mirror of
https://github.com/MODSetter/SurfSense.git
synced 2026-04-26 01:06:23 +02:00
feat: enhance document upload process with two-phase indexing and real-time status updates
This commit is contained in:
Anish Sarkar
parent
f56f5a281e
commit
ed2fc5c636
3 changed files with 694 additions and 11 deletions
|
|
@ -33,6 +33,7 @@ from .base import (
|
|||
check_document_by_unique_identifier,
|
||||
check_duplicate_document,
|
||||
get_current_timestamp,
|
||||
safe_set_chunks,
|
||||
)
|
||||
from .markdown_processor import add_received_markdown_file_document
|
||||
|
||||
|
|
@ -1612,3 +1613,316 @@ async def process_file_in_background(
|
|||
|
||||
logging.error(f"Error processing file in background: {error_message}")
|
||||
raise # Re-raise so the wrapper can also handle it
|
||||
|
||||
|
||||
async def process_file_in_background_with_document(
|
||||
document: Document,
|
||||
file_path: str,
|
||||
filename: str,
|
||||
search_space_id: int,
|
||||
user_id: str,
|
||||
session: AsyncSession,
|
||||
task_logger: TaskLoggingService,
|
||||
log_entry: Log,
|
||||
connector: dict | None = None,
|
||||
notification: Notification | None = None,
|
||||
) -> Document | None:
|
||||
"""
|
||||
Process file and update existing pending document (2-phase pattern).
|
||||
|
||||
This function is Phase 2 of the real-time document status updates:
|
||||
- Phase 1 (API): Created document with pending status
|
||||
- Phase 2 (this): Process file and update document to ready/failed
|
||||
|
||||
The document already exists with pending status. This function:
|
||||
1. Parses the file content (markdown, audio, or ETL services)
|
||||
2. Updates the document with content, embeddings, and chunks
|
||||
3. Sets status to 'ready' on success
|
||||
|
||||
Args:
|
||||
document: Existing document with pending status
|
||||
file_path: Path to the uploaded file
|
||||
filename: Original filename
|
||||
search_space_id: ID of the search space
|
||||
user_id: ID of the user
|
||||
session: Database session
|
||||
task_logger: Task logging service
|
||||
log_entry: Log entry for this task
|
||||
connector: Optional connector info for Google Drive files
|
||||
notification: Optional notification for progress updates
|
||||
|
||||
Returns:
|
||||
Updated Document object if successful, None if duplicate content detected
|
||||
"""
|
||||
import os
|
||||
|
||||
from app.config import config as app_config
|
||||
from app.services.llm_service import get_user_long_context_llm
|
||||
from app.utils.blocknote_converter import convert_markdown_to_blocknote
|
||||
|
||||
try:
|
||||
markdown_content = None
|
||||
etl_service = None
|
||||
|
||||
# ===== STEP 1: Parse file content based on type =====
|
||||
|
||||
# Check if the file is a markdown or text file
|
||||
if filename.lower().endswith((".md", ".markdown", ".txt")):
|
||||
# Update notification: parsing stage
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="parsing", stage_message="Reading file"
|
||||
)
|
||||
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Processing markdown/text file: {filename}",
|
||||
{"file_type": "markdown", "processing_stage": "reading_file"},
|
||||
)
|
||||
|
||||
# Read markdown content directly
|
||||
with open(file_path, encoding="utf-8") as f:
|
||||
markdown_content = f.read()
|
||||
etl_service = "MARKDOWN"
|
||||
|
||||
# Clean up temp file
|
||||
with contextlib.suppress(Exception):
|
||||
os.unlink(file_path)
|
||||
|
||||
# Check if the file is an audio file
|
||||
elif filename.lower().endswith(
|
||||
(".mp3", ".mp4", ".mpeg", ".mpga", ".m4a", ".wav", ".webm")
|
||||
):
|
||||
# Update notification: parsing stage (transcription)
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="parsing", stage_message="Transcribing audio"
|
||||
)
|
||||
|
||||
await task_logger.log_task_progress(
|
||||
log_entry,
|
||||
f"Processing audio file for transcription: {filename}",
|
||||
{"file_type": "audio", "processing_stage": "starting_transcription"},
|
||||
)
|
||||
|
||||
# Transcribe audio
|
||||
stt_service_type = (
|
||||
"local"
|
||||
if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/")
|
||||
else "external"
|
||||
)
|
||||
|
||||
if stt_service_type == "local":
|
||||
from app.services.stt_service import stt_service
|
||||
|
||||
result = stt_service.transcribe_file(file_path)
|
||||
transcribed_text = result.get("text", "")
|
||||
if not transcribed_text:
|
||||
raise ValueError("Transcription returned empty text")
|
||||
markdown_content = f"# Transcription of {filename}\n\n{transcribed_text}"
|
||||
else:
|
||||
with open(file_path, "rb") as audio_file:
|
||||
transcription_kwargs = {
|
||||
"model": app_config.STT_SERVICE,
|
||||
"file": audio_file,
|
||||
"api_key": app_config.STT_SERVICE_API_KEY,
|
||||
}
|
||||
if app_config.STT_SERVICE_API_BASE:
|
||||
transcription_kwargs["api_base"] = app_config.STT_SERVICE_API_BASE
|
||||
transcription_response = await atranscription(**transcription_kwargs)
|
||||
transcribed_text = transcription_response.get("text", "")
|
||||
if not transcribed_text:
|
||||
raise ValueError("Transcription returned empty text")
|
||||
markdown_content = f"# Transcription of {filename}\n\n{transcribed_text}"
|
||||
|
||||
etl_service = "AUDIO_TRANSCRIPTION"
|
||||
# Clean up temp file
|
||||
with contextlib.suppress(Exception):
|
||||
os.unlink(file_path)
|
||||
|
||||
else:
|
||||
# Document files - use ETL service
|
||||
from app.services.page_limit_service import PageLimitExceededError, PageLimitService
|
||||
|
||||
page_limit_service = PageLimitService(session)
|
||||
|
||||
# Estimate page count
|
||||
try:
|
||||
estimated_pages = page_limit_service.estimate_pages_before_processing(file_path)
|
||||
except Exception:
|
||||
file_size = os.path.getsize(file_path)
|
||||
estimated_pages = max(1, file_size // (80 * 1024))
|
||||
|
||||
# Check page limit
|
||||
await page_limit_service.check_page_limit(user_id, estimated_pages)
|
||||
|
||||
if app_config.ETL_SERVICE == "UNSTRUCTURED":
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="parsing", stage_message="Extracting content"
|
||||
)
|
||||
|
||||
from langchain_unstructured import UnstructuredLoader
|
||||
|
||||
loader = UnstructuredLoader(
|
||||
file_path, mode="elements", post_processors=[], languages=["eng"],
|
||||
include_orig_elements=False, include_metadata=False, strategy="auto"
|
||||
)
|
||||
docs = await loader.aload()
|
||||
markdown_content = await convert_document_to_markdown(docs)
|
||||
actual_pages = page_limit_service.estimate_pages_from_elements(docs)
|
||||
final_page_count = max(estimated_pages, actual_pages)
|
||||
etl_service = "UNSTRUCTURED"
|
||||
|
||||
# Update page usage
|
||||
await page_limit_service.update_page_usage(user_id, final_page_count, allow_exceed=True)
|
||||
|
||||
elif app_config.ETL_SERVICE == "LLAMACLOUD":
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="parsing", stage_message="Extracting content"
|
||||
)
|
||||
|
||||
result = await parse_with_llamacloud_retry(
|
||||
file_path=file_path, estimated_pages=estimated_pages,
|
||||
task_logger=task_logger, log_entry=log_entry
|
||||
)
|
||||
markdown_documents = await result.aget_markdown_documents(split_by_page=False)
|
||||
if not markdown_documents:
|
||||
raise RuntimeError(f"LlamaCloud parsing returned no documents: {filename}")
|
||||
markdown_content = markdown_documents[0].text
|
||||
etl_service = "LLAMACLOUD"
|
||||
|
||||
# Update page usage
|
||||
await page_limit_service.update_page_usage(user_id, estimated_pages, allow_exceed=True)
|
||||
|
||||
elif app_config.ETL_SERVICE == "DOCLING":
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="parsing", stage_message="Extracting content"
|
||||
)
|
||||
|
||||
# Suppress logging during Docling import
|
||||
getLogger("docling.pipeline.base_pipeline").setLevel(ERROR)
|
||||
getLogger("docling.document_converter").setLevel(ERROR)
|
||||
getLogger("docling_core.transforms.chunker.hierarchical_chunker").setLevel(ERROR)
|
||||
|
||||
from docling.document_converter import DocumentConverter
|
||||
|
||||
converter = DocumentConverter()
|
||||
result = converter.convert(file_path)
|
||||
markdown_content = result.document.export_to_markdown()
|
||||
etl_service = "DOCLING"
|
||||
|
||||
# Update page usage
|
||||
await page_limit_service.update_page_usage(user_id, estimated_pages, allow_exceed=True)
|
||||
|
||||
else:
|
||||
raise RuntimeError(f"Unknown ETL_SERVICE: {app_config.ETL_SERVICE}")
|
||||
|
||||
# Clean up temp file
|
||||
with contextlib.suppress(Exception):
|
||||
os.unlink(file_path)
|
||||
|
||||
if not markdown_content:
|
||||
raise RuntimeError(f"Failed to extract content from file: {filename}")
|
||||
|
||||
# ===== STEP 2: Check for duplicate content =====
|
||||
content_hash = generate_content_hash(markdown_content, search_space_id)
|
||||
|
||||
existing_by_content = await check_duplicate_document(session, content_hash)
|
||||
if existing_by_content and existing_by_content.id != document.id:
|
||||
# Duplicate content found - mark this document as failed
|
||||
logging.info(
|
||||
f"Duplicate content detected for {filename}, "
|
||||
f"matches document {existing_by_content.id}"
|
||||
)
|
||||
return None
|
||||
|
||||
# ===== STEP 3: Generate embeddings and chunks =====
|
||||
if notification:
|
||||
await NotificationService.document_processing.notify_processing_progress(
|
||||
session, notification, stage="chunking"
|
||||
)
|
||||
|
||||
user_llm = await get_user_long_context_llm(session, user_id, search_space_id)
|
||||
|
||||
if user_llm:
|
||||
document_metadata = {
|
||||
"file_name": filename,
|
||||
"etl_service": etl_service,
|
||||
"document_type": "File Document",
|
||||
}
|
||||
summary_content, summary_embedding = await generate_document_summary(
|
||||
markdown_content, user_llm, document_metadata
|
||||
)
|
||||
else:
|
||||
# Fallback: use truncated content as summary
|
||||
summary_content = markdown_content[:4000]
|
||||
from app.config import config
|
||||
|
||||
summary_embedding = config.embedding_model_instance.embed(summary_content)
|
||||
|
||||
chunks = await create_document_chunks(markdown_content)
|
||||
|
||||
# Convert to BlockNote for editing
|
||||
blocknote_json = await convert_markdown_to_blocknote(markdown_content)
|
||||
|
||||
# ===== STEP 4: Update document to READY =====
|
||||
from sqlalchemy.orm.attributes import flag_modified
|
||||
|
||||
document.title = filename
|
||||
document.content = summary_content
|
||||
document.content_hash = content_hash
|
||||
document.embedding = summary_embedding
|
||||
document.document_metadata = {
|
||||
"FILE_NAME": filename,
|
||||
"ETL_SERVICE": etl_service or "UNKNOWN",
|
||||
**(document.document_metadata or {}),
|
||||
}
|
||||
flag_modified(document, "document_metadata")
|
||||
|
||||
# Use safe_set_chunks to avoid async issues
|
||||
safe_set_chunks(document, chunks)
|
||||
|
||||
document.blocknote_document = blocknote_json
|
||||
document.content_needs_reindexing = False
|
||||
document.updated_at = get_current_timestamp()
|
||||
document.status = DocumentStatus.ready() # Shows checkmark in UI
|
||||
|
||||
await session.commit()
|
||||
await session.refresh(document)
|
||||
|
||||
await task_logger.log_task_success(
|
||||
log_entry,
|
||||
f"Successfully processed file: {filename}",
|
||||
{
|
||||
"document_id": document.id,
|
||||
"content_hash": content_hash,
|
||||
"file_type": etl_service,
|
||||
"chunks_count": len(chunks),
|
||||
},
|
||||
)
|
||||
|
||||
return document
|
||||
|
||||
except Exception as e:
|
||||
await session.rollback()
|
||||
|
||||
from app.services.page_limit_service import PageLimitExceededError
|
||||
|
||||
if isinstance(e, PageLimitExceededError):
|
||||
error_message = str(e)
|
||||
elif isinstance(e, HTTPException) and "page limit" in str(e.detail).lower():
|
||||
error_message = str(e.detail)
|
||||
else:
|
||||
error_message = f"Failed to process file: {filename}"
|
||||
|
||||
await task_logger.log_task_failure(
|
||||
log_entry,
|
||||
error_message,
|
||||
str(e),
|
||||
{"error_type": type(e).__name__, "filename": filename, "document_id": document.id},
|
||||
)
|
||||
logging.error(f"Error processing file with document: {error_message}")
|
||||
raise
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue