RAG Chatbot with AWS Bedrock and LangChain

This repository contains the implementation of a chatbot with Retrieval-Augmented Generation (RAG) capabilities. The chatbot leverages AWS Bedrock and LangChain to provide enhanced interactions by integrating retrieval-augmented generation.

Table of Contents

• Introduction
• Key Features
• Technologies Utilized
• Running the Application

Introduction

In this project, we aim to demonstrate the capabilities of Retrieval-Augmented Generation (RAG) by building a chatbot that leverages AWS Bedrock and LangChain. This chatbot integrates document retrieval and generation to provide contextually accurate and relevant responses.

Key Features

• Streamlit Interface: A sleek and intuitive front-end interface for effortless interaction with the chatbot.
• Context Awareness: Maintains conversation context using LangChain's ConversationBufferWindowMemory.
• Retrieval-Augmented Generation: Enhances chatbot responses by integrating document retrieval with the ConversationalRetrievalChain.
• Embeddings and Vector Store: Utilizes AWS Bedrock embeddings and FAISS for in-memory vector storage.
• PDF Document Source: Uses a PDF document as a source for retrieval-augmented generation.

Technologies Utilized

• Streamlit: For the front-end interface.
• LangChain: For managing conversation memory and retrieval chains.
• AWS Bedrock: For embeddings and pre-trained models.
• FAISS: As an in-memory vector store.
• RecursiveCharacterTextSplitter: For handling large text documents.
• PyPDFLoader: For loading PDF documents.

1. Install the required packages:

   pip install -r requirements.txt

2. Ensure you have your AWS credentials set up for accessing AWS Bedrock services.

Running the Application

3. Start the Streamlit application:

   streamlit run rag_chatbot_app.py --server.port 8080

4. Open your web browser and navigate to the URL provided by Streamlit to interact with the chatbot.