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/progress.py

@@ -0,0 +1,24 @@
+"""采集进度模型"""
+from pydantic import BaseModel
+
+
+class CrawlProgress(BaseModel):
+    """采集进度"""
+    task_id: str
+    current_offset: int = 0
+    total: int = 0
+    last_update: str = ""
+    status: str = "idle"  # idle, running, completed, error
+
+
+class CrawlStatus(BaseModel):
+    """采集状态响应"""
+    task_id: str
+    total: int
+    current_offset: int
+    progress: str
+    kafka_lag: int = 0
+    status: str
+    last_update: str
+    filtered_count: int = 0
+    produced_count: int = 0