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refactor: refactored background_tasks & indexing_tasks
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DESKTOP-RTLN3BA\$punk
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commit
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24 changed files with 4704 additions and 5149 deletions
242
surfsense_backend/app/tasks/document_processors/url_crawler.py
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242
surfsense_backend/app/tasks/document_processors/url_crawler.py
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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 langchain_community.document_loaders import AsyncChromiumLoader, FireCrawlLoader
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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 generate_content_hash
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from .base import (
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check_duplicate_document,
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create_document_chunks,
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generate_document_summary,
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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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if config.FIRECRAWL_API_KEY:
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crawl_loader = FireCrawlLoader(
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url=url,
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api_key=config.FIRECRAWL_API_KEY,
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mode="scrape",
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params={
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"formats": ["markdown"],
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"excludeTags": ["a"],
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},
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)
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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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{"stage": "crawling", "crawler_type": type(crawl_loader).__name__},
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)
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url_crawled = await crawl_loader.aload()
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if isinstance(crawl_loader, FireCrawlLoader):
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content_in_markdown = url_crawled[0].page_content
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elif isinstance(crawl_loader, AsyncChromiumLoader):
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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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content_hash = generate_content_hash(combined_document_string, search_space_id)
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# Check for duplicates
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await task_logger.log_task_progress(
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log_entry,
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f"Checking for duplicate content: {url}",
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{"stage": "duplicate_check", "content_hash": content_hash},
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)
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existing_document = await check_duplicate_document(session, content_hash)
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if existing_document:
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await task_logger.log_task_success(
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log_entry,
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f"Document already exists for URL: {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(
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f"Document with content hash {content_hash} already exists. Skipping processing."
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)
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return existing_document
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# Get LLM for summary generation
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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)
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if not user_llm:
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raise RuntimeError(f"No long context LLM configured for user {user_id}")
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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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summary_content, summary_embedding = await generate_document_summary(
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combined_document_string, user_llm
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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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chunks = await create_document_chunks(content_in_markdown)
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# Create and store 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["title"]
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if isinstance(crawl_loader, FireCrawlLoader)
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else url_crawled[0].metadata["source"],
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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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)
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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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