Documentation Retrieval (Python Libraries)

🚀 Document Retrieval MCP Server (DOCRET)

This project implements a Model Context Protocol (MCP) server, enabling AI assistants to access the latest documentation of various Python libraries, including LangChain, LlamaIndex, and OpenAI. By leveraging this server, AI assistants can dynamically obtain and provide relevant information from official documentation sources. The goal is to ensure that AI applications always have access to the latest official documentation.

🚀 Quick Start

The Model Context Protocol (MCP) is an open - source standard that allows developers to build secure two - way connections between their data sources and AI tools. The architecture is straightforward: developers can use an MCP server to expose their data or build AI applications (MCP clients) to access this data.

✨ Features

• Document Retrieval: Supports fetching documents from multiple sources.
• Dynamic Update: Capable of updating document content in real - time based on requests.
• Multi - Language Support: Supports documentation for multiple programming languages and frameworks.
• Intelligent Search: Provides context - based intelligent search functionality.

📦 Installation

Prerequisites

1. Install Python.
2. Install the pip package management tool.
3. Install the necessary dependencies:

   pip install requests
   pip install beautifulsoup4
   pip install lxml
   

Installation Steps

Install from Source Code

git clone https://github.com/your-repository.git
cd dorect
python setup.py install

Use Pre - built Packages

Download and install the binary file suitable for your operating system.

Environment Variable Configuration

Create a .env file:

export API_KEY=your_api_key_here
export ENDPOINT=http://localhost:8000

Running the Server

Run the MCP server:

python -m dorect.server

Access the console for more information.

💻 Usage Examples

Basic Usage

# Retrieve documents
dorect fetch --query "Python programming"

# Update the document library
dorect update --source "official_docs"

Advanced Usage

• Custom Parsers: Specify different parsers through a configuration file.
• Result Filtering: Supports filtering based on keywords or regular expressions.
• Output Format: You can choose JSON, Markdown, or plain text format.

📚 Documentation

Future Development Plans

1. Expand Support for More Sources
   • Support code hosting platforms such as GitHub and GitLab.
   • Integrate resources from Stack Overflow and other developer communities.
2. Enhance Search Functionality
   • Introduce machine learning models to improve search accuracy.
   • Implement semantic understanding to enhance query results.
3. Provide API Support
   • Release a RESTful API for external system calls.
   • Develop Webhooks for real - time update notifications.
4. Optimize Performance
   • Use a caching mechanism to reduce duplicate requests.
   • Introduce parallel processing to accelerate document retrieval speed.

References

• Model Context Protocol Official Documentation
• Python SDK Documentation

📄 License

This project is licensed under the MIT License. For more details, please refer to the LICENSE file.