Wiki-RAG

Wiki-RAG (Mediawiki Retrieval-Augmented Generation) is a project that leverages Mediawiki as a source for augmenting text generation tasks, enhancing the quality and relevance of generated content.

Summary

Wiki-RAG integrates Mediawiki's structured knowledge (ingested via API) with language models to improve text generation. By using retrieval-augmented generation and some interesting techniques, it provides more contextually relevant and accurate responses using any Mediawiki site as KB.

Labeled as an experimental project, Wiki-RAG is part of the Moodle Research initiative, aiming to explore and develop innovative solutions for the educational technology sector.

Requirements

To get started with Wiki-RAG, ensure you have the following:

• Git
• Python 3.12 or later with pip (Python package installer)
• Docker (if you intend to run the project using Docker)
• Milvus 2.5.5 or later (for vector similarity search). Standalone or Distributed deployments are supported. Lite deployments are not supported. It's highly recommended to use the Docker Compose deployment specially for testing and development purposes.

Configuration

1. Set Environment Variables (to be replaced soon by config.ymlfile)
   • Using the env_template file as source, create a new .env file:

     cp env_template .env

   • Edit it to suit your needs (mediawiki site and namespaces or exclusions, models to use for both embeddings and generation, etc.)

Installation

Running Locally

1. Clone the Repository:

   git clone https://github.com/moodlehq/wiki-rag.git
   cd wiki-rag

2. Set Up Virtual Environment (optional but recommended):

   python -m venv .venv
   source .venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies:

   pip install -e .

   If interested into contributing to the project, you can install the development dependencies and enable all the checks by running:

   pip install -e .[dev]  # To install all the development dependencies.
   pre-commit install     # To enable all the check (style, lint, commits, etc.)

4. Run the Application: The application comes with four different executables:

   • wr-load: Will parse all the configured pages in the source Mediawiki site, extracting contents and other important metadata. All the generated information will be stored into a JSON file in the data directory.
   • wr-index: In charge of creating the collection in the vector index (Milvus) with all the information extracted in the previous step.
   • wr-search: A tiny CLI utility to perform searches against the RAG system from the command line.
   • wr-server: A comprehensive and secure web service (documented with OpenAPI) that allows users to interact with the RAG system using the OpenAI API (v1/models and v1/chat/completions endpoints) as if it were a large language model (LLM).

Running with Docker (Milvus elsewhere)

1. Pull the image from GitHub Container Registry:

   docker pull ghcr.io/moodlehq/wiki-rag:latest  # or specify a tag

2. Run the container:

   • Note 1: Don't forget to have the .env file in the same directory as the command below.
   • Note 2: The data directory will be created in the current directory, and it will store all the data generated by the wr-load command. If the LOADER_DUMP_PATH is set, you will have to change the volume mapping accordingly.

   docker run --rm --detach \
       --volume $(pwd)/data:/app/data \
       --volume $(pwd)/.env:/app/.env \
       --env MILVUS_URL=http://milvus-standalone:19530 \
       --network milvus \
       --publish 8080:8080 \
       --env OPENAI_API_KEY=YOUR_OPENAI_API_KEY \
       --env LOG_LEVEL=info \
       --name wiki-rag \
       wiki-rag:latest

   • Note 3: The command above will start the wr-server automatically, listening on the configured port (8080) and the OPENAI_API_KEY is required to interact with the embedding and LLM models. If, instead, you want to execute any of the other commands (wr-load, wr-index, wr-search), you can specify it as the last argument.
   • Note 4: The 2 lines related to Milvus are required to connect to the Milvus server if also running in Docker. If it's running elsewhere, you can replace the MILVUS_URL with the appropriate URL or configure it in the .env file instead
   • Note 5: You can use docker logs wiki-rag to check the logs of the running container (wr-server logs).
   • Note 6: When running the wr-server, you still can execute any of the commands (wr-load, wr-index, wr-search) using docker exec -it wiki-rag <command>.
   • Note 7: To stop and remove the container, you can use docker stop wiki-rag.

Running with Docker Compose (all-in-one)

1. Download the Milvus Docker Compose file:

   wget https://github.com/milvus-io/milvus/releases/download/v2.5.5/milvus-standalone-docker-compose.yml -O milvus-standalone.yml

   OR

   curl https://github.com/milvus-io/milvus/releases/download/v2.5.5/milvus-standalone-docker-compose.yml -o milvus-standalone.yml

2. Run Wiki-RAG own Docker Compose file:

   • Note 1: Don't forget to have the .env file in the same directory as the command below.
   • Note 2: The data directory will be created in the current directory, and it will store all the data generated by the wr-load command. If the LOADER_DUMP_PATH is set, you will have to change the volume mapping accordingly.
   • Note 3: The volumes directory will be created in the current directory, ant it will store all the data required by the Milvus containers.

   docker compose up -d

   • Note 4: Some useful commands include:
     • To stop all the containers, you can use docker compose stop.
     • To start again all the containers, you can use docker compose start.
     • To stop and remove all the containers, you can use docker compose down.
   • Note 5: You can use docker logs wiki-rag to check the logs of the running container (wr-server logs).
   • Note 6: When running, you still can execute any of the commands (wr-load, wr-index, wr-search) using docker exec -it wiki-rag <command>.

Features

Coming soon...

Future Work

Coming soon...

Documentation

For more detailed information, please refer to the following files:

• License
• Contributing Guidelines
• Code of Conduct

License

This project is licensed under the BSD 3-Clause License. See the LICENSE file for more information.

Contributing

We welcome contributions! Please see our Contributing Guidelines for more details.

Code of Conduct

Please note that this project adheres to a Code of Conduct. By participating, you agree to uphold this code.

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© 2025 Moodle Research Team