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https://github.com/open-webui/open-webui
synced 2025-05-02 20:11:56 +00:00
enh: add to vector db support
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parent
325ca98773
commit
d394f8b7be
@ -637,6 +637,7 @@ def save_docs_to_vector_db(
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metadata: Optional[dict] = None,
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metadata: Optional[dict] = None,
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overwrite: bool = False,
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overwrite: bool = False,
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split: bool = True,
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split: bool = True,
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add: bool = False,
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) -> bool:
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) -> bool:
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log.info(f"save_docs_to_vector_db {docs} {collection_name}")
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log.info(f"save_docs_to_vector_db {docs} {collection_name}")
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@ -662,42 +663,44 @@ def save_docs_to_vector_db(
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metadata[key] = str(value)
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metadata[key] = str(value)
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try:
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try:
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if overwrite:
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if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
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log.info(f"deleting existing collection {collection_name}")
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VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
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if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
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if VECTOR_DB_CLIENT.has_collection(collection_name=collection_name):
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log.info(f"collection {collection_name} already exists")
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log.info(f"collection {collection_name} already exists")
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return True
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else:
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embedding_function = get_embedding_function(
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app.state.config.RAG_EMBEDDING_ENGINE,
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app.state.config.RAG_EMBEDDING_MODEL,
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app.state.sentence_transformer_ef,
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app.state.config.OPENAI_API_KEY,
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app.state.config.OPENAI_API_BASE_URL,
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app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
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)
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embeddings = embedding_function(
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if overwrite:
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list(map(lambda x: x.replace("\n", " "), texts))
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VECTOR_DB_CLIENT.delete_collection(collection_name=collection_name)
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)
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log.info(f"deleting existing collection {collection_name}")
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VECTOR_DB_CLIENT.insert(
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if add is False:
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collection_name=collection_name,
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return True
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items=[
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{
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"id": str(uuid.uuid4()),
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"text": text,
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"vector": embeddings[idx],
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"metadata": metadatas[idx],
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}
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for idx, text in enumerate(texts)
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],
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)
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return True
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log.info(f"adding to collection {collection_name}")
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embedding_function = get_embedding_function(
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app.state.config.RAG_EMBEDDING_ENGINE,
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app.state.config.RAG_EMBEDDING_MODEL,
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app.state.sentence_transformer_ef,
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app.state.config.OPENAI_API_KEY,
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app.state.config.OPENAI_API_BASE_URL,
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app.state.config.RAG_EMBEDDING_OPENAI_BATCH_SIZE,
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)
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embeddings = embedding_function(
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list(map(lambda x: x.replace("\n", " "), texts))
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)
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VECTOR_DB_CLIENT.insert(
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collection_name=collection_name,
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items=[
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{
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"id": str(uuid.uuid4()),
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"text": text,
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"vector": embeddings[idx],
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"metadata": metadatas[idx],
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}
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for idx, text in enumerate(texts)
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],
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)
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return True
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except Exception as e:
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except Exception as e:
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log.exception(e)
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log.exception(e)
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return False
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return False
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@ -715,37 +718,53 @@ def process_file(
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):
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):
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try:
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try:
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file = Files.get_file_by_id(form_data.file_id)
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file = Files.get_file_by_id(form_data.file_id)
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file_path = file.meta.get("path", f"{UPLOAD_DIR}/{file.filename}")
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collection_name = form_data.collection_name
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collection_name = form_data.collection_name
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if collection_name is None:
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if collection_name is None:
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with open(file_path, "rb") as f:
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collection_name = file.id
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collection_name = calculate_sha256(f)[:63]
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loader = Loader(
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loader = Loader(
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engine=app.state.config.CONTENT_EXTRACTION_ENGINE,
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engine=app.state.config.CONTENT_EXTRACTION_ENGINE,
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TIKA_SERVER_URL=app.state.config.TIKA_SERVER_URL,
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TIKA_SERVER_URL=app.state.config.TIKA_SERVER_URL,
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PDF_EXTRACT_IMAGES=app.state.config.PDF_EXTRACT_IMAGES,
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PDF_EXTRACT_IMAGES=app.state.config.PDF_EXTRACT_IMAGES,
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)
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)
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docs = loader.load(file.filename, file.meta.get("content_type"), file_path)
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file_path = file.meta.get("path", None)
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if file_path:
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docs = loader.load(file.filename, file.meta.get("content_type"), file_path)
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else:
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docs = [
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Document(
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page_content=file.data.get("content", ""),
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metadata={
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"name": file.filename,
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"created_by": file.user_id,
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**file.meta,
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},
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)
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]
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text_content = " ".join([doc.page_content for doc in docs])
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text_content = " ".join([doc.page_content for doc in docs])
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log.debug(f"text_content: {text_content}")
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log.debug(f"text_content: {text_content}")
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hash = calculate_sha256_string(text_content)
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hash = calculate_sha256_string(text_content)
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Files.update_file_data_by_id(
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res = Files.update_file_data_by_id(
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form_data.file_id,
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file.id,
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{"content": text_content},
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{"content": text_content},
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)
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)
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print(res)
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Files.update_file_hash_by_id(form_data.file_id, hash)
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Files.update_file_hash_by_id(form_data.file_id, hash)
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try:
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try:
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result = save_docs_to_vector_db(
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result = save_docs_to_vector_db(
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docs,
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docs=docs,
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collection_name,
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collection_name=collection_name,
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{
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metadata={
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"file_id": form_data.file_id,
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"file_id": form_data.file_id,
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"name": file.meta.get("name", file.filename),
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"name": file.meta.get("name", file.filename),
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"hash": hash,
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},
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},
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add=(True if form_data.collection_name else False),
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)
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)
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if result:
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if result:
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@ -1184,6 +1203,30 @@ def query_collection_handler(
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####################################
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####################################
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class DeleteForm(BaseModel):
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collection_name: str
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file_id: str
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@app.post("/delete")
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def delete_entries_from_collection(form_data: DeleteForm, user=Depends(get_admin_user)):
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try:
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if VECTOR_DB_CLIENT.has_collection(collection_name=form_data.collection_name):
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file = Files.get_file_by_id(form_data.file_id)
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hash = file.hash
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VECTOR_DB_CLIENT.delete(
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collection_name=form_data.collection_name,
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metadata={"hash": hash},
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)
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return {"status": True}
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else:
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return {"status": False}
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except Exception as e:
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log.exception(e)
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return {"status": False}
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@app.post("/reset/db")
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@app.post("/reset/db")
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def reset_vector_db(user=Depends(get_admin_user)):
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def reset_vector_db(user=Depends(get_admin_user)):
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VECTOR_DB_CLIENT.reset()
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VECTOR_DB_CLIENT.reset()
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