feat(job_crawler): initialize job crawler service with kafka integration

- Add technical documentation (技术方案.md) with system architecture and design details
- Create FastAPI application structure with modular organization (api, core, models, services, utils)
- Implement job data crawler service with incremental collection from third-party API
- Add Kafka service integration with Docker Compose configuration for message queue
- Create data models for job listings, progress tracking, and API responses
- Implement REST API endpoints for data consumption (/consume, /status) and task management
- Add progress persistence layer using SQLite for tracking collection offsets
- Implement date filtering logic to extract data published within 7 days
- Create API client service for third-party data source integration
- Add configuration management with environment-based settings
- Include Docker support with Dockerfile and docker-compose.yml for containerized deployment
- Add logging configuration and utility functions for date parsing
- Include requirements.txt with all Python dependencies and README documentation

job_crawler/app/main.py

@@ -0,0 +1,35 @@
+"""FastAPI应用入口"""
+import logging
+from contextlib import asynccontextmanager
+from fastapi import FastAPI
+from app.core.config import settings
+from app.core.logging import setup_logging
+from app.api import router
+from app.services import kafka_service
+
+setup_logging()
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """应用生命周期管理"""
+    logger.info("服务启动中...")
+    yield
+    logger.info("服务关闭中...")
+    kafka_service.close()
+
+
+app = FastAPI(
+    title="招聘数据采集服务",
+    description="从八爪鱼API采集招聘数据，通过Kafka提供消费接口",
+    version=settings.app.version,
+    lifespan=lifespan
+)
+
+app.include_router(router)
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)