# TraceRTM Week 1 Implementation Checklist

## 🎯 Goal: Get AI-Powered Semantic Search Working

**Timeline:** 5 working days (40 hours)
**Expected Result:** Semantic search + RAG analysis working

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## 📅 Monday: Setup & Configuration (8 hours)

### Morning (4 hours)
- [ ] Get VoyageAI API key (free tier available)
- [ ] Add environment variables to `.env`
- [ ] Review `backend/internal/embeddings/README.md`
- [ ] Review `backend/internal/search/README.md`

### Afternoon (4 hours)
- [ ] Update `backend/main.go` to initialize embeddings
- [ ] Update `backend/main.go` to initialize search engine
- [ ] Register search handler in routes
- [ ] Test embeddings health check: `GET /api/v1/search/health`

**Success Criteria:**
- ✅ Health check returns `"status": "healthy"`
- ✅ All extensions enabled (vector, pg_trgm, etc.)

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## 📅 Tuesday: Indexing (8 hours)

### Morning (4 hours)
- [ ] Verify database has pgvector extension
- [ ] Verify items table has embedding column
- [ ] Verify IVFFlat index exists
- [ ] Check indexer configuration

### Afternoon (4 hours)
- [ ] Call `POST /api/v1/search/reindex` to start indexing
- [ ] Monitor progress: `GET /api/v1/search/stats`
- [ ] Wait for indexing to complete
- [ ] Verify embeddings in database

**Success Criteria:**
- ✅ All items have embeddings
- ✅ Indexer stats show 0 errors
- ✅ Cost tracking shows reasonable usage

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## 📅 Wednesday: Semantic Search (8 hours)

### Morning (4 hours)
- [ ] Test full-text search: `GET /api/v1/search?q=requirement&type=fulltext`
- [ ] Test vector search: `GET /api/v1/search?q=requirement&type=vector`
- [ ] Test hybrid search: `GET /api/v1/search?q=requirement&type=hybrid`
- [ ] Compare results quality

### Afternoon (4 hours)
- [ ] Test fuzzy search: `GET /api/v1/search?q=requiremnt&type=fuzzy`
- [ ] Test with filters: `GET /api/v1/search?q=...&status=todo,in_progress`
- [ ] Test pagination: `GET /api/v1/search?q=...&limit=10&offset=0`
- [ ] Test autocomplete: `GET /api/v1/search/suggest?prefix=req`

**Success Criteria:**
- ✅ Semantic search returns relevant results
- ✅ Hybrid search better than full-text alone
- ✅ Reranking improves result quality
- ✅ Response time < 100ms

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## 📅 Thursday: RAG Implementation (8 hours)

### Morning (4 hours)
- [ ] Create `backend/internal/rag/rag.go`
- [ ] Implement `AnalyzeRequirement()` method
- [ ] Add prompt caching for context
- [ ] Test with sample requirement

### Afternoon (4 hours)
- [ ] Create `backend/internal/handlers/rag_handler.go`
- [ ] Add `POST /api/v1/rag/analyze` endpoint
- [ ] Add `POST /api/v1/rag/suggest-links` endpoint
- [ ] Test end-to-end

**Success Criteria:**
- ✅ RAG returns analysis for requirement
- ✅ Prompt caching reduces costs by 90%
- ✅ Suggested links are relevant
- ✅ Response time < 2 seconds

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## 📅 Friday: Function Calling & Testing (8 hours)

### Morning (4 hours)
- [ ] Create `backend/internal/agents/tools.go`
- [ ] Define requirement tools (search, link, analyze)
- [ ] Implement tool handlers
- [ ] Test tool execution

### Afternoon (4 hours)
- [ ] Write integration tests
- [ ] Test agent with function calling
- [ ] Monitor costs and performance
- [ ] Document API endpoints

**Success Criteria:**
- ✅ Agent can call tools
- ✅ Tools execute correctly
- ✅ All tests pass
- ✅ Documentation complete

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## 🔍 Daily Verification

Each day, verify:
- [ ] No errors in logs
- [ ] API health check passes
- [ ] Database queries are fast
- [ ] Costs are within budget
- [ ] All tests pass

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## 📊 Success Metrics

| Metric | Target | Status |
|--------|--------|--------|
| Semantic search accuracy | 90%+ | ⬜ |
| Query response time | <100ms | ⬜ |
| RAG analysis time | <2s | ⬜ |
| Indexing cost | <$1 | ⬜ |
| Monthly search cost | <$5 | ⬜ |
| Uptime | 99.9% | ⬜ |

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## 🚨 Troubleshooting

### Issue: Embeddings not generating
- Check API key is set
- Check database has vector extension
- Check indexer is running
- Check logs for errors

### Issue: Slow search
- Check IVFFlat index exists
- Check query is using vector search
- Check cache is working
- Profile with EXPLAIN ANALYZE

### Issue: High costs
- Use voyage-3.5 (cheaper)
- Disable reranking if not needed
- Implement caching
- Batch embeddings

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## 📝 Notes

- VoyageAI free tier: 200M tokens/month
- Reranking improves accuracy by 13%+
- Prompt caching saves 90% on repeated queries
- IVFFlat index: good for <1M items
- HNSW index: better for >1M items

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## ✅ Week 1 Complete When

- ✅ Semantic search working
- ✅ RAG analysis working
- ✅ Function calling working
- ✅ All tests passing
- ✅ Costs tracked
- ✅ Documentation complete

