mirror of
https://github.com/open-webui/open-webui.git
synced 2025-12-13 21:05:19 +00:00
refac: robust file upload failed handling
This commit is contained in:
Timothy Jaeryang Baek
parent
05732de898
commit
23a51f2d01
2 changed files with 208 additions and 180 deletions
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@ -130,6 +130,17 @@ class FilesTable:
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except Exception:
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return None
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def get_file_by_id_and_user_id(self, id: str, user_id: str) -> Optional[FileModel]:
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with get_db() as db:
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try:
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file = db.query(File).filter_by(id=id, user_id=user_id).first()
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if file:
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return FileModel.model_validate(file)
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else:
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return None
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except Exception:
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return None
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def get_file_metadata_by_id(self, id: str) -> Optional[FileMetadataResponse]:
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with get_db() as db:
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try:
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@ -1407,59 +1407,35 @@ def process_file(
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form_data: ProcessFileForm,
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user=Depends(get_verified_user),
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):
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try:
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if user.role == "admin":
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file = Files.get_file_by_id(form_data.file_id)
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else:
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file = Files.get_file_by_id_and_user_id(form_data.file_id, user.id)
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collection_name = form_data.collection_name
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if file:
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try:
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if collection_name is None:
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collection_name = f"file-{file.id}"
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collection_name = form_data.collection_name
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if form_data.content:
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# Update the content in the file
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# Usage: /files/{file_id}/data/content/update, /files/ (audio file upload pipeline)
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if collection_name is None:
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collection_name = f"file-{file.id}"
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try:
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# /files/{file_id}/data/content/update
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VECTOR_DB_CLIENT.delete_collection(collection_name=f"file-{file.id}")
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except:
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# Audio file upload pipeline
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pass
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if form_data.content:
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# Update the content in the file
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# Usage: /files/{file_id}/data/content/update, /files/ (audio file upload pipeline)
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docs = [
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Document(
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page_content=form_data.content.replace("<br/>", "\n"),
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metadata={
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**file.meta,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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},
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)
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]
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text_content = form_data.content
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elif form_data.collection_name:
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# Check if the file has already been processed and save the content
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# Usage: /knowledge/{id}/file/add, /knowledge/{id}/file/update
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result = VECTOR_DB_CLIENT.query(
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collection_name=f"file-{file.id}", filter={"file_id": file.id}
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)
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if result is not None and len(result.ids[0]) > 0:
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docs = [
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Document(
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page_content=result.documents[0][idx],
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metadata=result.metadatas[0][idx],
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try:
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# /files/{file_id}/data/content/update
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VECTOR_DB_CLIENT.delete_collection(
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collection_name=f"file-{file.id}"
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)
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for idx, id in enumerate(result.ids[0])
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]
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else:
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except:
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# Audio file upload pipeline
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pass
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docs = [
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Document(
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page_content=file.data.get("content", ""),
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page_content=form_data.content.replace("<br/>", "\n"),
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metadata={
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**file.meta,
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"name": file.filename,
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@ -1470,149 +1446,190 @@ def process_file(
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)
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]
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text_content = file.data.get("content", "")
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else:
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# Process the file and save the content
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# Usage: /files/
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file_path = file.path
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if file_path:
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file_path = Storage.get_file(file_path)
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loader = Loader(
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engine=request.app.state.config.CONTENT_EXTRACTION_ENGINE,
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DATALAB_MARKER_API_KEY=request.app.state.config.DATALAB_MARKER_API_KEY,
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DATALAB_MARKER_API_BASE_URL=request.app.state.config.DATALAB_MARKER_API_BASE_URL,
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DATALAB_MARKER_ADDITIONAL_CONFIG=request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG,
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DATALAB_MARKER_SKIP_CACHE=request.app.state.config.DATALAB_MARKER_SKIP_CACHE,
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DATALAB_MARKER_FORCE_OCR=request.app.state.config.DATALAB_MARKER_FORCE_OCR,
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DATALAB_MARKER_PAGINATE=request.app.state.config.DATALAB_MARKER_PAGINATE,
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DATALAB_MARKER_STRIP_EXISTING_OCR=request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR,
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DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION=request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION,
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DATALAB_MARKER_FORMAT_LINES=request.app.state.config.DATALAB_MARKER_FORMAT_LINES,
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DATALAB_MARKER_USE_LLM=request.app.state.config.DATALAB_MARKER_USE_LLM,
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DATALAB_MARKER_OUTPUT_FORMAT=request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT,
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EXTERNAL_DOCUMENT_LOADER_URL=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL,
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EXTERNAL_DOCUMENT_LOADER_API_KEY=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
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TIKA_SERVER_URL=request.app.state.config.TIKA_SERVER_URL,
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DOCLING_SERVER_URL=request.app.state.config.DOCLING_SERVER_URL,
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DOCLING_PARAMS={
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"do_ocr": request.app.state.config.DOCLING_DO_OCR,
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"force_ocr": request.app.state.config.DOCLING_FORCE_OCR,
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"ocr_engine": request.app.state.config.DOCLING_OCR_ENGINE,
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"ocr_lang": request.app.state.config.DOCLING_OCR_LANG,
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"pdf_backend": request.app.state.config.DOCLING_PDF_BACKEND,
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"table_mode": request.app.state.config.DOCLING_TABLE_MODE,
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"pipeline": request.app.state.config.DOCLING_PIPELINE,
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"do_picture_description": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
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"picture_description_mode": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
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"picture_description_local": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
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"picture_description_api": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
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},
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PDF_EXTRACT_IMAGES=request.app.state.config.PDF_EXTRACT_IMAGES,
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DOCUMENT_INTELLIGENCE_ENDPOINT=request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
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DOCUMENT_INTELLIGENCE_KEY=request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
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MISTRAL_OCR_API_KEY=request.app.state.config.MISTRAL_OCR_API_KEY,
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)
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docs = loader.load(
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file.filename, file.meta.get("content_type"), file_path
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text_content = form_data.content
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elif form_data.collection_name:
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# Check if the file has already been processed and save the content
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# Usage: /knowledge/{id}/file/add, /knowledge/{id}/file/update
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result = VECTOR_DB_CLIENT.query(
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collection_name=f"file-{file.id}", filter={"file_id": file.id}
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)
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docs = [
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Document(
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page_content=doc.page_content,
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metadata={
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**doc.metadata,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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},
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)
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for doc in docs
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]
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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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**file.meta,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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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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log.debug(f"text_content: {text_content}")
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Files.update_file_data_by_id(
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file.id,
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{"content": text_content},
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)
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hash = calculate_sha256_string(text_content)
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Files.update_file_hash_by_id(file.id, hash)
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if request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL:
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Files.update_file_data_by_id(file.id, {"status": "completed"})
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return {
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"status": True,
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"collection_name": None,
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"filename": file.filename,
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"content": text_content,
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}
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else:
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try:
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result = save_docs_to_vector_db(
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request,
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docs=docs,
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collection_name=collection_name,
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metadata={
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"file_id": file.id,
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"name": file.filename,
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"hash": hash,
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},
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add=(True if form_data.collection_name else False),
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user=user,
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)
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log.info(f"added {len(docs)} items to collection {collection_name}")
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if result:
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Files.update_file_metadata_by_id(
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file.id,
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{
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"collection_name": collection_name,
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},
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)
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Files.update_file_data_by_id(
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file.id,
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{"status": "completed"},
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)
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return {
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"status": True,
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"collection_name": collection_name,
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"filename": file.filename,
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"content": text_content,
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}
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if result is not None and len(result.ids[0]) > 0:
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docs = [
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Document(
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page_content=result.documents[0][idx],
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metadata=result.metadatas[0][idx],
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)
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for idx, id in enumerate(result.ids[0])
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]
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else:
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raise Exception("Error saving document to vector database")
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except Exception as e:
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raise e
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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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**file.meta,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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},
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)
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]
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except Exception as e:
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log.exception(e)
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if "No pandoc was found" in str(e):
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
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text_content = file.data.get("content", "")
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else:
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# Process the file and save the content
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# Usage: /files/
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file_path = file.path
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if file_path:
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file_path = Storage.get_file(file_path)
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loader = Loader(
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engine=request.app.state.config.CONTENT_EXTRACTION_ENGINE,
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DATALAB_MARKER_API_KEY=request.app.state.config.DATALAB_MARKER_API_KEY,
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DATALAB_MARKER_API_BASE_URL=request.app.state.config.DATALAB_MARKER_API_BASE_URL,
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DATALAB_MARKER_ADDITIONAL_CONFIG=request.app.state.config.DATALAB_MARKER_ADDITIONAL_CONFIG,
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DATALAB_MARKER_SKIP_CACHE=request.app.state.config.DATALAB_MARKER_SKIP_CACHE,
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DATALAB_MARKER_FORCE_OCR=request.app.state.config.DATALAB_MARKER_FORCE_OCR,
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DATALAB_MARKER_PAGINATE=request.app.state.config.DATALAB_MARKER_PAGINATE,
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DATALAB_MARKER_STRIP_EXISTING_OCR=request.app.state.config.DATALAB_MARKER_STRIP_EXISTING_OCR,
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DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION=request.app.state.config.DATALAB_MARKER_DISABLE_IMAGE_EXTRACTION,
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DATALAB_MARKER_FORMAT_LINES=request.app.state.config.DATALAB_MARKER_FORMAT_LINES,
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DATALAB_MARKER_USE_LLM=request.app.state.config.DATALAB_MARKER_USE_LLM,
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DATALAB_MARKER_OUTPUT_FORMAT=request.app.state.config.DATALAB_MARKER_OUTPUT_FORMAT,
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EXTERNAL_DOCUMENT_LOADER_URL=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_URL,
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EXTERNAL_DOCUMENT_LOADER_API_KEY=request.app.state.config.EXTERNAL_DOCUMENT_LOADER_API_KEY,
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TIKA_SERVER_URL=request.app.state.config.TIKA_SERVER_URL,
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DOCLING_SERVER_URL=request.app.state.config.DOCLING_SERVER_URL,
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DOCLING_PARAMS={
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"do_ocr": request.app.state.config.DOCLING_DO_OCR,
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"force_ocr": request.app.state.config.DOCLING_FORCE_OCR,
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"ocr_engine": request.app.state.config.DOCLING_OCR_ENGINE,
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"ocr_lang": request.app.state.config.DOCLING_OCR_LANG,
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"pdf_backend": request.app.state.config.DOCLING_PDF_BACKEND,
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"table_mode": request.app.state.config.DOCLING_TABLE_MODE,
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"pipeline": request.app.state.config.DOCLING_PIPELINE,
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"do_picture_description": request.app.state.config.DOCLING_DO_PICTURE_DESCRIPTION,
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"picture_description_mode": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_MODE,
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"picture_description_local": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_LOCAL,
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"picture_description_api": request.app.state.config.DOCLING_PICTURE_DESCRIPTION_API,
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},
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PDF_EXTRACT_IMAGES=request.app.state.config.PDF_EXTRACT_IMAGES,
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DOCUMENT_INTELLIGENCE_ENDPOINT=request.app.state.config.DOCUMENT_INTELLIGENCE_ENDPOINT,
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DOCUMENT_INTELLIGENCE_KEY=request.app.state.config.DOCUMENT_INTELLIGENCE_KEY,
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MISTRAL_OCR_API_KEY=request.app.state.config.MISTRAL_OCR_API_KEY,
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)
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docs = loader.load(
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file.filename, file.meta.get("content_type"), file_path
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)
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docs = [
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Document(
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page_content=doc.page_content,
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metadata={
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**doc.metadata,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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},
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)
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for doc in docs
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]
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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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**file.meta,
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"name": file.filename,
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"created_by": file.user_id,
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"file_id": file.id,
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"source": file.filename,
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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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log.debug(f"text_content: {text_content}")
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Files.update_file_data_by_id(
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file.id,
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{"content": text_content},
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)
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else:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e),
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hash = calculate_sha256_string(text_content)
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Files.update_file_hash_by_id(file.id, hash)
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if request.app.state.config.BYPASS_EMBEDDING_AND_RETRIEVAL:
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Files.update_file_data_by_id(file.id, {"status": "completed"})
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return {
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"status": True,
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"collection_name": None,
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"filename": file.filename,
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"content": text_content,
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}
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else:
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try:
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result = save_docs_to_vector_db(
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request,
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docs=docs,
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collection_name=collection_name,
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metadata={
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"file_id": file.id,
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"name": file.filename,
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"hash": hash,
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},
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add=(True if form_data.collection_name else False),
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user=user,
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)
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log.info(f"added {len(docs)} items to collection {collection_name}")
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if result:
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Files.update_file_metadata_by_id(
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file.id,
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{
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"collection_name": collection_name,
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},
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)
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Files.update_file_data_by_id(
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file.id,
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{"status": "completed"},
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)
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return {
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"status": True,
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"collection_name": collection_name,
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"filename": file.filename,
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"content": text_content,
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}
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else:
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raise Exception("Error saving document to vector database")
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except Exception as e:
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raise e
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except Exception as e:
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log.exception(e)
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Files.update_file_data_by_id(
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file.id,
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{"status": "failed"},
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)
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if "No pandoc was found" in str(e):
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=ERROR_MESSAGES.PANDOC_NOT_INSTALLED,
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)
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else:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e),
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)
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else:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND, detail=ERROR_MESSAGES.NOT_FOUND
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)
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class ProcessTextForm(BaseModel):
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name: str
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|
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