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This project is a RAG-based chatbot developed using Streamlit and Python. The chatbot provides information about temple culture and traditions using Retrieval-Augmented Generation (RAG). Features:

Interactive chatbot interface Retrieval-based response generation Streamlit frontend Document-based querying Easy-to-use UI

Tech Stack:

Python Streamlit LangChain Hugging Face / FAISS

How to Run:

Install dependencies using requirements.txt Run the application using: streamlit run app.py

Future Improvements:

Add multilingual support Improve response accuracy Add voice interaction support

This project is a RAG-based chatbot developed using Streamlit and Python. The chatbot provides information about temple culture and traditions using Retrieval-Augmented Generation (RAG). Features:

Interactive chatbot interface Retrieval-based response generation Streamlit frontend Document-based querying Easy-to-use UI

Tech Stack:

Python Streamlit LangChain Hugging Face / FAISS

How to Run:

Install dependencies using requirements.txt Run the application using: streamlit run app.py

Future Improvements:

Add multilingual support Improve response accuracy Add voice interaction support

This project is an AI-powered FAQ Chatbot developed using Retrieval-Augmented Generation (RAG) architecture. The chatbot allows users to ask questions related to uploaded PDF documents and retrieves the most relevant information using semantic search techniques. Instead of relying solely on a language model's pre-trained knowledge, the system first searches the document for relevant content and then returns context-aware responses based on the retrieved information. This improves accuracy and ensures that answers are grounded in the provided documents.