Chat with the Document (RAG): Attention is All You Need, Transformer

This project provides an interactive interface to chat with documents using a Retrieval-Augmented Generation (RAG) approach. It uses the "Attention is All You Need" Transformer paper as a primary document, Chroma for vector storage, and OpenAI's language model for chat responses. The application is built using Streamlit to provide an easy-to-use chat interface.

Project Structure

• retrieval_augment_generation_chat.py: The main application file that sets up the Streamlit interface and handles the chat logic.
• document_indexing.ipynb: A Jupyter Notebook responsible for loading, splitting, and creating embeddings from the document.
• Transformer.pdf: The primary document used for the chat context.
• chromadb: Directory where the Chroma vector store persists the document embeddings.

Requirements

• Python 3.8 or higher
• Streamlit
• LangChain
• OpenAI's API key
• Chroma
• PyPDFLoader

Installation

• Clone the repository:

git clone [email protected]:gopikrsmscs/chat-with-document-rag.git

cd chat-with-document-rag

• Install the required dependencies:

pip install streamlit langchain chromadb PyPDFLoader openai

• Set up your OpenAI API key: Export your API key to the environment variable:

export OPENAI_API_KEY='your_openai_api_key'

Features

• Document Interaction: Chat with the content of the Transformer paper using a retrieval-augmented generation approach.
• Contextual Responses: The model generates responses based on specific document sections, ensuring answers are relevant.
• Streamlit Interface: Basic interface to type queries and see responses.

License

This project is licensed under the MIT License. See the LICENSE file for more details.