LocalRAGify is a free, local Retrieval-Augmented Generation (RAG) system that combines open-source tools like OpenSearch and Ollama with a user-friendly chat interface powered by Streamlit. This project enables you to set up a cost-effective chatbot capable of answering queries using both retrieval and generative AI techniques.
- Zero-Cost: Fully local deployment using free and open-source tools.
- Generative AI: Powered by the Ollama LLM for interactive responses.
- Custom Retrieval: Integrates with OpenSearch for document retrieval.
- User-Friendly Interface: Built with Streamlit for seamless interaction.
Follow these steps to set up and run the application locally using Docker Compose.
- Docker and Docker Compose installed on your machine.
- Python 3.6+ installed (for local edits or testing).
Ensure the following files are present in your project directory:
.
├── dockerfile
├── compose.yml
├── app.py
├── llama.py
├── requirements.txt
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Clone the Repository
git clone https://github.com/your-repo-name/localragify.git cd localragify -
Build and Start Services
Use Docker Compose to build and run the application:docker-compose up --build
This command:
- Pulls the required images for OpenSearch and Ollama.
- Builds the LocalRAGify app Docker image.
- Starts both the OpenSearch vector store and the chatbot application.
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Access the Chat Interface
Once the containers are up and running, access the chatbot via your browser at:http://localhost:8501 -
Verify OpenSearch Service
The OpenSearch vector store is available at:http://localhost:9200
I welcome contributions! Feel free to open issues or submit pull requests for improvements.
This project is licensed under the MIT License.
- Ollama for the local LLM framework.
- OpenSearch for the vector database functionality.
- Streamlit for the interactive web interface.
Enjoy building your zero-cost local RAG chatbot with LocalRAGify! 🚀