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

Minima currently supports three modes:

1. Isolated installation – Operate fully on-premises with containers, free from external dependencies such as ChatGPT or Claude. All neural networks (LLM, reranker, embedding) run on your cloud or PC, ensuring your data remains secure.

2. Custom GPT – Query your local documents using ChatGPT app or web with custom GPTs. The indexer running on your cloud or local PC, while the primary LLM remains ChatGPT.

3. Anthropic Claude – Use Anthropic Claude app to query your local documents. The indexer operates on your local PC, while Anthropic Claude serves as the primary LLM.

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
• OLLAMA_MODEL
• RERANKER_MODEL
• 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 (on your cloud or local pc). 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’.

OLLAMA_MODEL: Set up the Ollama model, use an ID available on the Ollama site. Please, use LLM model here, not an embedding.

RERANKER_MODEL: Specify the reranker model. Currently, we have tested with BAAI rerankers. You can explore all available rerankers using this link.

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 on-premises/local 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_MODEL=qwen2:0.5b # must be LLM model id from Ollama models page
RERANKER_MODEL=BAAI/bge-reranker-base # please, choose any BAAI reranker model

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

Example of .env file for Claude app:

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

For the Claude app, please apply the changes to the claude_desktop_config.json file as outlined above.

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).