

Minima
Overview :
Minima is an open-source, fully localized Retrieval-Augmented Generation (RAG) model, capable of integrating with ChatGPT and the Model Context Protocol (MCP). It supports three modes: full local installation, querying local documents through ChatGPT, and querying local files using Anthropic Claude. The primary advantages of Minima include localized data processing, privacy protection, and the ability to leverage powerful language models to enhance retrieval and generation tasks. Background information indicates that Minima supports multiple file formats and allows users to customize configurations to fit different usage scenarios. Minima is free and open-source, targeting developers and enterprises seeking localized AI solutions.
Target Users :
The target audience includes developers and enterprises that need to handle and analyze documents locally, especially users who have high data privacy requirements. Minima is suitable for them as it offers a localized solution to process and retrieve a large volume of documents without relying on external services.
Use Cases
Enterprises use Minima to build internal knowledge bases, improving the efficiency of information retrieval for employees.
Developers leverage Minima to create custom document retrieval and generation tools tailored for specific projects.
Educational institutions use Minima to consolidate teaching materials, providing students with easier access to learning resources.
Features
Supports full local installation without requiring external network connections.
Ability to query local files using ChatGPT and Anthropic Claude.
Supports custom GPT integration to enhance local document retrieval capabilities.
Supports various file formats including .pdf, .xls, .docx, .txt, .md, .csv.
Provides Docker containerized deployment to simplify installation and usage.
Supports environment variable configuration for convenient personalization.
Allows interaction through a web interface to enhance user experience.
How to Use
1. Visit Minima's GitHub page and clone the project locally.
2. Create a .env file as needed and configure the relevant environment variables.
3. Choose the appropriate Docker Compose file (docker-compose-ollama.yml, docker-compose-chatgpt.yml, docker-compose-mcp.yml) based on your usage scenario for deployment.
4. Run the corresponding Docker Compose command to start the service.
5. If using the ChatGPT integration, copy the OTP from the terminal and authenticate using Minima GPT.
6. If using Anthropic Claude, add Minima's MCP server information in Claude's configuration file.
7. Interact with Minima via the web interface or API for document retrieval and generation tasks.
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