RAG Implementation Using Gemini, Chroma, and FAISS

This repository contains a Retrieval-Augmented Generation (RAG) implementation using Gemini with Chroma and FAISS Vector Database, focusing on the Wikipedia page for Luke Skywalker. The RAG model combines retrieval and generation capabilities to provide accurate and contextually relevant responses by leveraging external knowledge.

RAG is a powerful approach for enhancing natural language processing applications by incorporating external knowledge sources into the generation process. This project demonstrates how to set up and use RAG with Gemini for question-answering tasks based on the Wikipedia page of Luke Skywalker.

Features

Gemini Integration: Utilize Gemini for efficient knowledge retrieval. Chroma and FAISS: Leverage Chroma and FAISS for fast and accurate similarity search. Preprocessed Data: The repository includes preprocessed data from Luke Skywalker's Wikipedia page. Question Answering: Provide answers to questions about Luke Skywalker using retrieved knowledge.

Installation

Clone the repository: bash

git clone https://github.com/HarshP2109/Gemini-RAG.git
cd Gemini-RAG

Create and activate a virtual environment:

bash

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

Install dependencies:

bash

pip install -r requirements.txt

Setup Server (Backend)

fastapi dev mainServer.py

Setup Streamlit (Frontend)

streamlit run streampost.py

License

This project is licensed under the MIT License. See the LICENSE file for details.