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/services/__init__.py

@@ -0,0 +1,12 @@
+"""服务模块"""
+from .api_client import api_client, BazhuayuClient
+from .kafka_service import kafka_service, KafkaService
+from .progress_store import progress_store, ProgressStore
+from .crawler import crawler_manager, CrawlerManager, TaskCrawler
+
+__all__ = [
+    "api_client", "BazhuayuClient",
+    "kafka_service", "KafkaService", 
+    "progress_store", "ProgressStore",
+    "crawler_manager", "CrawlerManager", "TaskCrawler"
+]