Teams Messenger MCP App

# Agent Aech: Modular IR Architecture and Reasoning Primer This document is a comprehensive, detailed record of a technical conversation about designing a modular, future-proof Information Retrieval (IR) backend for Agent Aech—a domain-integrated agent operating via MCP (Model Context Protocol) with access to M365 (Teams, OneDrive, Email, etc.) and extensible to any business workflow. The goal is to provide a reasoning LLM with a rich, structured context for proposing novel architectures and solutions. --- ## 1. Problem Statement & Vision - **Agent Aech** is an autonomous agent with its own mailbox, OneDrive, Teams, and more, operating as a first-class user in an M365 domain. - The goal is to enable Agent Aech to automate any business workflow via MCP-enabled apps, with IR as a core capability. - Data sources are varied (PDF, Word, Excel, email, chat, etc.), and the IR backend must be: - **Flexible** (easy to add new sources, chunking strategies, search modes) - **Powerful** (hybrid search, LLM reranking, RAG, etc.) - **Low-maintenance** (easy to operate, update, and extend) - **Composable** (MCP tools/resources for LLMs and agentic clients) --- ## 2. Modular MCP Server for FlockMTL & DuckDB: Architecture & Benefits ### 2.1. Motivation - **Reusability**: Plug-and-play IR backend for any MCP-enabled app - **Separation of Concerns**: Core IR logic decoupled from domain adapters (Teams, Email, etc.) - **Scalability**: Dedicated resources, centralized optimization, resource pooling - **LLM Integration**: Direct tool access, unified data interface, RAG pipeline standardization ### 2.2. Layered Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ FlockMTL-DuckDB MCP Server │ ├─────────────────┬──────────────────────┬────────────────────────┤ │ Core Layer │ Extension Layer │ Adapter Layer │ ├─────────────────┼──────────────────────┼────────────────────────┤ │ • DuckDB Engine │ • Schema Management │ • Application Adapters │ │ • FlockMTL │ • Search Extensions │ - Teams Adapter │ │ Extensions │ • Index Management │ - Email Adapter │ │ • Embedding │ • LLM Connectors │ - Document Adapter │ │ Generation │ • Resource Management│ • Schema Transformers │ └─────────────────┴──────────────────────┴────────────────────────┘ ▲ ▲ ▲ │ │ │ ▼ ▼ ▼ ┌────────────────┐ ┌────────────────┐ ┌────────────────────────┐ │ LLM Agents │ │ Teams MCP App │ │ Other MCP Applications │ │ (Direct Access)│ │ │ │ │ └────────────────┘ └────────────────┘ └────────────────────────┘ ``` - **Core Layer**: DuckDB, FlockMTL, vector/embedding, storage - **Extension Layer**: Search, LLM connectors, resource management - **Adapter Layer**: Teams, Email, Document adapters; schema transformers ### 2.3. MCP Interface - **Resources**: tables, models, prompts, indexes - **Tools**: execute_sql, create_table, search, hybrid_search, semantic_search, keyword_search, llm_rerank, llm_summarize, llm_generate ### 2.4. Handling Domain-Specific Logic - **Adapter Pattern**: Map domain objects to generic schema - **Schema Abstraction**: Use abstract tables with metadata for platform-specific fields - **Plugin Architecture**: Register adapters/plugins for each domain ### 2.5. Code Management - **Dedicated repo** with clear layering (core, extensions, adapters, mcp, tests, docs) - **Integration**: Python package, Docker, Git submodule - **Versioning**: Semantic versioning, Git tags - **Adapter management**: Monorepo or separate repos - **Dependency management**: Core vs. optional deps - **Extending**: Documented process for new adapters --- ## 3. DuckDB vs. PostgreSQL for IR ### 3.1. DuckDB - **Embedded, serverless, zero-config** - **Columnar, analytics-optimized** (OLAP, vectorized execution) - **Modern SQL, easy extension loading** - **Tight Python/data science integration** - **Single-file, portable, low-maintenance** ### 3.2. PostgreSQL - **Enterprise-grade, multi-user, networked** - **ACID, robust, scalable, secure** - **Rich extension ecosystem** (pgvector, pg_trgm, PostGIS) - **Advanced features** (materialized views, partitioning, triggers) - **Best for production, multi-user, transactional workloads** ### 3.3. Summary Table | Feature | DuckDB | PostgreSQL | |-----------------|-----------------------|----------------------| | Deployment | Embedded, serverless | Networked, multiuser | | Analytics | Excellent | Good | | OLTP | Not ideal | Excellent | | Extensions | Growing | Mature | | Maintenance | Low | Higher | | Python/DS | Excellent | Good | --- ## 4. Chunking Strategies for Document IR ### 4.1. Why Chunk? - LLMs and vector search have context limits; long docs must be split - Goal: maximize IR quality by chunking at optimal boundaries ### 4.2. Strategies - **Fixed-size** (tokens/words/characters, with/without overlap) - **Semantic/paragraph-based** (split at natural boundaries) - **Hybrid** (merge small paragraphs, split large ones) - **Sliding window** (overlapping chunks) - **Content-aware** (special handling for code, tables, etc.) ### 4.3. Workflow 1. Extract text (PDF, Word, etc.) 2. Chunk using best-fit strategy 3. Store each chunk as a row in DuckDB/Postgres (with metadata) 4. Embed and index (BM25, vector, hybrid) 5. Retrieve and rerank at query time ### 4.4. Optimization - Tune chunk size/overlap for best recall/precision - Store rich metadata for filtering/context expansion - Experiment and compare strategies in SQL --- ## 5. Automation & State of the Art - **Most chunking/embedding/indexing is still semi-manual** (Python scripts, pipelines) - **TimescaleDB**: [Auto chunk size recommendations](https://medium.com/timescale/boost-your-postgresql-performance-with-auto-chunk-size-recommendations-f407406174e0) (infra-level, not semantic) - **Frameworks**: LlamaIndex, LangChain, Haystack automate pipelines, often using Postgres as backend - **No end-to-end semantic chunking automation in Postgres yet** - **Emerging trend**: Managed platforms and cloud DBs are starting to automate more of the IR pipeline --- ## 6. Ideal IR Backend for Agent Aech - **Modular, pluggable IR backend** (Postgres or DuckDB + FlockMTL) - **Automated ingestion/chunking layer** (detects type, applies best strategy, embeds, stores, indexes) - **MCP-enabled API layer** (all IR/search as MCP tools/resources) - **Continuous improvement** (monitor, re-chunk, re-embed as models/strategies improve) - **Abstract IR layer** (don't tie business logic to a specific DB or chunker) - **Composable pipelines** (modular, swappable components) - **Open standards** (for embeddings, metadata, chunk storage) - **Monitor ecosystem** (swap in new models/chunkers as they emerge) --- ## 7. Review & Feedback ### 7.1. Strengths - Clear motivation, benefits, and architecture - Practical repo/code management plan - Concrete handling of domain-specific logic - Realistic chunking/IR workflow and best practices - Cites relevant external work ### 7.2. Minor Gaps / Suggestions - Note DuckDB's concurrency/networking limitations - Add security/auth model (MCP auth, role separation) - Mention observability (logging, tracing, metrics) - Outline test strategy (unit/integration/contract) - Suggest CI/CD pipeline (GitHub Actions, etc.) - Data migration/versioning (Alembic, SQL scripts) - LLM-provider abstraction (pluggable registry) ### 7.3. Optional Additions - Roadmap/phased implementation - Cost considerations (embedded vs. managed) - Reference benchmarks - Example MCP schema (YAML/JSON) --- ## 8. References & Further Reading - [Building RAG with Postgres (anyblockers.com)](https://anyblockers.com/posts/building-rag-with-postgres) - [Which RAG Chunking and Formatting Strategy Is Best for Your App With Pgvector (Timescale)](https://www.timescale.com/blog/which-rag-chunking-and-formatting-strategy-is-best/) - [Boost Your PostgreSQL Performance With Auto Chunk Size Recommendations (Timescale)](https://medium.com/timescale/boost-your-postgresql-performance-with-auto-chunk-size-recommendations-f407406174e0) --- ## 9. Open Questions for Reasoning LLM - How can chunking and embedding be made fully adaptive and content-aware, with minimal human tuning? - What is the best way to automate the ingestion and IR pipeline for new data sources (e.g., OneDrive, SharePoint, email) in a modular MCP architecture? - How can the IR backend be made "self-improving" (e.g., feedback loops, auto-rechunking, auto-reembedding)? - What is the optimal way to expose IR/search capabilities to LLMs and agentic clients via MCP? - How can we future-proof the architecture to quickly adopt new LLMs, chunkers, and search paradigms as they emerge? - What are the best practices for observability, security, and cost management in such a modular, multi-adapter IR backend? --- ## 10. Call to Action **Reasoning LLM:** - Use this document as a starting point to propose novel architectures, automation strategies, and IR innovations for Agent Aech and MCP-enabled business automation. - Suggest new approaches for chunking, indexing, retrieval, and LLM integration that maximize flexibility, performance, and maintainability. - Identify gaps, risks, and opportunities for improvement in the current design. --- *End of conversation summary. Ready for reasoning and innovation.*