Document-Based Q&A Chatbot

This chatbot allows users to ask questions based on pre-uploaded documents or upload their own documents. It leverages LangChain, Astra DB, and Hugging Face models to provide intelligent responses by analyzing and retrieving information from the documents stored in a vector database.

Features

• Pre-loaded Document Q&A: Ask questions and get responses based on documents already uploaded in the system.
• Custom Document Upload: Upload your own PDF documents and query them in real-time.
• Persistent Data Storage: Documents are stored in Astra DB, ensuring fast and efficient retrieval for Q&A.
• Seamless LLM Integration: Uses Hugging Face models for natural language understanding and response generation.

How It Works

1. Upload a PDF Document: Users can upload a PDF, and the document's content will be split, embedded, and stored in Astra DB.
2. Ask a Question: Users can ask questions, and the system will retrieve the most relevant answers from the uploaded documents.
3. Real-Time Q&A: After uploading a document, users can immediately query its contents and receive accurate answers.

Tech Stack

• Streamlit: For building the user interface.
• LangChain: To handle document processing, embedding, and retrieval.
• Astra DB: For storing document embeddings as vectors.
• Hugging Face: For language model-based Q&A.

Installation

1. Clone the repository.
2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables for Astra DB and Hugging Face.
4. Run the Streamlit app:

   streamlit run app.py

Future Improvements

• Support for additional document types.
• Multi-language support for broader use cases.
• Display of uploaded file titles.