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.

Overview
Architecture
Features
Prerequisites
Installation
Configuration
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

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 first

hljs language-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:

hljs language-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:

hljs language-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:

hljs language-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:

hljs language-bash

python local_vector_store/prepare_corpus_and_data_locally.py

Note: Currently only processes .md and .pdf files. The system will:

Extract text from the documents
Split the text into manageable chunks
Generate embeddings for each chunk
Store the embeddings in the Qdrant vector database

Basic Usage

To test and run the system with default settings:

hljs language-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:

hljs language-bash

adk web

Project Structure

hljs language-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

adk-mcp-rag

RAG Agent with Google ADK and Qdrant MCP server

Table of Contents

Overview

Architecture

Features

Installation

Configuration

Environment Variables

Build Qdrant and Qdrant MCP

Usage

Document Ingestion

Basic Usage

Built-in ADK-UI

Project Structure

Similar Packages

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Publisher