Minima is an open source fully local RAG, with ability to integrate with ChatGPT and MCP. Minima can also be used as a RAG on-premises.

Minima supports 3 modes right now. You can use fully local (minimal) installation, you can use Custom GPT to query your local documents via ChatGPT and use an Anthropic Claude for for querying local files.

For MCP usage, please be sure that your local machines python is >=3.10 and 'uv' installed.

1. Create a .env file in the project’s root directory (where you’ll find env.sample). Place .env in the same folder and copy all environment variables from env.sample to .env.

2. Ensure your .env file includes the following variables:

• LOCAL_FILES_PATH
• EMBEDDING_MODEL_ID
• EMBEDDING_SIZE
• START_INDEXING
• USER_ID
- required for ChatGPT integration, just use your email
• PASSWORD
- required for ChatGPT integration, just use any password

3. For fully local installation use: docker compose -f docker-compose-ollama.yml --env-file .env up --build.

4. For ChatGPT enabled installation use: docker compose -f docker-compose-chatgpt.yml --env-file .env up --build.

5. For MCP integration (Anthropic Desktop app usage): docker compose -f docker-compose-mcp.yml --env-file .env up --build.

6. In case of ChatGPT enabled installation copy OTP from terminal where you launched docker and use Minima GPT

7. If you use Anthropic Claude, just add folliwing to /Library/Application\ Support/Claude/claude_desktop_config.json

{
    "mcpServers": {
      "minima": {
        "command": "uv",
        "args": [
          "--directory",
          "/path_to_cloned_minima_project/mcp-server",
          "run",
          "minima"
        ]
      }
    }
  }

8. Ask anything, and you'll get answers based on local files in {LOCAL_FILES_PATH} folder.

Explanation of Variables:

LOCAL_FILES_PATH: Specify the root folder for indexing. Indexing is a recursive process, meaning all documents within subfolders of this root folder will also be indexed. Supported file types: .pdf, .xls, .docx, .txt, .md, .csv.

EMBEDDING_MODEL_ID: Specify the embedding model to use. Currently, only Sentence Transformer models are supported. Testing has been done with sentence-transformers/all-mpnet-base-v2, but other Sentence Transformer models can be used.

EMBEDDING_SIZE: Define the embedding dimension provided by the model, which is needed to configure Qdrant vector storage. Ensure this value matches the actual embedding size of the specified EMBEDDING_MODEL_ID.

START_INDEXING: Set this to ‘true’ on initial startup to begin indexing. Data can be queried while it indexes. Note that reindexing is not yet supported. To reindex, remove the qdrant_data folder (created automatically), set this flag to ‘true,’ and restart the containers. After indexing completes, you can keep the container running or restart without reindexing by setting this flag to ‘false’.

USER_ID: Just use your email here, this is needed to authenticate custom GPT to search in your data.

PASSWORD: Put any password here, this is used to create a firebase account for the email specified above.

Example of .env file for fully local usage and MCP usage:

LOCAL_FILES_PATH=/Users/davidmayboroda/Downloads/PDFs/
EMBEDDING_MODEL_ID=sentence-transformers/all-mpnet-base-v2
EMBEDDING_SIZE=768
START_INDEXING=false # true on the first run for indexing

Ollama chatting model - qwen2:0.5b (hard coded, but we will provide you with a model options in next updates)

Rerank model - BAAI/bge-reranker-base (used for both configurations: fully local and custom GPT)

To use a chat ui, please navigate to http://localhost:3000

Example of .env file for ChatGPT custom GPT usage:

LOCAL_FILES_PATH=/Users/davidmayboroda/Downloads/PDFs/
EMBEDDING_MODEL_ID=sentence-transformers/all-mpnet-base-v2
EMBEDDING_SIZE=768
START_INDEXING=false
[email protected] # your real email
PASSWORD=password # you can create here password that you want

Also, you can run minima using run.sh.

Minima (https://github.com/dmayboroda/minima) is licensed under the Mozilla Public License v2.0 (MPLv2).