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6347f42e20
- Remove nested git repository from bugulma/frontend/.git - Add all frontend files to main repository tracking - Convert from separate frontend/backend repos to unified monorepo - Preserve all frontend code and development history as tracked files - Eliminate nested repository complexity for simpler development workflow This creates a proper monorepo structure with frontend and backend coexisting in the same repository for easier development and deployment.
109 lines
3.9 KiB
Markdown
109 lines
3.9 KiB
Markdown
# Frontend Simplification - Backend AI Integration
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## Overview
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The frontend has been simplified to be "dumb" - it no longer directly calls LLM providers. All AI/LLM operations are now handled by backend API endpoints.
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## What Changed
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### Removed
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- Direct LLM provider calls from frontend
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- LLM abstraction layer initialization
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- Frontend API key management
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- Provider-specific logic in frontend
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### Added
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- Backend API client for AI endpoints (`services/ai-api.ts`)
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- Simplified service layer that just calls backend
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- Clean separation of concerns
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## Architecture
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```
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┌─────────────────────────────────────────┐
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│ Frontend (React) │
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│ - Components │
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│ - Hooks (useGemini.ts) │
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│ - Services (aiService.ts) │
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└──────────────┬──────────────────────────┘
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│ HTTP Requests
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▼
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┌─────────────────────────────────────────┐
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│ Backend API Endpoints │
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│ /api/ai/extract/text │
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│ /api/ai/extract/file │
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│ /api/ai/analyze/symbiosis │
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│ /api/ai/web-intelligence │
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│ /api/ai/search-suggestions │
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│ /api/ai/generate/description │
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│ /api/ai/generate/historical-context │
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│ /api/ai/chat │
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└──────────────┬──────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────┐
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│ Backend LLM Service │
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│ - Provider abstraction │
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│ - API key management │
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│ - Rate limiting │
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│ - Caching │
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└──────────────────────────────────────────┘
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```
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## Benefits
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1. **Security**: API keys stay on backend
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2. **Simplicity**: Frontend doesn't need to know about providers
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3. **Centralization**: All AI logic in one place
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4. **Cost Control**: Backend can manage rate limiting, caching
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5. **Easier Testing**: Mock backend endpoints instead of LLM providers
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6. **Better Error Handling**: Centralized error handling on backend
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## Files Changed
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### New Files
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- `services/ai-api.ts` - Backend API client for AI endpoints
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- `BACKEND_AI_ENDPOINTS.md` - Specification for backend endpoints
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### Modified Files
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- `services/aiService.ts` - Now just calls backend API
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- `index.tsx` - Removed LLM initialization
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- `lib/api-client.ts` - Added FormData support for file uploads
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### Kept (for reference/future use)
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- `lib/llm/` - LLM abstraction layer (can be used by backend)
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## Migration Path
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1. ✅ Frontend updated to call backend endpoints
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2. ⏳ Backend needs to implement AI endpoints (see `BACKEND_AI_ENDPOINTS.md`)
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3. ⏳ Backend can use the LLM abstraction from `lib/llm/` (if ported to Go) or implement its own
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## Example Usage
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### Before (Direct LLM call)
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```typescript
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import { llmService } from './lib/llm/llmService';
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const response = await llmService.generateContent({ ... });
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```
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### After (Backend API call)
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```typescript
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import * as aiApi from './services/ai-api';
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const data = await aiApi.extractDataFromText({ text: '...' });
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```
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## Next Steps
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1. Implement backend AI endpoints (see `BACKEND_AI_ENDPOINTS.md`)
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2. Add rate limiting and caching on backend
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3. Add monitoring and cost tracking
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4. Consider streaming responses for chat (WebSocket or SSE)
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