Personal RAG MCP Server
A Model Context Protocol (MCP) server that provides personal knowledge base with RAG (Retrieval-Augmented Generation) capabilities. Share context across Claude Desktop, Claude Code, VS Code, and Open WebUI.
Features
Hybrid Storage: SQLite for full-text documents + Qdrant for semantic search
Rich Metadata: Comprehensive metadata capture for future extensibility
Dual Transport: stdio (for Claude Desktop/VS Code) + HTTP Streaming (for Open WebUI)
Forward-Compatible: Strategy pattern allows adding advanced RAG features without refactoring
Containerized: Runs in Docker, connects to existing Qdrant/Ollama/LiteLLM infrastructure
Architecture
User Input → MCP Tool
↓
[1] Generate embedding (Ollama)
↓
[2] Store full text + metadata in SQLite
↓
[3] Store vector in Qdrant
↓
Return confirmation
Search Query
↓
[1] Embed query (Ollama)
↓
[2] Search Qdrant (semantic search)
↓
[3] Retrieve full text from SQLite
↓
[4] Generate response (LiteLLM)
↓
Return answer + sources
MCP Tools
1. store_memory
Store notes, documents, or snippets in the knowledge base.
store_memory(
text="Your content here",
namespace="notes/personal", # Hierarchical organization
tags=["tag1", "tag2"],
title="Optional Title",
category="personal", # work, personal, family
content_type="note" # note, document, snippet
)
2. search_memory
Semantic search across your knowledge base.
search_memory(
query="What did I learn about X?",
namespace="notes/personal", # Optional filter
limit=5,
content_type="note" # Optional filter
)
3. ask_with_context
Ask questions with RAG (retrieval + generation).
ask_with_context(
question="What are my thoughts on X?",
namespace="notes/personal", # Optional filter
limit=5 # Context chunks to retrieve
)
Project Structure
personal-rag-mcp/
├── Dockerfile
├── requirements.txt
├── README.md
├── config/
│ ├── pipeline.yaml # RAG pipeline config
│ └── server.yaml # Server config
├── personal_rag_mcp/
│ ├── server.py # MCP server entry point
│ ├── storage/
│ │ ├── sqlite_store.py # SQLite document storage
│ │ ├── qdrant_store.py # Qdrant vector storage
│ │ └── schema.py # Pydantic metadata models
│ ├── pipeline/
│ │ ├── retriever.py # Retrieval strategies
│ │ ├── reranker.py # Reranking strategies
│ │ ├── expander.py # Query expansion
│ │ ├── generator.py # LLM generation
│ │ └── pipeline.py # RAG orchestration
│ └── utils/
│ ├── embeddings.py # Ollama embedding client
│ └── chunking.py # Text chunking
├── scripts/
│ ├── init_db.py # Initialize database
│ └── backup.py # Backup utility
└── tests/
Environment Variables
# Transport
TRANSPORT=http # or stdio
PORT=8765
# Storage
SQLITE_PATH=/app/data/documents.db
QDRANT_URL=http://qdrant:6333
# AI Services
OLLAMA_URL=http://ollama:11434
LITELLM_URL=http://litellm:4000
Development
Setup
# Create virtual environment
python -m venv venv
source venv/bin/activate # or `venv\Scripts\activate` on Windows
# Install dependencies
pip install -r requirements.txt
Run Locally (stdio)
export SQLITE_PATH=./data/documents.db
export QDRANT_URL=http://localhost:6333
export OLLAMA_URL=http://localhost:11434
export LITELLM_URL=http://localhost:4000
python -m personal_rag_mcp.server
Run Locally (HTTP)
export TRANSPORT=http
export PORT=8765
python -m personal_rag_mcp.server
Docker Deployment
Prerequisites
This MCP server depends on the following AI infrastructure services:
Qdrant (vector database) - Port 6333
Ollama (embeddings) - Port 11434
LiteLLM (LLM proxy) - Port 4000/8000
Example Docker Compose Integration
services:
# Required: Qdrant vector database
qdrant:
image: qdrant/qdrant:latest
container_name: qdrant
ports:
- "6333:6333"
volumes:
- qdrant-data:/qdrant/storage
restart: unless-stopped
# Required: Ollama for embeddings
ollama:
image: ollama/ollama:latest
container_name: ollama
ports:
- "11434:11434"
volumes:
- ollama-data:/root/.ollama
restart: unless-stopped
# Required: LiteLLM proxy for LLM access
litellm-proxy:
image: ghcr.io/berriai/litellm:main-latest
container_name: litellm-proxy
ports:
- "4080:8000"
volumes:
- ./litellm_config.yaml:/app/config.yaml
environment:
- AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
- AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
- AWS_REGION=${AWS_REGION}
- OLLAMA_API_BASE=http://ollama:11434
entrypoint: ["litellm", "--config", "/app/config.yaml", "--port", "8000"]
depends_on:
- ollama
restart: unless-stopped
# Personal RAG MCP Server
personal-rag-mcp:
build: ./personal-rag-mcp
container_name: personal-rag-mcp
ports:
- "8765:8765"
environment:
- TRANSPORT=http
- PORT=8765
- QDRANT_URL=http://qdrant:6333
- OLLAMA_URL=http://ollama:11434
- LITELLM_URL=http://litellm-proxy:8000
- OPENAI_API_KEY=${LITELLM_API_KEY} # LiteLLM auth
- SQLITE_PATH=/app/data/documents.db
volumes:
- personal-rag-data:/app/data
- ./config/personal-rag:/app/config:ro
depends_on:
- qdrant
- ollama
- litellm-proxy
restart: unless-stopped
volumes:
qdrant-data:
ollama-data:
personal-rag-data:
LiteLLM Configuration Example
The MCP server uses LiteLLM as a unified proxy, which means you can use any LLM provider:
Local: Ollama (llama3, deepseek, qwen, etc.)
Cloud: OpenAI, Anthropic Claude, Google Gemini, Cohere
AWS Bedrock: Claude, Llama, Mistral, etc.
Azure OpenAI: GPT-4, GPT-3.5
100+ other providers: See LiteLLM docs
Simply configure your preferred models in litellm_config.yaml:
model_list:
# Local Ollama models (no API key needed)
- model_name: deepseek-r1-1.5b
litellm_params:
model: ollama/deepseek-r1:1.5b
api_base: http://ollama:11434
# AWS Bedrock models
- model_name: bedrock-claude-3-5-sonnet-v2
litellm_params:
model: bedrock/us.anthropic.claude-3-5-sonnet-20241022-v2:0
aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
aws_region_name: us-east-2
# OpenAI models
- model_name: gpt-4o
litellm_params:
model: openai/gpt-4o
api_key: os.environ/OPENAI_API_KEY
# Anthropic Claude
- model_name: claude-3-5-sonnet
litellm_params:
model: anthropic/claude-3-5-sonnet-20241022
api_key: os.environ/ANTHROPIC_API_KEY
# Embedding model (for semantic search)
- model_name: nomic-embed-text
litellm_params:
model: ollama/nomic-embed-text
api_base: http://ollama:11434
general_settings:
master_key: sk-1234 # Set LITELLM_API_KEY in .env
The server defaults to using whatever model is configured in LiteLLM. You can easily switch between local and cloud models without changing the MCP server code.
Environment File (.env)
# LiteLLM API Key
LITELLM_API_KEY=sk-1234
# AWS Credentials (optional, for Bedrock models)
AWS_ACCESS_KEY_ID=your_access_key
AWS_SECRET_ACCESS_KEY=your_secret_key
AWS_REGION=us-east-2
First-Time Setup
Pull required Ollama models:
docker exec ollama ollama pull nomic-embed-text
docker exec ollama ollama pull deepseek-r1:1.5b
Verify services are running:
curl http://localhost:6333/collections # Qdrant
curl http://localhost:11434/api/tags # Ollama
curl -H "Authorization: Bearer sk-1234" http://localhost:4080/v1/models # LiteLLM
Test the MCP server:
docker exec personal-rag-mcp python /app/scripts/test_e2e.py
For complete infrastructure setup, see the parent repository.
Roadmap
Phase 1 (Current)
✅ Hybrid SQLite + Qdrant storage
✅ Basic RAG pipeline (vector retrieval)
✅ MCP tools (store, search, ask)
✅ Dual transport (stdio + HTTP)
Phase 2 (Future)
Advanced RAG features (reranking, hybrid search, query expansion)
Bulk document ingestion (PDF, DOCX parsing)
Conversation history capture
Multi-user support with authentication
License
MIT