Personal Knowledge Base Q&A Agent (RAG + MCP)

A local-first, lightweight Retrieval-Augmented Generation (RAG) system that indexes and queries your personal documents (PDFs, markdown, text files) and exposes this capability as a Model Context Protocol (MCP) server.

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Architecture Overview

1. Ingestion: Documents are loaded, split into smaller overlapping segments (800 characters with 150-character overlap), and converted into vector embeddings.
2. Local Vector Database: ChromaDB is used to store document chunks and run semantic queries.
3. Local ONNX Embeddings: All text embeddings are computed locally using an ONNX-optimized version of all-MiniLM-L6-v2. This requires no API keys, runs fast on CPUs, and maintains privacy.
4. Answer Generation: Context is retrieved and sent to OpenAI's gpt-4o-mini using strict guidelines (temperature 0.0, answer-only-from-context) to prevent hallucination.
5. MCP Server Integration: Exposes the Q&A logic as a tool (query_knowledge_base) that LLM clients can execute directly.

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Directory Structure

e:\Personal_knowledge_base
├── documents/                     # Drop your PDFs, TXT, or MD files here
├── src/
│   ├── config.py                  # Environment variable configuration
│   ├── document_loader.py         # Reads text, markdown, and PDF files
│   ├── chunker.py                 # Splits documents recursively
│   ├── vector_store.py            # Local ChromaDB & ONNX embeddings
│   ├── rag_engine.py              # Retrieval & OpenAI prompt generation
│   └── mcp_server.py              # FastMCP Server (exposes query tool)
├── query.py                       # CLI client for ingestion/testing
├── requirements.txt               # Dependencies list
├── .env                           # Config (containing API key)
├── Dockerfile                     # Container config (pre-caches ONNX)
└── README.md                      # This documentation file

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Prerequisites

• Python 3.10 or higher.
• An OpenAI API Key (sk-...).

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Installation & Setup

1. Clone/Navigate to the project directory:

   cd e:\Personal_knowledge_base
   

2. Create a virtual environment and activate it:

   python -m venv .venv
   
   # On Windows:
   .venv\Scripts\activate
   
   # On macOS/Linux:
   source .venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Verify your .env file: Ensure you have your OpenAI API key in the .env file in the project root:

   OPENAI_API_KEY=your_openai_api_key_here
   CHROMA_DB_PATH=./chroma_db
   DOCUMENTS_DIR=./documents
   OPENAI_MODEL=gpt-4o-mini
   

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CLI Usage

1. Ingest Documents

Place your text, markdown, or PDF files into the documents/ directory. Then, run the ingestion pipeline:

python query.py --ingest

This loads files from documents/, chunks them, generates embeddings, and saves them locally in ./chroma_db.

2. Query the Knowledge Base

Ask questions about your documents:

python query.py "What is the database password for staging?"

3. Clear the Database

If you want to remove all indexed files and start fresh:

python query.py --clear

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Model Context Protocol (MCP) Server Setup

You can expose this tool directly to Claude Desktop, allowing the desktop LLM to search your private files whenever you ask a relevant question.

Configuration for Claude Desktop

Add the server definition to your Claude Desktop configuration file.

• Windows Path: %APPDATA%\Claude\claude_desktop_config.json
• macOS Path: ~/Library/Application Support/Claude/claude_desktop_config.json

Add the following to the mcpServers object (ensure you update the paths to match your absolute path, replacing backslashes with double backslashes \\ or forward slashes /):

{
  "mcpServers": {
    "personal-knowledge-base": {
      "command": "python",
      "args": [
        "E:/Personal_knowledge_base/src/mcp_server.py"
      ],
      "env": {
        "OPENAI_API_KEY": "your_actual_openai_key_here"
      }
    }
  }
}

Restart Claude Desktop, and you will see the plug icon indicating that the Personal Knowledge Base Server tool query_knowledge_base is available!

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Docker Integration

You can build and run the MCP server inside a Docker container. The Dockerfile is configured to pre-cache the ONNX model files during the build phase.

1. Build the image:

   docker build -t personal-knowledge-base .
   

2. Run the container (running a shell or exposing it if running HTTP transport): Since MCP default is stdio, Docker runs it on stdio. To use it with Claude Desktop, configure the desktop configuration to launch the container:

   {
     "mcpServers": {
       "personal-knowledge-base-docker": {
         "command": "docker",
         "args": [
           "run",
           "-i",
           "--rm",
           "-e", "OPENAI_API_KEY=your_actual_openai_key_here",
           "-v", "E:/Personal_knowledge_base/documents:/app/documents",
           "-v", "E:/Personal_knowledge_base/chroma_db:/app/chroma_db",
           "personal-knowledge-base"
         ]
       }
     }
   }