Chain Debugger MCP Server

# RAG/Memory Integration Plan ## 1. Overview This document outlines a plan to integrate a simple but useful Retrieval-Augmented Generation (RAG) component into the MCP server. This will provide the server with a memory capability, allowing it to store and retrieve information from past interactions, documentation, or other text-based data sources to provide more context-aware and accurate responses. We will leverage existing open-source libraries to avoid building a new system from scratch. ## 2. Proposed Technology Stack - **RAG Framework**: `LangChain.js` - **Reasoning**: It is a popular and comprehensive library for building applications with LLMs. It provides all the necessary components for a RAG pipeline, including document loaders, text splitters, and integrations with embedding models and vector stores. - **Vector Store**: `Vectra` - **Reasoning**: `Vectra` is a lightweight, local, file-based vector database for Node.js. It requires no separate server or infrastructure, making it easy to integrate directly into the existing project. Data will be stored locally in a specified directory. - **Embedding Model**: `sentence-transformers/all-MiniLM-L6-v2` (or similar) - **Reasoning**: This is a high-quality, efficient model for creating text embeddings. It can be used locally via `LangChain.js` integrations, ensuring all data processing can happen within the project's environment. ## 3. Integration Steps 1. **Add Dependencies**: Install the required npm packages: `langchain`, `vectra`, and a library for using sentence-transformers (e.g., `@xenova/transformers`). 2. **Create a Memory Service**: - Create a new directory `src/memory/`. - Inside, create a `MemoryService.ts` class. This service will encapsulate all the RAG logic, including: - Initializing the `Vectra` vector store in a local directory (e.g., `./memory_store`). - Loading and processing documents (e.g., from `.md` files, or plain text). - Creating and storing vector embeddings. - Executing similarity searches to retrieve relevant information. 3. **Implement Memory Operations**: - Create a new file `src/operations/memory.ts`. - Define new MCP operations to interact with the `MemoryService`, such as: - `memory_addDocument`: An operation to add a new document (e.g., a file path or text content) to the memory. - `memory_query`: An operation to ask a question and retrieve the most relevant information from the memory. 4. **Update Server Configuration**: - Modify `src/config.ts` to include any necessary configuration for the memory service, such as the path to the vector store directory. 5. **Integrate into Server**: - Update `src/index.ts` to initialize the `MemoryService` and register the new memory operations. ## 4. Example Use Case Once integrated, the memory could be used to make the MCP server more knowledgeable about its own projects. For example: 1. The `MEGAETH_INTEGRATION_PLAN.md` file could be added to the memory using the `memory_addDocument` operation. 2. A user could then ask, "What is the plan for MegaETH integration?" 3. The MCP server would use the `memory_query` operation to retrieve the relevant sections from the plan and formulate an accurate answer. This approach provides a powerful, self-contained memory solution without adding significant external dependencies or infrastructure complexity.