adk-mcp-rag

[](https://mseep.ai/app/khoi03-adk-mcp-rag) # RAG Agent with Google ADK and Qdrant MCP server A Retrieval-Augmented Generation (RAG) system that leverages Google's Agent Development Kit (ADK) and Qdrant vector database via MCP server. ## Table of Contents - [Overview](#overview) - [Architecture](#architecture) - [Features](#features) - [Prerequisites](#prerequisites) - [Installation](#installation) - [Configuration](#configuration) - [Usage](#usage) ## Overview This project implements a Retrieval-Augmented Generation (RAG) system that combines the power of Google's Agent Development Kit (ADK) with Qdrant vector database (via MCP server) for efficient knowledge retrieval. The system enhances Large Language Model (LLM) responses by retrieving relevant context from a vector database before generating answers. ## Architecture  ## Features - **Advanced Retrieval**: Semantic search powered by Qdrant vector database - **Google ADK Integration**: Leverages Google's Agent Development Kit for LLM capabilities - **MCP Server**: Model Context Protocol server for Qdrant vector database - **Context Augmentation**: Enhances LLM responses with relevant retrieved information - **Monitoring & Logging**: Comprehensive tracking of system performance ## Installation Remember to install the [uv](https://docs.astral.sh/uv/getting-started/installation/) first ```bash # Clone the repository git clone https://github.com/khoi03/adk-mcp-rag.git cd adk-mcp-rag # Create a virtual environment and install dependencies uv sync # Activate the virtual environment # For macOS/Linux source .venv/bin/activate # For Windows .venv\Scripts\activate # Add required dependencies via requirement file (In case there are new libs) uv add -r requirements.txt ``` ## Configuration ### Environment Variables Create a `.env` file in `docker` directory: ```bash # Change directory into docker cd docker # Set up environment variables cp .env.example .env # Edit .env with your API keys and configuration ``` Example `.env` file: ```bash # ------------------- # Google API keys # ------------------- GOOGLE_API_KEY=YOUR_VALUE_HERE # ------------------- # OPENAI API keys # ------------------- OPENAI_API_KEY=YOUR_VALUE_HERE # ------------------- # ANTHROPIC API keys # ------------------- ANTHROPIC_API_KEY=YOUR_VALUE_HERE # ------------------- # Network names # ------------------- NETWORK_NAME=mcp-servers # ---------------------------------- # Parameters for Qdrant MCP Server # ---------------------------------- QDRANT_CONTAINER_NAME=qdrant-mcp QDRANT_URL=http://qdrant:6333 QRANT_MCP_SSE=http://localhost:8888/sse # QDRANT_LOCAL_PATH=/qdrant/db COLLECTION_NAME=default_collection QDRANT_EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2 QDRANT_SEARCH_LIMIT=3 FASTMCP_HOST=0.0.0.0 FASTMCP_PORT=8888 ``` ### Build Qdrant and Qdrant MCP Build Qdrant and Qdrant MCP server using Docker Compose after completing the configuration steps: ```bash # Build and start services docker compose -p qdrant-mcp up --build -d # Check running services docker compose ps # View logs docker compose logs -f # Stop and remove services docker compose -p qdrant-mcp down ``` You can also access qdrant web UI at `http://localhost:6333/dashboard` ## Usage ### Document Ingestion Push all your needed files into the `data` directory and run: ```bash python local_vector_store/prepare_corpus_and_data_locally.py ``` Note: Currently only processes .md and .pdf files. The system will: 1. Extract text from the documents 2. Split the text into manageable chunks 3. Generate embeddings for each chunk 4. Store the embeddings in the Qdrant vector database ### Basic Usage To test and run the system with default settings: ```bash python main.py ``` ### Built-in ADK-UI For tracing, testing, and debugging with a UI, run the built-in web interface provided by ADK: ```bash adk web ``` ## Project Structure ```bash adk-mcp-rag/ ├── assets/ # Images and static files ├── data/ # Documents for ingestion ├── docker/ # Docker configurations │ ├── .env.example # Example environment variables │ ├── Dockerfile.qdrant # Docker file for qdrant mcp │ └── docker-compose.yml # Docker Compose configuration ├── agents/ # Main code │ ├── config/prompts.yml # Store prompts │ ├── tools/ # Embedding generation │ ├── mcp_tools.py # Manage MCP Tools │ └── prompts.py # Manage Prompts │ └── agent/ # Manage agents ├── .gitignore # Git ignore file ├── main.py # Main entry point ├── README.md # This file └── requirements.txt # Python dependencies ```