ocups-kafka/job_crawler/requirements.txt

fastapi==0.109.0
uvicorn==0.27.0
httpx==0.27.0
pika==1.3.2
apscheduler==3.10.4
pydantic==2.5.3
PyYAML==6.0.1
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 2026-01-15 17:09:43 +08:00			`fastapi==0.109.0`
			`uvicorn==0.27.0`
			`httpx==0.27.0`
rabbitmq 2026-01-15 21:20:57 +08:00			`pika==1.3.2`
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 2026-01-15 17:09:43 +08:00			`apscheduler==3.10.4`
			`pydantic==2.5.3`
			`PyYAML==6.0.1`