EyeLevel RAG 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:

   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:

   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