07088e60e9
Phase 1 Backend Implementation: - ✅ MongoDB data models (Keyword, Pipeline, User, Application) - ✅ Pydantic schemas for all models with validation - ✅ KeywordService: Full CRUD, filtering, pagination, stats, toggle status - ✅ PipelineService: Full CRUD, start/stop/restart, logs, config management - ✅ Keywords API: 8 endpoints with complete functionality - ✅ Pipelines API: 11 endpoints with complete functionality - ✅ Updated TODO.md to reflect completion Key Features: - Async MongoDB operations with Motor - Comprehensive filtering and pagination support - Pipeline logging system - Statistics tracking for keywords and pipelines - Proper error handling with HTTP status codes - Type-safe request/response models Files Added: - models/: 4 data models with PyObjectId support - schemas/: 4 schema modules with Create/Update/Response patterns - services/: KeywordService (234 lines) + PipelineService (332 lines) Files Modified: - api/keywords.py: 40 → 212 lines (complete implementation) - api/pipelines.py: 25 → 300 lines (complete implementation) - TODO.md: Updated checklist with completed items Next Steps: - UserService with authentication - ApplicationService for OAuth2 - MonitoringService - Redis integration - Frontend implementation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
8 lines
192 B
Python
8 lines
192 B
Python
# Data Models
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from .keyword import Keyword
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from .pipeline import Pipeline
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from .user import User
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from .application import Application
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__all__ = ["Keyword", "Pipeline", "User", "Application"]
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