MCP Brain Service

# Project Summary: Retriv Integration for MCP Brain Service ## What We Decided After thorough discussion, we made key architectural decisions: ### ❌ What We're NOT Doing - Creating separate "data preparation agent" service - Creating separate "retrieval agent" service - Adding business logic to brain service - Making brain service understand PayloadCMS ### ✅ What We ARE Doing - Enhancing brain service with Retriv for better queries - Keeping data preparation in Next.js app (where it belongs) - Maintaining brain service as pure infrastructure - Making existing functionality better, not adding complexity ## The Key Insight **Retriv is not a separate system - it's just a better query engine!** ``` Before: Query → Jina only → Results After: Query → Retriv (BM25 + Jina) → Better Results ``` ## Architecture ### Simple and Clean ``` Next.js App (Business Logic) ↓ Prepares data (enrichment, transformation) ↓ Brain Service (Infrastructure) ↓ Stores in: Neo4j + Jina + Retriv ↑ Queries with: Retriv hybrid search ↑ Returns better results ``` ### Why This Works 1. **Brain Service = Database**: Like PostgreSQL doesn't understand your business logic 2. **App = Application**: Understands PayloadCMS, prepares data properly 3. **Retriv = Better Index**: Like adding a better index to your database ## What Retriv Does ### Problem with Current Approach - Jina semantic search alone misses exact keyword matches - Query "Aladdin's vest in scene 3" might miss "vest" or "scene 3" ### Solution with Retriv - **BM25**: Catches exact keywords ("vest", "scene", "3") - **Embeddings**: Understands semantic meaning ("clothing", "appearance") - **Combined**: More accurate results! ## Implementation Plan ### Phase 1: Add Retriv to Brain Service 1. Add `retriv` package to requirements.txt 2. Create `src/services/retriv_service.py` 3. Enhance `src/services/knowledge_service.py` 4. Existing MCP tools automatically get better! ### Phase 2: Data Preparation in Next.js (Later) 1. Create data preparation layer in Next.js 2. Transform PayloadCMS data to brain format 3. Call brain service storage API 4. Use brain service query API for retrieval ## Documentation Created All documentation is in `mcp-brain-service/docs/`: 1. **README.md** - Documentation index and overview 2. **architecture-decision.md** - Why data prep belongs in app 3. **retriv-integration-plan.md** - How to add Retriv to brain service 4. **api-contracts.md** - Storage and query API specifications 5. **implementation-checklist.md** - Step-by-step implementation guide 6. **SUMMARY.md** - This file ## Next Steps ### For Brain Service (Now) 1. Read `docs/retriv-integration-plan.md` 2. Follow `docs/implementation-checklist.md` 3. Add Retriv integration 4. Test and deploy ### For Next.js App (Later) 1. Read `docs/architecture-decision.md` 2. Review `docs/api-contracts.md` 3. Create data preparation layer 4. Integrate with brain service ## Benefits ### Technical - ✅ Simpler architecture (no separate agents) - ✅ Better search results (hybrid > semantic alone) - ✅ Clear separation of concerns - ✅ Brain service stays reusable - ✅ Easier to test and maintain ### Practical - ✅ One service to deploy (brain service) - ✅ Existing code gets better automatically - ✅ No new APIs to learn - ✅ Backward compatible - ✅ Easy to understand ## Timeline Estimate - **Retriv Integration**: ~8-10 hours - Setup & service creation: 2-3 hours - Integration & testing: 4-5 hours - Documentation & deployment: 2-3 hours - **Next.js Data Prep**: ~10-12 hours (when ready) - Preparation layer: 4-5 hours - PayloadCMS integration: 3-4 hours - Testing & refinement: 3-4 hours ## Success Criteria ### Functional - [ ] Hybrid search returns relevant results - [ ] Search includes keyword + semantic matches - [ ] Project isolation works correctly - [ ] Existing MCP tools work better ### Performance - [ ] Search latency < 100ms (p95) - [ ] No degradation of existing features - [ ] Memory usage acceptable ### Quality - [ ] All tests passing - [ ] Documentation complete - [ ] Code reviewed - [ ] Deployed successfully ## Key Principles Remember these throughout implementation: 1. **Brain service = Infrastructure** (like a database) 2. **App = Business logic** (like application code) 3. **Retriv = Better queries** (not a separate system) 4. **Keep it simple** (enhance, don't complicate) ## Questions Answered ### Q: Do we need separate agents? **A**: No! Just enhance brain service with Retriv. ### Q: Where does data preparation happen? **A**: In the Next.js app (where business logic belongs). ### Q: What does brain service do? **A**: Pure storage and retrieval - no business logic. ### Q: Is Retriv a separate service? **A**: No! It's just a better query engine inside brain service. ### Q: Do we need new APIs? **A**: No! Existing MCP tools get better automatically. ## Conclusion We've simplified the original plan significantly: **Original Idea**: - Create data preparation agent - Create retrieval agent - Complex multi-service architecture **Final Decision**: - Enhance brain service with Retriv - Keep data prep in Next.js app - Simple, clean architecture This is the right approach because: - Follows industry best practices - Keeps concerns separated - Makes brain service reusable - Easier to understand and maintain - Gets us better results faster ## Ready to Start? 1. Open `docs/retriv-integration-plan.md` 2. Follow `docs/implementation-checklist.md` 3. Start with Phase 1: Add Retriv package 4. Work through each phase systematically All the documentation you need is in the `docs/` folder. Good luck! 🚀