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Multi-Docs ChatBot using Llama2 📚

This project is a document-based chatbot that uses the Llama2 model to generate responses based on the content of uploaded documents. It leverages the LangChain framework to manage conversational retrieval and document processing.

Features

• Document Upload: Users can upload multiple documents (PDF, DOCX, TXT) for processing.
• Conversational AI: The chatbot interacts with users based on the contents of the uploaded documents.
• Advanced NLP: Utilizes embeddings and vector stores to efficiently retrieve relevant document sections.

Installation

1. Clone the repository:

   git clone <repository_url>
   cd <repository_folder>

2. Install the required Python packages:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Upload Documents: Use the sidebar to upload PDF, DOCX, or TXT files.

3. Ask Questions: Interact with the chatbot by asking questions related to the uploaded documents.

Project Structure

• app.py: Main application file containing the Streamlit app and chatbot logic.
• requirements.txt: List of required Python libraries.

Dependencies

The project depends on several libraries for natural language processing and document handling:

• langchain
• torch
• accelerate
• sentence_transformers
• streamlit_chat
• streamlit
• faiss-cpu
• tiktoken
• ctransformers
• huggingface-hub
• pypdf
• python-dotenv
• replicate
• docx2txt

Acknowledgements

• LangChain for providing the framework to build conversational AI with document retrieval capabilities.
• Replicate for model deployment.

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