EyeLevel RAG MCP Server

# EyeLevel RAG MCP Server A local Retrieval-Augmented Generation (RAG) system implemented as an MCP (Model Context Protocol) server. This server allows you to ingest markdown files into a local knowledge base and perform semantic search to retrieve relevant context for LLM queries. ## Features - **Local RAG Implementation**: No external dependencies or paid services required - **Markdown File Support**: Ingest and search through `.md` files - **Semantic Search**: Uses sentence transformers for embedding-based similarity search - **Persistent Storage**: Automatically saves and loads the vector index using FAISS - **Chunk Management**: Intelligently splits documents into searchable chunks - **Multiple Documents**: Support for ingesting and searching across multiple markdown files ## Installation 1. Clone this repository 2. Install dependencies using uv: ```bash uv sync ``` ## Dependencies - `sentence-transformers`: For creating text embeddings - `faiss-cpu`: For efficient vector similarity search - `numpy`: For numerical operations - `mcp[cli]`: For the MCP server framework ## Available Tools ### 1. `search_doc_for_rag_context(query: str)` Searches the knowledge base for relevant context based on a user query. **Parameters:** - `query` (str): The search query **Returns:** - Relevant text chunks with relevance scores ### 2. `ingest_markdown_file(local_file_path: str)` Ingests a markdown file into the knowledge base. **Parameters:** - `local_file_path` (str): Path to the markdown file to ingest **Returns:** - Status message indicating success or failure ### 3. `list_indexed_documents()` Lists all documents currently in the knowledge base. **Returns:** - Summary of indexed files and chunk counts ### 4. `clear_knowledge_base()` Clears all documents from the knowledge base. **Returns:** - Confirmation message ## Usage 1. **Start the server:** ```bash python main.py ``` 2. **Ingest markdown files:** Use the `ingest_markdown_file` tool to add your `.md` files to the knowledge base. 3. **Search for context:** Use the `search_doc_for_rag_context` tool to find relevant information for your queries. ## How It Works 1. **Document Processing**: Markdown files are split into chunks based on paragraphs and sentence boundaries 2. **Embedding Creation**: Text chunks are converted to embeddings using the `all-MiniLM-L6-v2` model 3. **Vector Storage**: Embeddings are stored in a FAISS index for fast similarity search 4. **Retrieval**: User queries are embedded and matched against the stored vectors to find relevant content ## File Structure - `main.py`: Main server implementation with RAG functionality - `pyproject.toml`: Project dependencies and configuration - `rag_index.faiss`: FAISS vector index (created automatically) - `rag_documents.pkl`: Serialized documents and metadata (created automatically) ## Configuration The RAG system uses the `all-MiniLM-L6-v2` sentence transformer model by default. This model provides a good balance between speed and quality for semantic search tasks. ## Example Workflow 1. Prepare your markdown files with the content you want to search 2. Use `ingest_markdown_file` to add each file to the knowledge base 3. Use `search_doc_for_rag_context` to find relevant context for your questions 4. The retrieved context can be used by an LLM to provide informed answers ## Notes - The first time you run the server, it will download the sentence transformer model - The vector index is automatically saved and loaded between sessions - Long documents are automatically chunked to optimize search performance - The system supports multiple markdown files and maintains source file metadata