QnA Chatbot

Project Overview

This project aims to build a Q/A chatbot using Retrieval-Augmented Generation (RAG) to answer questions from website FAQs. The project will involve web scraping, data preprocessing, building a chatbot using Streamlit, and integrating with AWS Elasticsearch for vector storage and retrieval.

Goals

• Scrape FAQ pages from selected websites.
• Build a Streamlit app for the chatbot interface.
• Use AWS Elasticsearch for storing and retrieving vector data.
• Evaluate the chatbot's performance using AWS Bedrock.

Setup Instructions

1. Clone the repository:

   git clone https://github.com/deveshcode/QnA_Chatbot.git

2. Create a new repository on GitHub.
3. Ask everyone's GitHub usernames and add them as collaborators to the repository.
4. Initialize the repository with a README file:

   touch README.md
   git add README.md
   git commit -m "Initial commit"
   git push origin main

5. Create a CONTRIBUTING.md file to outline coding standards, branching strategy, and pull request guidelines:

   touch CONTRIBUTING.md
   git add CONTRIBUTING.md
   git commit -m "Add CONTRIBUTING.md"
   git push origin main

6. Instruct everyone to fork the repository and create their own branch to work on:

   git checkout -b <branch-name>

7. Each contributor can now start working on their respective branches and make changes.
8. Once the changes are ready, they can create a pull request to merge their branch into the main branch.
9. Review the pull requests and merge them if they meet the coding standards and guidelines outlined in CONTRIBUTING.md.
10. Continue working on the project, following the established workflow.

Remember to replace <branch-name> with an appropriate name for each contributor's branch.

Let me know if you need any further assistance!