RAG Chatbot

This project implements a Retrieval-Augmented Generation (RAG) chatbot using Streamlit, Groq, and various NLP libraries. The chatbot can process PDF documents, answer questions based on the content, and maintain a conversation history.

Features

• PDF document upload and processing
• Text embedding and vector store creation
• Contextual question answering using RAG
• Real-time chat interface with Streamlit
• Integration with Groq API for language model inference

Prerequisites

• Python 3.7+
• Groq API key

Installation

1. Clone this repository:

   git clone the repo
   cd to it
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Set up your Groq API key:

   • Add your Groq API key:

     GROQ_API_KEY=your_api_key_here
     

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501).

3. Upload a PDF document using the file uploader.

4. Once the document is processed, you can start chatting with the bot about the content of the PDF.

How it Works

1. Document Processing: The app uses PyPDFLoader to read PDF files and TokenTextSplitter to split the text into manageable chunks.

2. Embedding and Indexing: Text chunks are embedded using HuggingFace's sentence transformers and stored in a Chroma vector store.

3. Query Processing: When a user asks a question, the app retrieves relevant context from the vector store.

4. Response Generation: The retrieved context and user query are sent to the Groq API, which generates a response using the Mixtral 8x7B model.

5. Chat Interface: Streamlit provides a user-friendly chat interface for interacting with the bot.

Customization

• You can modify the template in the initialize_session_state() function to change the chatbot's behavior.
• Adjust the chunk_size and chunk_overlap in the TokenTextSplitter to optimize document processing for your needs.
• Experiment with different embedding models by changing the model_name in the HuggingFaceEmbeddings initialization.