refactor(docker): improve server architecture and configuration
Complete overhaul of Docker deployment setup with improved architecture: - Add Redis integration for task management - Implement rate limiting and security middleware - Add Prometheus metrics and health checks - Improve error handling and logging - Add support for streaming responses - Implement proper configuration management - Add platform-specific optimizations for ARM64/AMD64 BREAKING CHANGE: Docker deployment now requires Redis and new config.yml structure
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UncleCode
305
deploy/docker/api.py
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305
deploy/docker/api.py
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import os
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import json
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import logging
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from typing import Optional, AsyncGenerator
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from urllib.parse import unquote
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from fastapi import HTTPException, Request, status
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from fastapi.background import BackgroundTasks
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from fastapi.responses import JSONResponse
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from redis import asyncio as aioredis
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from crawl4ai import (
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AsyncWebCrawler,
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CrawlerRunConfig,
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LLMExtractionStrategy,
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CacheMode
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)
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from crawl4ai.content_filter_strategy import (
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PruningContentFilter,
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BM25ContentFilter,
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LLMContentFilter
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)
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from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
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from crawl4ai.content_scraping_strategy import LXMLWebScrapingStrategy
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from utils import (
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TaskStatus,
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FilterType,
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get_base_url,
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is_task_id,
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should_cleanup_task,
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decode_redis_hash
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)
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logger = logging.getLogger(__name__)
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async def process_llm_extraction(
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redis: aioredis.Redis,
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config: dict,
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task_id: str,
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url: str,
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instruction: str,
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schema: Optional[str] = None,
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cache: str = "0"
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) -> None:
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"""Process LLM extraction in background."""
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try:
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llm_strategy = LLMExtractionStrategy(
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provider=config["llm"]["provider"],
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api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
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instruction=instruction,
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schema=json.loads(schema) if schema else None,
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)
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cache_mode = CacheMode.ENABLED if cache == "1" else CacheMode.BYPASS
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(
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url=url,
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config=CrawlerRunConfig(
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extraction_strategy=llm_strategy,
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scraping_strategy=LXMLWebScrapingStrategy(),
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cache_mode=cache_mode
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)
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)
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if not result.success:
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await redis.hset(f"task:{task_id}", mapping={
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"status": TaskStatus.FAILED,
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"error": result.error_message
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})
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return
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content = json.loads(result.extracted_content)
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await redis.hset(f"task:{task_id}", mapping={
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"status": TaskStatus.COMPLETED,
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"result": json.dumps(content)
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})
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except Exception as e:
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logger.error(f"LLM extraction error: {str(e)}", exc_info=True)
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await redis.hset(f"task:{task_id}", mapping={
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"status": TaskStatus.FAILED,
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"error": str(e)
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})
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async def handle_markdown_request(
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url: str,
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filter_type: FilterType,
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query: Optional[str] = None,
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cache: str = "0",
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config: Optional[dict] = None
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) -> str:
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"""Handle markdown generation requests."""
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try:
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decoded_url = unquote(url)
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if not decoded_url.startswith(('http://', 'https://')):
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decoded_url = 'https://' + decoded_url
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if filter_type == FilterType.RAW:
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md_generator = DefaultMarkdownGenerator()
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else:
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content_filter = {
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FilterType.FIT: PruningContentFilter(),
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FilterType.BM25: BM25ContentFilter(user_query=query or ""),
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FilterType.LLM: LLMContentFilter(
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provider=config["llm"]["provider"],
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api_token=os.environ.get(config["llm"].get("api_key_env", None), ""),
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instruction=query or "Extract main content"
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)
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}[filter_type]
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md_generator = DefaultMarkdownGenerator(content_filter=content_filter)
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cache_mode = CacheMode.ENABLED if cache == "1" else CacheMode.BYPASS
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(
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url=decoded_url,
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config=CrawlerRunConfig(
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markdown_generator=md_generator,
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scraping_strategy=LXMLWebScrapingStrategy(),
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cache_mode=cache_mode
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)
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)
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if not result.success:
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=result.error_message
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)
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return (result.markdown_v2.raw_markdown
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if filter_type == FilterType.RAW
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else result.markdown_v2.fit_markdown)
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except Exception as e:
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logger.error(f"Markdown error: {str(e)}", exc_info=True)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=str(e)
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)
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async def handle_llm_request(
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redis: aioredis.Redis,
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background_tasks: BackgroundTasks,
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request: Request,
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input_path: str,
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query: Optional[str] = None,
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schema: Optional[str] = None,
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cache: str = "0",
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config: Optional[dict] = None
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) -> JSONResponse:
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"""Handle LLM extraction requests."""
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base_url = get_base_url(request)
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try:
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if is_task_id(input_path):
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return await handle_task_status(
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redis, input_path, base_url
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)
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if not query:
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return JSONResponse({
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"message": "Please provide an instruction",
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"_links": {
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"example": {
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"href": f"{base_url}/llm/{input_path}?q=Extract+main+content",
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"title": "Try this example"
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}
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}
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})
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return await create_new_task(
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redis,
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background_tasks,
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input_path,
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query,
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schema,
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cache,
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base_url,
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config
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)
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except Exception as e:
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logger.error(f"LLM endpoint error: {str(e)}", exc_info=True)
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return JSONResponse({
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"error": str(e),
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"_links": {
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"retry": {"href": str(request.url)}
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}
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}, status_code=status.HTTP_500_INTERNAL_SERVER_ERROR)
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async def handle_task_status(
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redis: aioredis.Redis,
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task_id: str,
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base_url: str
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) -> JSONResponse:
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"""Handle task status check requests."""
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task = await redis.hgetall(f"task:{task_id}")
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if not task:
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raise HTTPException(
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status_code=status.HTTP_404_NOT_FOUND,
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detail="Task not found"
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)
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task = decode_redis_hash(task)
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response = create_task_response(task, task_id, base_url)
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if task["status"] in [TaskStatus.COMPLETED, TaskStatus.FAILED]:
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if should_cleanup_task(task["created_at"]):
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await redis.delete(f"task:{task_id}")
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return JSONResponse(response)
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async def create_new_task(
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redis: aioredis.Redis,
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background_tasks: BackgroundTasks,
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input_path: str,
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query: str,
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schema: Optional[str],
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cache: str,
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base_url: str,
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config: dict
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) -> JSONResponse:
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"""Create and initialize a new task."""
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decoded_url = unquote(input_path)
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if not decoded_url.startswith(('http://', 'https://')):
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decoded_url = 'https://' + decoded_url
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from datetime import datetime
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task_id = f"llm_{int(datetime.now().timestamp())}_{id(background_tasks)}"
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await redis.hset(f"task:{task_id}", mapping={
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"status": TaskStatus.PROCESSING,
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"created_at": datetime.now().isoformat(),
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"url": decoded_url
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})
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background_tasks.add_task(
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process_llm_extraction,
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redis,
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config,
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task_id,
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decoded_url,
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query,
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schema,
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cache
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)
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return JSONResponse({
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"task_id": task_id,
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"status": TaskStatus.PROCESSING,
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"url": decoded_url,
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"_links": {
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"self": {"href": f"{base_url}/llm/{task_id}"},
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"status": {"href": f"{base_url}/llm/{task_id}"}
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}
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})
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def create_task_response(task: dict, task_id: str, base_url: str) -> dict:
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"""Create response for task status check."""
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response = {
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"task_id": task_id,
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"status": task["status"],
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"created_at": task["created_at"],
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"url": task["url"],
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"_links": {
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"self": {"href": f"{base_url}/llm/{task_id}"},
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"refresh": {"href": f"{base_url}/llm/{task_id}"}
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}
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}
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if task["status"] == TaskStatus.COMPLETED:
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response["result"] = json.loads(task["result"])
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elif task["status"] == TaskStatus.FAILED:
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response["error"] = task["error"]
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return response
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async def stream_results(crawler: AsyncWebCrawler, results_gen: AsyncGenerator) -> AsyncGenerator[bytes, None]:
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"""Stream results with heartbeats and completion markers."""
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import asyncio
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import json
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from utils import datetime_handler
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try:
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async for result in results_gen:
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try:
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result_dict = result.model_dump()
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logger.info(f"Streaming result for {result_dict.get('url', 'unknown')}")
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data = json.dumps(result_dict, default=datetime_handler) + "\n"
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yield data.encode('utf-8')
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except Exception as e:
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logger.error(f"Serialization error: {e}")
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error_response = {"error": str(e), "url": getattr(result, 'url', 'unknown')}
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yield (json.dumps(error_response) + "\n").encode('utf-8')
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yield json.dumps({"status": "completed"}).encode('utf-8')
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except asyncio.CancelledError:
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logger.warning("Client disconnected during streaming")
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finally:
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try:
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await crawler.close()
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except Exception as e:
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logger.error(f"Crawler cleanup error: {e}")
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