Retrieval-Augmented Generation (RAG)

Overview

This project demonstrates how to build a Retrieval-Augmented Generation (RAG) chatbot using OpenAI's API for generation and LangChain for managing text processing and retrieval.

Features

OpenAI API: Utilizes OpenAI's powerful language models for text generation. LangChain: Manages text splitting, embeddings, and retrieval operations. RAG Approach: Combines retrieval of relevant information with generation of responses for enhanced chatbot capabilities.

Requirements

Python 3.7+ Installation of required libraries: pip install openai langchain

Setup

1. Get OpenAI API Key Sign up for OpenAI API access at OpenAI API. Obtain your API key from the OpenAI dashboard.

2. Set Environment Variables Set your OpenAI API key as an environment variable: export OPENAI_API_KEY='openai_api_key'.

3.Extract the information from the url.

4.perform the retrival using open ai.

Example Interaction

Enter a query or question. The chatbot will retrieve relevant information using LangChain's retrieval capabilities. OpenAI will generate a response based on the retrieved information and the query.

This README.md template should give users a clear understanding of how to set up and run your RAG chatbot project using OpenAI's API and LangChain