Gen-Ai-RAG-Enhanced-Interactive-QA-Bot

Overview

Gen-Ai-RAG-Enhanced-Interactive-QA-Bot is an advanced interactive question-answering system leveraging Retrieval-Augmented Generation (RAG) with enhanced AI technologies. The system utilizes Cohere for language models and Pinecone for vector search to deliver accurate and dynamic responses to user queries.

Table of Contents

• [Features]
• [Installation]
• [Configuration]
• [Usage]
• [Directory Structure]
• [Docker Setup]
• [Contributing]
• [License]

Features

• Interactive QA System: Advanced AI models for generating responses.
• Retrieval-Augmented Generation (RAG): Integrates retrieval with generation for enhanced accuracy.
• Cohere Integration: Utilizes Cohere’s language models for text generation.
• Pinecone Integration: Employs Pinecone for efficient vector search and retrieval.
• Data Storage: Saves generated answers to the data folder for further processing and analysis.
• Docker Support: Streamlined setup and deployment with Docker.
• Multi-Environment Configuration: Separate configurations for backend and frontend.

Installation

Prerequisites

• Node.js: Required for the frontend.
• Python: Required for the backend and RAG model.
• Docker: For containerized setup.

Backend Setup

** Navigate to the backend directory **

• cd "Backend"

• Install the necessary Python packages:

• pip install -r requirements.txt

• Set up the environment variables in the .env file as described in the Configuration section.

Frontend Setup

Navigate to the frontend directory:

• cd "Frontend"

• Install the necessary Node.js packages:

• npm install

// Set up the environment variables in the .env file as described in the Configuration section.

Configuration

Environment Variables

Create and configure the following .env files:

• Backend (Backend (RAG Model)/.env): Contains configuration for the RAG model, Cohere API key, Pinecone API key, and other backend settings.
• Frontend (Frontend (Interactive QA Bot)/.env): Contains configuration for the frontend application, including API URLs.
• Ensure these files are not included in version control. Check .gitignore for details.

.env Example

• Backend .env
• env
• DATABASE_URL=your_database_url
• SECRET_KEY=your_secret_key
• COHERE_API_KEY=your_cohere_api_key
• PINECONE_API_KEY=your_pinecone_api_key
• DATA_FOLDER=path_to_data_folder

Frontend .env

• env
• BACKEND_URL=http://backend:5000

Usage

Running Locally

Start the Backend:

• cd "Backend (RAG Model)"
• python app.py

Start the Frontend:

• cd "Frontend (Interactive QA Bot)"
• npm start

Docker Setup

Build Docker Images:

• docker-compose build

Run Docker Containers:

• docker-compose up

Directory Structure

GEN-AI-RAG-ENHANCED-INTERACTIVE-QA-BOT

• ├── Backend (RAG Model)
• │ ├── pycache
• │ ├── config
• │ ├── data
• │ │ ├── answer_20240917_161421.txt
• │ │ └── answer_20240918_083208.txt
• │ ├── models
• │ ├── myapp
• │ │ ├── pycache
• │ │ ├── init.py
• │ │ ├── routes.py
• │ ├── vector_store
• │ │ ├── pycache
• │ │ └── pinecone_db.py
• │ ├── .env
• │ ├── Dockerfile
• │ ├── manage_index.py
• │ └── requirements.txt
• ├── Frontend (Interactive QA Bot)
• │ ├── app
• │ │ ├── app.py
• │ ├── static
• │ ├── .env
• │ ├── Dockerfile
• │ └── requirements.txt
• ├── .gitignore
• ├── .dockerignore
• └── docker-compose.yml

Contributing

Contributions are welcome! Please follow these steps to contribute:

• Fork the Repository
• Create a New Branch
• Make Your Changes
• Commit Your Changes
• Push to Your Fork
• Create a Pull Request

Clone the Repository

• git clone https:// github.com/MithunKumar09/Gen-Ai-RAG-Enhanced-Interactive-QA-Bot.git
• cd Gen-Ai-RAG-Enhanced-Interactive-QA-Bot