mishragauravgm / qna-faiss-rag

Document Querying using RAG, FAISS and LangChain

Overview

This project utilizes OpenAI's/HF's language models, Langchain, and Faiss for semantic search to create a custom summarization app. The app is designed to answer questions from any PDF document through a combination of semantic search and retrieval augmented generation (RAG).

Dependencies

Ensure you have the necessary dependencies installed by running:

pip install -r requirements.txt

The key dependencies include:

• OpenAI: Python wrapper for OpenAI API
• PyPDF2: Library for reading PDF files
• Langchain: A library for natural language processing tasks
• Streamlit: A web app framework for creating interactive data applications

Getting Started

1. Clone the repository:

git clone https://github.com/mishragauravgm/qna-faiss-rag
cd qna-faiss-rag

2. Set up your environment variables by creating a .env file with your OpenAI API key:

OPENAI_API_KEY=your-api-key
HF_KEY=your-hf-key

3. Run the application:

streamlit run app.py

Usage

1. Enter a user prompt and the path to a PDF file in the provided input fields.
2. Click the "Generate Answer" button to obtain the answer to your question.

Project Structure

The main components of the project include:

• app.py: The Streamlit web application.
• langchain: A library for natural language processing tasks.
• requirements.txt: List of Python dependencies for the project.

Additional Information

• RAG (Retrieval Augmented Generation): Utilizes Faiss for semantic search and the Hugging Face model repository for language models.
• Faiss: A library for efficient similarity search and clustering of dense vectors.