李顺东
ae681575b9
- 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
24 lines
434 B
Python
24 lines
434 B
Python
"""API响应模型"""
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from pydantic import BaseModel
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from typing import Optional, Any
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class ApiResponse(BaseModel):
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"""通用API响应"""
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code: int = 0
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message: str = "success"
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data: Optional[Any] = None
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class ConsumeResponse(BaseModel):
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"""消费响应"""
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code: int = 0
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data: list = []
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count: int = 0
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class StatusResponse(BaseModel):
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"""状态响应"""
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code: int = 0
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data: dict = {}
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