RAG with LlamaIndex, Elasticsearch and Llama3 🦙

This repository houses a powerful tool that blends Streamlit, Elasticsearch, and cutting-edge language models like Llama3. It's designed to simplify querying documents via conversation vectors embedded into a dynamic, user-friendly web interface.

Features 🌟

• Document Upload: Upload JSON files with ease.
• Efficient Search: Powered by Elasticsearch.
• Natural Language Queries: Harness models like Llama3 to query using everyday language.
• Interactive UI: Real-time updates and feedback.
• Scalable: Built to manage extensive data volumes and complex queries.

Getting Started 🚀

Prerequisites

• Python 3.8+
• Elasticsearch 7.x+
• Ollama
• llama_index
• Streamlit
• poetry

Installation

Clone and set up the project:

git clone https://github.com/vitorhcsousa/rag-llama3-elasticsearch.git
cd rag-llama3-elasticsearch
poetry install

Usage

Launch the app:

poetry run streamlit run app.py

Structure 📂

• app.py: The Streamlit application entry point.
• index.py: Handles document indexing in Elasticsearch.
• query.py: Manages the query engine setup and execution.
• assets/: Static files like images for the UI.

Contributing 🤝

Interested in contributing? Great! Here's how:

1. Fork the repo.
2. Create your feature branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

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This project has as reference:
"Using RAG with LlamaIndex and Elasticsearch", Elastic Search Labs Blog, available at: https://www.elastic.co/search-labs/blog/rag-with-llamaIndex-and-elasticsearch. ↩