mirror of
https://github.com/MODSetter/SurfSense.git
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Merge remote-tracking branch 'upstream/main' into feature/blocknote-editor
This commit is contained in:
Anish Sarkar
commit
b98c312fb1
81 changed files with 8976 additions and 2387 deletions
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@ -6,7 +6,6 @@ and sources. Each processor is responsible for handling a specific type of docum
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processing task in the background.
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Available processors:
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- URL crawler: Process web pages from URLs
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- Extension processor: Handle documents from browser extension
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- Markdown processor: Process markdown files
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- File processors: Handle files using different ETL services (Unstructured, LlamaCloud, Docling)
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@ -26,14 +25,11 @@ from .file_processors import (
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# Markdown processor
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from .markdown_processor import add_received_markdown_file_document
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from .url_crawler import add_crawled_url_document
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# YouTube processor
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from .youtube_processor import add_youtube_video_document
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__all__ = [
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# URL processing
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"add_crawled_url_document",
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# Extension processing
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"add_extension_received_document",
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"add_received_file_document_using_docling",
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@ -1,342 +0,0 @@
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"""
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URL crawler document processor.
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"""
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import logging
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import validators
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from firecrawl import AsyncFirecrawlApp
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from langchain_community.document_loaders import AsyncChromiumLoader
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from langchain_core.documents import Document as LangchainDocument
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from sqlalchemy.exc import SQLAlchemyError
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.config import config
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from app.db import Document, DocumentType
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from app.services.llm_service import get_user_long_context_llm
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from app.services.task_logging_service import TaskLoggingService
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from app.utils.document_converters import (
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create_document_chunks,
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generate_content_hash,
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generate_document_summary,
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generate_unique_identifier_hash,
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)
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from .base import (
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check_document_by_unique_identifier,
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md,
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)
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async def add_crawled_url_document(
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session: AsyncSession, url: str, search_space_id: int, user_id: str
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) -> Document | None:
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"""
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Process and store a document from a crawled URL.
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Args:
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session: Database session
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url: URL to crawl
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search_space_id: ID of the search space
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user_id: ID of the user
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Returns:
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Document object if successful, None if failed
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"""
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task_logger = TaskLoggingService(session, search_space_id)
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# Log task start
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log_entry = await task_logger.log_task_start(
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task_name="crawl_url_document",
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source="background_task",
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message=f"Starting URL crawling process for: {url}",
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metadata={"url": url, "user_id": str(user_id)},
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)
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try:
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# URL validation step
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await task_logger.log_task_progress(
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log_entry, f"Validating URL: {url}", {"stage": "validation"}
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)
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if not validators.url(url):
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raise ValueError(f"Url {url} is not a valid URL address")
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# Set up crawler
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await task_logger.log_task_progress(
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log_entry,
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f"Setting up crawler for URL: {url}",
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{
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"stage": "crawler_setup",
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"firecrawl_available": bool(config.FIRECRAWL_API_KEY),
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},
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)
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use_firecrawl = bool(config.FIRECRAWL_API_KEY)
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if use_firecrawl:
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# Use Firecrawl SDK directly
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firecrawl_app = AsyncFirecrawlApp(api_key=config.FIRECRAWL_API_KEY)
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else:
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crawl_loader = AsyncChromiumLoader(urls=[url], headless=True)
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# Perform crawling
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await task_logger.log_task_progress(
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log_entry,
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f"Crawling URL content: {url}",
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{
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"stage": "crawling",
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"crawler_type": "AsyncFirecrawlApp"
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if use_firecrawl
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else "AsyncChromiumLoader",
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},
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)
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if use_firecrawl:
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# Use async Firecrawl SDK with v1 API - properly awaited
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scrape_result = await firecrawl_app.scrape_url(
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url=url, formats=["markdown"]
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)
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# scrape_result is a Pydantic ScrapeResponse object
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# Access attributes directly
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if scrape_result and scrape_result.success:
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# Extract markdown content
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markdown_content = scrape_result.markdown or ""
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# Extract metadata - this is a DICT
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metadata = scrape_result.metadata if scrape_result.metadata else {}
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# Convert to LangChain Document format
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url_crawled = [
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LangchainDocument(
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page_content=markdown_content,
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metadata={
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"source": url,
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"title": metadata.get("title", url),
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"description": metadata.get("description", ""),
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"language": metadata.get("language", ""),
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"sourceURL": metadata.get("sourceURL", url),
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**metadata, # Include all other metadata fields
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},
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)
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]
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content_in_markdown = url_crawled[0].page_content
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else:
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error_msg = (
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scrape_result.error
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if scrape_result and hasattr(scrape_result, "error")
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else "Unknown error"
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)
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raise ValueError(f"Firecrawl failed to scrape URL: {error_msg}")
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else:
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# Use AsyncChromiumLoader as fallback
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url_crawled = await crawl_loader.aload()
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content_in_markdown = md.transform_documents(url_crawled)[0].page_content
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# Format document
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await task_logger.log_task_progress(
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log_entry,
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f"Processing crawled content from: {url}",
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{"stage": "content_processing", "content_length": len(content_in_markdown)},
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)
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# Format document metadata in a more maintainable way
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metadata_sections = [
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(
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"METADATA",
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[
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f"{key.upper()}: {value}"
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for key, value in url_crawled[0].metadata.items()
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],
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),
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(
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"CONTENT",
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["FORMAT: markdown", "TEXT_START", content_in_markdown, "TEXT_END"],
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),
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]
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# Build the document string more efficiently
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document_parts = []
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document_parts.append("<DOCUMENT>")
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for section_title, section_content in metadata_sections:
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document_parts.append(f"<{section_title}>")
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document_parts.extend(section_content)
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document_parts.append(f"</{section_title}>")
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document_parts.append("</DOCUMENT>")
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combined_document_string = "\n".join(document_parts)
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# Generate unique identifier hash for this URL
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unique_identifier_hash = generate_unique_identifier_hash(
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DocumentType.CRAWLED_URL, url, search_space_id
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)
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# Generate content hash
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content_hash = generate_content_hash(combined_document_string, search_space_id)
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# Check if document with this unique identifier already exists
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await task_logger.log_task_progress(
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log_entry,
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f"Checking for existing URL: {url}",
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{"stage": "duplicate_check", "url": url},
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)
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existing_document = await check_document_by_unique_identifier(
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session, unique_identifier_hash
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)
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if existing_document:
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# Document exists - check if content has changed
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if existing_document.content_hash == content_hash:
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await task_logger.log_task_success(
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log_entry,
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f"URL document unchanged: {url}",
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{
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"duplicate_detected": True,
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"existing_document_id": existing_document.id,
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},
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)
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logging.info(f"Document for URL {url} unchanged. Skipping.")
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return existing_document
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else:
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# Content has changed - update the existing document
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logging.info(f"Content changed for URL {url}. Updating document.")
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await task_logger.log_task_progress(
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log_entry,
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f"Updating URL document: {url}",
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{"stage": "document_update", "url": url},
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)
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# Get LLM for summary generation (needed for both create and update)
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await task_logger.log_task_progress(
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log_entry,
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f"Preparing for summary generation: {url}",
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{"stage": "llm_setup"},
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)
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# Get user's long context LLM
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user_llm = await get_user_long_context_llm(session, user_id, search_space_id)
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if not user_llm:
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raise RuntimeError(
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f"No long context LLM configured for user {user_id} in search space {search_space_id}"
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)
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# Generate summary
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await task_logger.log_task_progress(
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log_entry,
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f"Generating summary for URL content: {url}",
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{"stage": "summary_generation"},
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)
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# Generate summary with metadata
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document_metadata = {
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"url": url,
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"title": url_crawled[0].metadata.get("title", url),
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"document_type": "Crawled URL Document",
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"crawler_type": "FirecrawlApp" if use_firecrawl else "AsyncChromiumLoader",
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}
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summary_content, summary_embedding = await generate_document_summary(
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combined_document_string, user_llm, document_metadata
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)
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# Process chunks
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await task_logger.log_task_progress(
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log_entry,
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f"Processing content chunks for URL: {url}",
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{"stage": "chunk_processing"},
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)
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from app.utils.blocknote_converter import convert_markdown_to_blocknote
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# Convert markdown to BlockNote JSON
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blocknote_json = await convert_markdown_to_blocknote(combined_document_string)
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if not blocknote_json:
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logging.warning(
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f"Failed to convert crawled URL '{url}' to BlockNote JSON, "
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"document will not be editable"
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)
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chunks = await create_document_chunks(content_in_markdown)
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# Update or create document
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if existing_document:
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# Update existing document
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await task_logger.log_task_progress(
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log_entry,
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f"Updating document in database for URL: {url}",
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{"stage": "document_update", "chunks_count": len(chunks)},
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)
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existing_document.title = url_crawled[0].metadata.get(
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"title", url_crawled[0].metadata.get("source", url)
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)
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existing_document.content = summary_content
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existing_document.content_hash = content_hash
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existing_document.embedding = summary_embedding
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existing_document.document_metadata = url_crawled[0].metadata
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existing_document.chunks = chunks
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existing_document.blocknote_document = blocknote_json
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document = existing_document
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else:
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# Create new document
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await task_logger.log_task_progress(
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log_entry,
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f"Creating document in database for URL: {url}",
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{"stage": "document_creation", "chunks_count": len(chunks)},
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)
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document = Document(
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search_space_id=search_space_id,
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title=url_crawled[0].metadata.get(
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"title", url_crawled[0].metadata.get("source", url)
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),
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document_type=DocumentType.CRAWLED_URL,
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document_metadata=url_crawled[0].metadata,
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content=summary_content,
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embedding=summary_embedding,
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chunks=chunks,
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content_hash=content_hash,
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unique_identifier_hash=unique_identifier_hash,
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blocknote_document=blocknote_json,
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)
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session.add(document)
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await session.commit()
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await session.refresh(document)
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# Log success
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await task_logger.log_task_success(
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log_entry,
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f"Successfully crawled and processed URL: {url}",
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{
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"document_id": document.id,
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"title": document.title,
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"content_hash": content_hash,
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"chunks_count": len(chunks),
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"summary_length": len(summary_content),
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},
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)
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return document
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except SQLAlchemyError as db_error:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Database error while processing URL: {url}",
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str(db_error),
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{"error_type": "SQLAlchemyError"},
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)
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raise db_error
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except Exception as e:
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await session.rollback()
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await task_logger.log_task_failure(
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log_entry,
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f"Failed to crawl URL: {url}",
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str(e),
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{"error_type": type(e).__name__},
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)
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raise RuntimeError(f"Failed to crawl URL: {e!s}") from e
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