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/models/__init__.py

@@ -0,0 +1,13 @@
+"""数据模型"""
+from .job import JobData
+from .progress import CrawlProgress, CrawlStatus
+from .response import ApiResponse, ConsumeResponse, StatusResponse
+
+__all__ = [
+    "JobData",
+    "CrawlProgress", 
+    "CrawlStatus",
+    "ApiResponse",
+    "ConsumeResponse",
+    "StatusResponse"
+]