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/core/logging.py

@@ -0,0 +1,22 @@
+"""日志配置"""
+import logging
+import sys
+from .config import settings
+
+
+def setup_logging():
+    """配置日志"""
+    level = logging.DEBUG if settings.app.debug else logging.INFO
+    
+    logging.basicConfig(
+        level=level,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+        handlers=[
+            logging.StreamHandler(sys.stdout)
+        ]
+    )
+    
+    # 降低第三方库日志级别
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+    logging.getLogger("kafka").setLevel(logging.WARNING)
+    logging.getLogger("uvicorn").setLevel(logging.INFO)