This repository contains the code for a Library Management Chatbot. The chatbot is built using Colab, Streamlit, and various machine learning and natural language processing libraries. It is designed to answer queries related to a library's book collection.

• Uses Streamlit for the web interface.
• Integrates with various libraries for natural language processing and machine learning.
• Loads and processes library book data from a CSV file.
• Utilizes embeddings and vector stores for efficient document retrieval.
• Supports both HuggingFace and GPT-4All embeddings.
• Implements a question-answering system using LangChain and Groq.

To run this project, you need to have the following packages installed:

!pip install streamlit pyngrok faiss-cpu gpt4all groq huggingface-hub langchain langchain-community langchain-core langchain-groq langchain-openai langchain-text-splitters langserve langsmith sentence-transformers tokenizers transformers uvicorn unstructured

git clone https://github.com/yourusername/ChatbotforLMS.git cd ChatbotforLMS

Ensure all required packages are installed by running: pip install -r requirements.txt

The main application code is located in app.py. You can run this file using Streamlit: streamlit run app.py

To access the application locally, you can use the following command with localtunnel to expose your local server to the internet: streamlit run app.py & npx localtunnel --port 8501 Copy the tunnel URL provided by localtunnel and open it in your web browser.

File Structure app.py: The main application file. requirements.txt: List of dependencies required to run the application. Usage Load Documents: The chatbot loads library book data from a CSV file located at /content/sample_data/final_library_books.csv. Embedding and Vector Store: The data is processed using HuggingFace and GPT-4All embeddings and stored in a FAISS vector store for efficient retrieval. Question-Answering: Users can input queries related to the library books, and the chatbot will provide answers based on the context of the documents. Environment Variables The application uses a Groq API key for the language model. Ensure you set the GROQ_API_KEY environment variable in your Streamlit Cloud settings.

Acknowledgments Streamlit LangChain Groq HuggingFace