isfnr1/qdrant-neo4j-crawl4ai-mcp

Qdrant Neo4j Crawl4AI MCP: Next-Gen AI/ML Server 🚀

Table of Contents

• Overview
• Features
• Architecture
• Installation
• Usage
• Configuration
• Deployment
• Contributing
• License
• Contact

Overview

The Qdrant Neo4j Crawl4AI MCP server integrates advanced technologies to provide a powerful solution for data management and AI-driven insights. It combines Qdrant's vector search capabilities, Neo4j's knowledge graphs, and Crawl4AI's web intelligence. This server is designed to support agentic RAG (Retrieval-Augmented Generation) functionalities, making it suitable for various AI and machine learning applications.

For the latest releases, visit our Releases section.

Features

• Vector Search: Utilize Qdrant for efficient vector-based search operations.
• Knowledge Graphs: Leverage Neo4j to create and manage complex relationships between data points.
• Web Intelligence: Integrate Crawl4AI for enhanced data collection and analysis from web sources.
• Agentic RAG Capabilities: Support for intelligent data retrieval and generation processes.
• FastMCP 2.0 Architecture: Designed for high performance and scalability.
• Enterprise Security: Robust security features to protect sensitive data.
• Monitoring Tools: Built-in monitoring for system health and performance.
• Kubernetes Deployment: Simplified deployment and management in containerized environments.

Architecture

The architecture of the Qdrant Neo4j Crawl4AI MCP server is built around the FastMCP 2.0 framework. This architecture emphasizes modularity, allowing easy integration of various components.

Key Components

1. Qdrant Vector Database: Handles vector embeddings and supports fast semantic search.
2. Neo4j Graph Database: Manages interconnected data and enables complex queries.
3. Crawl4AI Module: Gathers web data and enriches the knowledge graph.
4. Agentic RAG Engine: Processes data retrieval and generation tasks intelligently.
5. Kubernetes: Orchestrates the deployment and scaling of the server.

Data Flow

• Data is collected via Crawl4AI and stored in Neo4j.
• Qdrant processes vector embeddings for semantic search.
• The agentic RAG engine retrieves relevant data and generates insights.

Installation

To install the Qdrant Neo4j Crawl4AI MCP server, follow these steps:

1. Clone the Repository:

   git clone https://github.com/isfnr1/qdrant-neo4j-crawl4ai-mcp.git
   cd qdrant-neo4j-crawl4ai-mcp
   

2. Install Dependencies: Use pip to install the required Python packages.

   pip install -r requirements.txt
   

3. Set Up Environment Variables: Create a .env file in the root directory and configure your environment variables.

4. Run the Server: Start the server using the following command:

   python app.py
   

Usage

After installation, you can start using the server to manage your data and perform AI-driven tasks.

API Endpoints

The server provides several API endpoints for interaction:

• GET /search: Perform a vector search.
• POST /add-data: Add new data to the Neo4j database.
• GET /graph: Retrieve the knowledge graph.
• POST /generate: Use the agentic RAG engine for data generation.

Example Request

Here’s an example of how to perform a vector search:

curl -X GET "http://localhost:8000/search?query=your_query_here"

Configuration

You can customize the server's behavior through the configuration file. This file allows you to set parameters such as:

• Database connection strings
• Security settings
• Logging options

Example Configuration

[database]
url=neo4j://localhost:7687
username=your_username
password=your_password

[security]
enable_ssl=true

Deployment

Kubernetes Setup

Deploying the server in a Kubernetes environment involves creating the necessary deployment and service files.

1. Create Deployment File:

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: mcp-server
   spec:
     replicas: 3
     selector:
       matchLabels:
         app: mcp-server
     template:
       metadata:
         labels:
           app: mcp-server
       spec:
         containers:
         - name: mcp-server
           image: your_docker_image
           ports:
           - containerPort: 8000
   

2. Create Service File:

   apiVersion: v1
   kind: Service
   metadata:
     name: mcp-service
   spec:
     type: LoadBalancer
     ports:
     - port: 80
       targetPort: 8000
     selector:
       app: mcp-server
   

3. Deploy to Kubernetes: Use the following command to deploy:

   kubectl apply -f deployment.yaml
   kubectl apply -f service.yaml
   

Contributing

We welcome contributions to improve the Qdrant Neo4j Crawl4AI MCP server. If you want to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a pull request.

License

This project is licensed under the MIT License. See the file for details.

Contact

For questions or feedback, reach out to us through GitHub issues or contact the maintainers directly.

For the latest releases, check out our Releases section.

Topics

• agentic-rag
• ai
• crawl4ai
• fastmcp
• graph-database
• hybrid-search
• keyword-search
• kubernetes
• mcp
• mcp-server
• neo4j
• pydantic-ai
• pydantic-v2
• qdrant
• reranking
• semantic-search
• vector-database
• vector-embeddings