zaaachos/genai-rag-agent

Your GenAI RAG-based Virtual Agent

GenAI-RAG-Agent

Welcome to GenAI-RAG Agent, your cutting-edge virtual Agent powered by Generative AI (GenAI) models and the innovative Retrieval-Augmented Generation (RAG) approach!

About

GenAI-RAG Agent is designed to be your ultimate companion, providing precise and relevant answers to your queries through state-of-the-art AI technology. With the integration of the RAG model, GenAI-RAG Agent ensures that you receive accurate information tailored to your needs.

Features

• Advanced AI Capabilities: Leveraging Generative AI (GenAI) models for intelligent responses.
• Retrieval-Augmented Generation (RAG): Incorporating the RAG model for precise and relevant answers. Used Pinecone for Vector Database.
• Web API: Simple FastAPI interface for user-agent interaction.

Getting Started

To start using GenAI-RAG Agent, follow these simple steps:

1. Environment Setup

Follow these steps to set up your environment:

• Clone the Repository:

git clone https://github.com/zaaachos/GenAI-RAG-Agent.git

• Install Dependencies:

It is highly recommended, to use conda as your virtual enviroment:

conda create -n chatbot python=3.9

conda activate chatbot

2. Dependencies

Install the necessary dependencies by running:

pip install -r requirements.txt

You will also need to have an Azure subscription, and create an .env file having the following variables:

AZURE_OPENAI_API_KEY=<YOUR_OPENAI_KEY>
OPENAI_MODEL_NAME=<YOUR_OPENAI_MODEL>
OPENAI_MODEL_VERSION=<YOUR_VERSION>
OPENAI_MODEL_DEPLOYMENT_VERSION=<YOUR_OPENAI_DEPLOYMENT_MODEL>
AZURE_OPENAI_ENDPOINT=<YOUR_OPENAI_ENDPOINT>
OPENAI_API_TYPE=azure
OPENAI_API_VERSION=2023-07-01-preview
PINECONE_API_KEY=<YOUR_PINECONE_KEY>
PINECONE_INDEX_NAME=<YOUR_PINECONE_INDEX>
EMBEDDINGS_MODEL_NAME=<YOUR_OPENAI_EMBEDDING>

3. Application

Run the Application Locally. Once dependencies are installed, you can run the FastAPI application locally by executing:

uvicorn main:app --reload

This will start the uvicorn server, and you can access the application at http://localhost:8000 in your web browser.