File Context MCP

🚀 File Context Machine Learning Console

This project is a File Context Machine Learning Console (MCP) designed to analyze and understand the content of various files, providing valuable insights. The system can handle multiple file types and utilizes different machine learning models to generate file summaries, identify key entities, and extract useful information.

🚀 Quick Start

Environment Requirements

• Node.js (Version 14 or higher is recommended)
• Docker (Optional, for containerized deployment)

Installation and Running

# Clone the repository
git clone https://github.com/your-repository/file-context-mcp.git

# Navigate to the project directory
cd file-context-mcp

# Install dependencies
npm install

# Start the application
npm start

✨ Features

Core Features

1. File Content Analysis

   • Natural Language Processing (NLP) to understand text content.
   • Identification and parsing of code snippets.

2. Context Awareness

   • Understanding the role and position of a file within a project.
   • Associating related files and information.

3. Machine Learning Integration

   • Integrating multiple pre - trained language models (e.g., Ollama, Together.ai) for content analysis.
   • Supporting the extension of custom models.

4. User Interaction

   • Providing an intuitive Web interface for file management and viewing analysis results.
   • Supporting Command - Line Interface (CLI) and API access.

Technology Selection

• Language and Framework: Node.js and TypeScript, ensuring strong - type support and efficient backend development.
• Machine Learning Models: Ollama and Together.ai offer powerful text analysis capabilities.
• File Storage: Using the local file system for storage to ensure high performance and simple deployment.

🔧 Technical Details

Architectural Decisions

Modular Design

• Core Modules
  • File processing and management
  • Context parsing
  • Machine learning integration

Design Principles

1. Scalability: Supporting the easy addition of multiple file types and analysis models.
2. Maintainability: Improving code readability and maintenance efficiency through modular design.
3. Performance Optimization: Using asynchronous processing and parallel computing to enhance system response speed.

Core Modules

File Processing and Management

Responsible for file upload, storage, retrieval, and deletion operations, supporting multiple formats (e.g., txt, docx, pdf, etc.).

Context Parsing

Using NLP technology to extract key information from files and build a context relationship graph.

Machine Learning Integration

Integrating various pre - trained models, providing a flexible model switching mechanism, and supporting the access of custom models.

Project Structure

file-context-mcp/
├── src/                 # Source code directory
│   ├── server.ts       # Main application server
│   ├── core/           # Core function modules
│   │   ├── fileSystem.ts      # File operation processing
│   │   └── modelInterface.ts  # Machine learning model interface
│   ├── utils/          # Utility functions
│   │   ├── fileUtils.ts       # File type and path utilities
│   │   ├── promptUtils.ts    # Prompt formatting utilities
│   │   ├── validators.ts     # Input validation utilities
│   │   └── logger.ts        # Application logging
│   ├── config/         # Configuration files
│   │   └── config.ts      # Environment variables and application configuration
│   └── resources/     # API specification documents
│       └── file-context-api.yml  # OpenAPI specification file
├── storage/            # File storage directory
│   ├── code-samples/   # Sample code files
│   └── notes/         # Documents and notes
├── postman/           # Postman test collections
│   └── file-context-mcp.postman-collection.json  # Test case file
└── package.json       # Project dependency management file

📚 Documentation

Testing Guide

Postman Testing

You can quickly conduct functional tests through the provided Postman collection. Refer to resources/file-context-api.yml for specific API interface documentation.

Unit Testing

The project contains detailed unit test cases implemented using the Jest framework:

# Run unit tests
npm test

Future Plans

1. Support for More File Types: Such as image, audio, and video files.
2. Enhanced Model Performance: Optimizing existing models and introducing more advanced pre - trained models.
3. Extended Analysis Functions: Adding advanced analysis functions like sentiment analysis and topic modeling.
4. Improved Scalability: Implementing a distributed architecture to support large - scale data processing.

Contribution Guide

Contributions from the community are welcome! Please refer to CONTRIBUTING.md to learn how to participate in project development.