mirror of
https://github.com/open-webui/open-webui
synced 2024-11-06 00:32:05 +00:00
1103 lines
35 KiB
Python
1103 lines
35 KiB
Python
from fastapi import (
|
|
FastAPI,
|
|
Depends,
|
|
HTTPException,
|
|
status,
|
|
UploadFile,
|
|
File,
|
|
Form,
|
|
)
|
|
from fastapi.middleware.cors import CORSMiddleware
|
|
import os, shutil, logging, re
|
|
|
|
from pathlib import Path
|
|
from typing import List, Union, Sequence
|
|
|
|
from chromadb.utils.batch_utils import create_batches
|
|
|
|
from langchain_community.document_loaders import (
|
|
WebBaseLoader,
|
|
TextLoader,
|
|
PyPDFLoader,
|
|
CSVLoader,
|
|
BSHTMLLoader,
|
|
Docx2txtLoader,
|
|
UnstructuredEPubLoader,
|
|
UnstructuredWordDocumentLoader,
|
|
UnstructuredMarkdownLoader,
|
|
UnstructuredXMLLoader,
|
|
UnstructuredRSTLoader,
|
|
UnstructuredExcelLoader,
|
|
UnstructuredPowerPointLoader,
|
|
YoutubeLoader,
|
|
)
|
|
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
|
|
|
|
import sentence_transformers
|
|
|
|
from apps.webui.models.documents import (
|
|
Documents,
|
|
DocumentForm,
|
|
DocumentResponse,
|
|
)
|
|
|
|
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,
|
|
search_web,
|
|
)
|
|
|
|
from utils.misc import (
|
|
calculate_sha256,
|
|
calculate_sha256_string,
|
|
sanitize_filename,
|
|
extract_folders_after_data_docs,
|
|
)
|
|
from utils.utils import get_current_user, get_admin_user
|
|
|
|
from config import (
|
|
ENV,
|
|
SRC_LOG_LEVELS,
|
|
UPLOAD_DIR,
|
|
DOCS_DIR,
|
|
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,
|
|
SEARXNG_QUERY_URL,
|
|
GOOGLE_PSE_API_KEY,
|
|
GOOGLE_PSE_ENGINE_ID,
|
|
BRAVE_SEARCH_API_KEY,
|
|
SERPSTACK_API_KEY,
|
|
SERPSTACK_HTTPS,
|
|
SERPER_API_KEY,
|
|
RAG_WEB_SEARCH_RESULT_COUNT,
|
|
RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
|
|
AppConfig,
|
|
)
|
|
|
|
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.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_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.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.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 == "":
|
|
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:
|
|
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,
|
|
)
|
|
|
|
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,
|
|
}
|
|
|
|
|
|
@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,
|
|
},
|
|
}
|
|
|
|
|
|
@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
|
|
|
|
|
|
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 != None:
|
|
app.state.config.OPENAI_API_BASE_URL = form_data.openai_config.url
|
|
app.state.config.OPENAI_API_KEY = form_data.openai_config.key
|
|
|
|
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,
|
|
)
|
|
|
|
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,
|
|
},
|
|
}
|
|
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,
|
|
"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": {
|
|
"enable": 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,
|
|
"result_count": app.state.config.RAG_WEB_SEARCH_RESULT_COUNT,
|
|
"concurrent_requests": app.state.config.RAG_WEB_SEARCH_CONCURRENT_REQUESTS,
|
|
},
|
|
},
|
|
}
|
|
|
|
|
|
class ChunkParamUpdateForm(BaseModel):
|
|
chunk_size: int
|
|
chunk_overlap: int
|
|
|
|
|
|
class YoutubeLoaderConfig(BaseModel):
|
|
language: List[str]
|
|
translation: Optional[str] = None
|
|
|
|
|
|
class WebSearchConfig(BaseModel):
|
|
enable: 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
|
|
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
|
|
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.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.enable
|
|
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.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,
|
|
"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": {
|
|
"enable": 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,
|
|
"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_current_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_current_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_current_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_current_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_current_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 WebBaseLoader(
|
|
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
|
|
|
|
|
|
@app.post("/web/search")
|
|
def store_web_search(form_data: SearchForm, user=Depends(get_current_user)):
|
|
try:
|
|
try:
|
|
web_results = search_web(
|
|
app.state.config.RAG_WEB_SEARCH_ENGINE, form_data.query
|
|
)
|
|
except Exception as e:
|
|
log.exception(e)
|
|
raise HTTPException(
|
|
status_code=status.HTTP_400_BAD_REQUEST,
|
|
detail=ERROR_MESSAGES.WEB_SEARCH_ERROR,
|
|
)
|
|
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]
|
|
|
|
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,
|
|
)
|
|
|
|
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
|
|
|
|
|
|
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",
|
|
]
|
|
|
|
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 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_current_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 TextRAGForm(BaseModel):
|
|
name: str
|
|
content: str
|
|
collection_name: Optional[str] = None
|
|
|
|
|
|
@app.post("/text")
|
|
def store_text(
|
|
form_data: TextRAGForm,
|
|
user=Depends(get_current_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")
|
|
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
|
|
|
|
|
|
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)}
|