query_settings(
form_data: QuerySettingsForm, user=Depends(get_admin_user)
):
app.state.config.RAG_TEMPLATE = (
form_data.template if form_data.template else RAG_TEMPLATE
)
app.state.config.TOP_K = form_data.k if form_data.k else 4
app.state.config.RELEVANCE_THRESHOLD = form_data.r if form_data.r else 0.0
app.state.config.ENABLE_RAG_HYBRID_SEARCH = (
form_data.hybrid if form_data.hybrid else False
)
return {
"status": True,
"template": app.state.config.RAG_TEMPLATE,
"k": app.state.config.TOP_K,
"r": app.state.config.RELEVANCE_THRESHOLD,
"hybrid": app.state.config.ENABLE_RAG_HYBRID_SEARCH,
}
class QueryDocForm(BaseModel):
collection_name: str
query: str
k: Optional[int] = None
r: Optional[float] = None
hybrid: Optional[bool] = None
@app.post("/query/doc")
def query_doc_handler(
form_data: QueryDocForm,
user=Depends(get_verified_user),
):
try:
if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
return query_doc_with_hybrid_search(
collection_name=form_data.collection_name,
query=form_data.query,
embedding_function=app.state.EMBEDDING_FUNCTION,
k=form_data.k if form_data.k else app.state.config.TOP_K,
reranking_function=app.state.sentence_transformer_rf,
r=(
form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
),
)
else:
return query_doc(
collection_name=form_data.collection_name,
query=form_data.query,
embedding_function=app.state.EMBEDDING_FUNCTION,
k=form_data.k if form_data.k else app.state.config.TOP_K,
)
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
class QueryCollectionsForm(BaseModel):
collection_names: List[str]
query: str
k: Optional[int] = None
r: Optional[float] = None
hybrid: Optional[bool] = None
@app.post("/query/collection")
def query_collection_handler(
form_data: QueryCollectionsForm,
user=Depends(get_verified_user),
):
try:
if app.state.config.ENABLE_RAG_HYBRID_SEARCH:
return query_collection_with_hybrid_search(
collection_names=form_data.collection_names,
query=form_data.query,
embedding_function=app.state.EMBEDDING_FUNCTION,
k=form_data.k if form_data.k else app.state.config.TOP_K,
reranking_function=app.state.sentence_transformer_rf,
r=(
form_data.r if form_data.r else app.state.config.RELEVANCE_THRESHOLD
),
)
else:
return query_collection(
collection_names=form_data.collection_names,
query=form_data.query,
embedding_function=app.state.EMBEDDING_FUNCTION,
k=form_data.k if form_data.k else app.state.config.TOP_K,
)
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
@app.post("/youtube")
def store_youtube_video(form_data: UrlForm, user=Depends(get_verified_user)):
try:
loader = YoutubeLoader.from_youtube_url(
form_data.url,
add_video_info=True,
language=app.state.config.YOUTUBE_LOADER_LANGUAGE,
translation=app.state.YOUTUBE_LOADER_TRANSLATION,
)
data = loader.load()
collection_name = form_data.collection_name
if collection_name == "":
collection_name = calculate_sha256_string(form_data.url)[:63]
store_data_in_vector_db(data, collection_name, overwrite=True)
return {
"status": True,
"collection_name": collection_name,
"filename": form_data.url,
}
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
@app.post("/web")
def store_web(form_data: UrlForm, user=Depends(get_verified_user)):
# "https://www.gutenberg.org/files/1727/1727-h/1727-h.htm"
try:
loader = get_web_loader(
form_data.url,
verify_ssl=app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION,
)
data = loader.load()
collection_name = form_data.collection_name
if collection_name == "":
collection_name = calculate_sha256_string(form_data.url)[:63]
store_data_in_vector_db(data, collection_name, overwrite=True)
return {
"status": True,
"collection_name": collection_name,
"filename": form_data.url,
}
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
def get_web_loader(url: Union[str, Sequence[str]], verify_ssl: bool = True):
# Check if the URL is valid
if not validate_url(url):
raise ValueError(ERROR_MESSAGES.INVALID_URL)
return SafeWebBaseLoader(
url,
verify_ssl=verify_ssl,
requests_per_second=RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
continue_on_failure=True,
)
def validate_url(url: Union[str, Sequence[str]]):
if isinstance(url, str):
if isinstance(validators.url(url), validators.ValidationError):
raise ValueError(ERROR_MESSAGES.INVALID_URL)
if not ENABLE_RAG_LOCAL_WEB_FETCH:
# Local web fetch is disabled, filter out any URLs that resolve to private IP addresses
parsed_url = urllib.parse.urlparse(url)
# Get IPv4 and IPv6 addresses
ipv4_addresses, ipv6_addresses = resolve_hostname(parsed_url.hostname)
# Check if any of the resolved addresses are private
# This is technically still vulnerable to DNS rebinding attacks, as we don't control WebBaseLoader
for ip in ipv4_addresses:
if validators.ipv4(ip, private=True):
raise ValueError(ERROR_MESSAGES.INVALID_URL)
for ip in ipv6_addresses:
if validators.ipv6(ip, private=True):
raise ValueError(ERROR_MESSAGES.INVALID_URL)
return True
elif isinstance(url, Sequence):
return all(validate_url(u) for u in url)
else:
return False
def resolve_hostname(hostname):
# Get address information
addr_info = socket.getaddrinfo(hostname, None)
# Extract IP addresses from address information
ipv4_addresses = [info[4][0] for info in addr_info if info[0] == socket.AF_INET]
ipv6_addresses = [info[4][0] for info in addr_info if info[0] == socket.AF_INET6]
return ipv4_addresses, ipv6_addresses
def search_web(engine: str, query: str) -> list[SearchResult]:
"""Search the web using a search engine and return the results as a list of SearchResult objects.
Will look for a search engine API key in environment variables in the following order:
- SEARXNG_QUERY_URL
- GOOGLE_PSE_API_KEY + GOOGLE_PSE_ENGINE_ID
- BRAVE_SEARCH_API_KEY
- SERPSTACK_API_KEY
- SERPER_API_KEY
- SERPLY_API_KEY
- TAVILY_API_KEY
Args:
query (str): The query to search for
"""
# TODO: add playwright to search the web
if engine == "searxng":
if app.state.config.SEARXNG_QUERY_URL:
return search_searxng(
app.state.config.SEARXNG_QUERY_URL,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
else:
raise Exception("No SEARXNG_QUERY_URL found in environment variables")
elif engine == "google_pse":
if (
app.state.config.GOOGLE_PSE_API_KEY
and app.state.config.GOOGLE_PSE_ENGINE_ID
):
return search_google_pse(
app.state.config.GOOGLE_PSE_API_KEY,
app.state.config.GOOGLE_PSE_ENGINE_ID,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
else:
raise Exception(
"No GOOGLE_PSE_API_KEY or GOOGLE_PSE_ENGINE_ID found in environment variables"
)
elif engine == "brave":
if app.state.config.BRAVE_SEARCH_API_KEY:
return search_brave(
app.state.config.BRAVE_SEARCH_API_KEY,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
else:
raise Exception("No BRAVE_SEARCH_API_KEY found in environment variables")
elif engine == "serpstack":
if app.state.config.SERPSTACK_API_KEY:
return search_serpstack(
app.state.config.SERPSTACK_API_KEY,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
https_enabled=app.state.config.SERPSTACK_HTTPS,
)
else:
raise Exception("No SERPSTACK_API_KEY found in environment variables")
elif engine == "serper":
if app.state.config.SERPER_API_KEY:
return search_serper(
app.state.config.SERPER_API_KEY,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
else:
raise Exception("No SERPER_API_KEY found in environment variables")
elif engine == "serply":
if app.state.config.SERPLY_API_KEY:
return search_serply(
app.state.config.SERPLY_API_KEY,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
else:
raise Exception("No SERPLY_API_KEY found in environment variables")
elif engine == "duckduckgo":
return search_duckduckgo(
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
app.state.config.RAG_WEB_SEARCH_DOMAIN_FILTER_LIST,
)
elif engine == "tavily":
if app.state.config.TAVILY_API_KEY:
return search_tavily(
app.state.config.TAVILY_API_KEY,
query,
app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
)
else:
raise Exception("No TAVILY_API_KEY found in environment variables")
elif engine == "jina":
return search_jina(query, app.state.config.RAG_WEB_SEARCH_RESULT_COUNT)
else:
raise Exception("No search engine API key found in environment variables")
@app.post("/web/search")
def store_web_search(form_data: SearchForm, user=Depends(get_verified_user)):
try:
logging.info(
f"trying to web search with {app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query}"
)
web_results = search_web(
app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query
)
except Exception as e:
log.exception(e)
print(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.WEB_SEARCH_ERROR(e),
)
try:
urls = [result.link for result in web_results]
loader = get_web_loader(urls)
data = loader.load()
collection_name = form_data.collection_name
if collection_name == "":
collection_name = calculate_sha256_string(form_data.query)[:63]
store_data_in_vector_db(data, collection_name, overwrite=True)
return {
"status": True,
"collection_name": collection_name,
"filenames": urls,
}
except Exception as e:
log.exception(e)
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> bool:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=app.state.config.CHUNK_SIZE,
chunk_overlap=app.state.config.CHUNK_OVERLAP,
add_start_index=True,
)
docs = text_splitter.split_documents(data)
if len(docs) > 0:
log.info(f"store_data_in_vector_db {docs}")
return store_docs_in_vector_db(docs, collection_name, overwrite), None
else:
raise ValueError(ERROR_MESSAGES.EMPTY_CONTENT)
def store_text_in_vector_db(
text, metadata, collection_name, overwrite: bool = False
) -> bool:
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=app.state.config.CHUNK_SIZE,
chunk_overlap=app.state.config.CHUNK_OVERLAP,
add_start_index=True,
)
docs = text_splitter.create_documents([text], metadatas=[metadata])
return store_docs_in_vector_db(docs, collection_name, overwrite)
def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
log.info(f"store_docs_in_vector_db {docs} {collection_name}")
texts = [doc.page_content for doc in docs]
metadatas = [doc.metadata for doc in docs]
# ChromaDB does not like datetime formats
# for meta-data so convert them to string.
for metadata in metadatas:
for key, value in metadata.items():
if isinstance(value, datetime):
metadata[key] = str(value)
try:
if overwrite:
for collection in CHROMA_CLIENT.list_collections():
if collection_name == collection.name:
log.info(f"deleting existing collection {collection_name}")
CHROMA_CLIENT.delete_collection(name=collection_name)
collection = CHROMA_CLIENT.create_collection(name=collection_name)
embedding_func = get_embedding_function(
app.state.config.RAG_EMBEDDING_ENGINE,
app.state.config.RAG_EMBEDDING_MODEL,
app.state.sentence_transformer_ef,
app.state.config.OPENAI_API_KEY,
app.state.config.OPENAI_API_BASE_URL,
app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
)
embedding_texts = list(map(lambda x: x.replace("\n", " "), texts))
embeddings = embedding_func(embedding_texts)
for batch in create_batches(
api=CHROMA_CLIENT,
ids=[str(uuid.uuid4()) for _ in texts],
metadatas=metadatas,
embeddings=embeddings,
documents=texts,
):
collection.add(*batch)
return True
except Exception as e:
log.exception(e)
if e.__class__.__name__ == "UniqueConstraintError":
return True
return False
class TikaLoader:
def __init__(self, file_path, mime_type=None):
self.file_path = file_path
self.mime_type = mime_type
def load(self) -> List[Document]:
with open(self.file_path, "rb") as f:
data = f.read()
if self.mime_type is not None:
headers = {"Content-Type": self.mime_type}
else:
headers = {}
endpoint = app.state.config.TIKA_SERVER_URL
if not endpoint.endswith("/"):
endpoint += "/"
endpoint += "tika/text"
r = requests.put(endpoint, data=data, headers=headers)
if r.ok:
raw_metadata = r.json()
text = raw_metadata.get("X-TIKA:content", "<No text content found>")
if "Content-Type" in raw_metadata:
headers["Content-Type"] = raw_metadata["Content-Type"]
log.info("Tika extracted text: %s", text)
return [Document(page_content=text, metadata=headers)]
else:
raise Exception(f"Error calling Tika: {r.reason}")
def get_loader(filename: str, file_content_type: str, file_path: str):
file_ext = filename.split(".")[-1].lower()
known_type = True
known_source_ext = [
"go",
"py",
"java",
"sh",
"bat",
"ps1",
"cmd",
"js",
"ts",
"css",
"cpp",
"hpp",
"h",
"c",
"cs",
"sql",
"log",
"ini",
"pl",
"pm",
"r",
"dart",
"dockerfile",
"env",
"php",
"hs",
"hsc",
"lua",
"nginxconf",
"conf",
"m",
"mm",
"plsql",
"perl",
"rb",
"rs",
"db2",
"scala",
"bash",
"swift",
"vue",
"svelte",
"msg",
]
if (
app.state.config.CONTENT_EXTRACTION_ENGINE == "tika"
and app.state.config.TIKA_SERVER_URL
):
if file_ext in known_source_ext or (
file_content_type and file_content_type.find("text/") >= 0
):
loader = TextLoader(file_path, autodetect_encoding=True)
else:
loader = TikaLoader(file_path, file_content_type)
else:
if file_ext == "pdf":
loader = PyPDFLoader(
file_path, extract_images=app.state.config.PDF_EXTRACT_IMAGES
)
elif file_ext == "csv":
loader = CSVLoader(file_path)
elif file_ext == "rst":
loader = UnstructuredRSTLoader(file_path, mode="elements")
elif file_ext == "xml":
loader = UnstructuredXMLLoader(file_path)
elif file_ext in ["htm", "html"]:
loader = BSHTMLLoader(file_path, open_encoding="unicode_escape")
elif file_ext == "md":
loader = UnstructuredMarkdownLoader(file_path)
elif file_content_type == "application/epub+zip":
loader = UnstructuredEPubLoader(file_path)
elif (
file_content_type
== "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
or file_ext in ["doc", "docx"]
):
loader = Docx2txtLoader(file_path)
elif file_content_type in [
"application/vnd.ms-excel",
"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
] or file_ext in ["xls", "xlsx"]:
loader = UnstructuredExcelLoader(file_path)
elif file_content_type in [
"application/vnd.ms-powerpoint",
"application/vnd.openxmlformats-officedocument.presentationml.presentation",
] or file_ext in ["ppt", "pptx"]:
loader = UnstructuredPowerPointLoader(file_path)
elif file_ext == "msg":
loader = OutlookMessageLoader(file_path)
elif file_ext in known_source_ext or (
file_content_type and file_content_type.find("text/") >= 0
):
loader = TextLoader(file_path, autodetect_encoding=True)
else:
loader = TextLoader(file_path, autodetect_encoding=True)
known_type = False
return loader, known_type
@app.post("/doc")
def store_doc(
collection_name: Optional[str] = Form(None),
file: UploadFile = File(...),
user=Depends(get_verified_user),
):
# "https://www.gutenberg.org/files/1727/1727-h/1727-h.htm"
log.info(f"file.content_type: {file.content_type}")
try:
unsanitized_filename = file.filename
filename = os.path.basename(unsanitized_filename)
file_path = f"{UPLOAD_DIR}/{filename}"
contents = file.file.read()
with open(file_path, "wb") as f:
f.write(contents)
f.close()
f = open(file_path, "rb")
if collection_name == None:
collection_name = calculate_sha256(f)[:63]
f.close()
loader, known_type = get_loader(filename, file.content_type, file_path)
data = loader.load()
try:
result = store_data_in_vector_db(data, collection_name)
if result:
return {
"status": True,
"collection_name": collection_name,
"filename": filename,
"known_type": known_type,
}
except Exception as e:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=e,
)
except Exception as e:
log.exception(e)
if "No pandoc was found" in str(e):
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
)
else:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
class ProcessDocForm(BaseModel):
file_id: str
collection_name: Optional[str] = None
@app.post("/process/doc")
def process_doc(
form_data: ProcessDocForm,
user=Depends(get_verified_user),
):
try:
file = Files.get_file_by_id(form_data.file_id)
file_path = file.meta.get("path", f"{UPLOAD_DIR}/{file.filename}")
f = open(file_path, "rb")
collection_name = form_data.collection_name
if collection_name == None:
collection_name = calculate_sha256(f)[:63]
f.close()
loader, known_type = get_loader(
file.filename, file.meta.get("content_type"), file_path
)
data = loader.load()
try:
result = store_data_in_vector_db(data, collection_name)
if result:
return {
"status": True,
"collection_name": collection_name,
"known_type": known_type,
}
except Exception as e:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=e,
)
except Exception as e:
log.exception(e)
if "No pandoc was found" in str(e):
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
)
else:
raise HTTPException(
status_code=status.HTTP_400_BAD_REQUEST,
detail=ERROR_MESSAGES.DEFAULT(e),
)
class TextRAGForm(BaseModel):
name: str
content: str
collection_name: Optional[str] = None
@app.post("/text")
def store_text(
form_data: TextRAGForm,
user=Depends(get_verified_user),
):
collection_name = form_data.collection_name
if collection_name == None:
collection_name = calculate_sha256_string(form_data.content)
result = store_text_in_vector_db(
form_data.content,
metadata={"name": form_data.name, "created_by": user.id},
collection_name=collection_name,
)
if result:
return {"status": True, "collection_name": collection_name}
else:
raise HTTPException(
status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
detail=ERROR_MESSAGES.DEFAULT(),
)
@app.get("/scan")
def scan_docs_dir(user=Depends(get_admin_user)):
for path in Path(DOCS_DIR).rglob("./**/*"):
try:
if path.is_file() and not path.name.startswith("."):
tags = extract_folders_after_data_docs(path)
filename = path.name
file_content_type = mimetypes.guess_type(path)
f = open(path, "rb")
collection_name = calculate_sha256(f)[:63]
f.close()
loader, known_type = get_loader(
filename, file_content_type[0], str(path)
)
data = loader.load()
try:
result = store_data_in_vector_db(data, collection_name)
if result:
sanitized_filename = sanitize_filename(filename)
doc = Documents.get_doc_by_name(sanitized_filename)
if doc == None:
doc = Documents.insert_new_doc(
user.id,
DocumentForm(
**{
"name": sanitized_filename,
"title": filename,
"collection_name": collection_name,
"filename": filename,
"content": (
json.dumps(
{
"tags": list(
map(
lambda name: {"name": name},
tags,
)
)
}
)
if len(tags)
else "{}"
),
}
),
)
except Exception as e:
log.exception(e)
pass
except Exception as e:
log.exception(e)
return True
@app.get("/reset/db")
def reset_vector_db(user=Depends(get_admin_user)):
CHROMA_CLIENT.reset()
@app.get("/reset/uploads")
def reset_upload_dir(user=Depends(get_admin_user)) -> bool:
folder = f"{UPLOAD_DIR}"
try:
# Check if the directory exists
if os.path.exists(folder):
# Iterate over all the files and directories in the specified directory
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path) # Remove the file or link
elif os.path.isdir(file_path):
shutil.rmtree(file_path) # Remove the directory
except Exception as e:
print(f"Failed to delete {file_path}. Reason: {e}")
else:
print(f"The directory {folder} does not exist")
except Exception as e:
print(f"Failed to process the directory {folder}. Reason: {e}")
return True
@app.get("/reset")
def reset(user=Depends(get_admin_user)) -> bool:
folder = f"{UPLOAD_DIR}"
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
log.error("Failed to delete %s. Reason: %s" % (file_path, e))
try:
CHROMA_CLIENT.reset()
except Exception as e:
log.exception(e)
return True
class SafeWebBaseLoader(WebBaseLoader):
"""WebBaseLoader with enhanced error handling for URLs."""
def lazy_load(self) -> Iterator[Document]:
"""Lazy load text from the url(s) in web_path with error handling."""
for path in self.web_paths:
try:
soup = self._scrape(path, bs_kwargs=self.bs_kwargs)
text = soup.get_text(**self.bs_get_text_kwargs)
# Build metadata
metadata = {"source": path}
if title := soup.find("title"):
metadata["title"] = title.get_text()
if description := soup.find("meta", attrs={"name": "description"}):
metadata["description"] = description.get(
"content", "No description found."
)
if html := soup.find("html"):
metadata["language"] = html.get("lang", "No language found.")
yield Document(page_content=text, metadata=metadata)
except Exception as e:
# Log the error and continue with the next URL
log.error(f"Error loading {path}: {e}")
if ENV == "dev":
@app.get("/ef")
async def get_embeddings():
return {"result": app.state.EMBEDDING_FUNCTION("hello world")}
@app.get("/ef/{text}")
async def get_embeddings_text(text: str):
return {"result": app.state.EMBEDDING_FUNCTION(text)}