This project provides a Python application for querying PDF documents using natural language, leveraging OpenAI's LLMs. It's designed for users who want to extract information from PDFs without needing to read them entirely, offering a guided tutorial for its creation.

How It Works

The application processes PDFs by chunking the text, generating vector embeddings using OpenAI, and performing semantic similarity searches to find relevant document sections. These relevant chunks are then passed to an LLM, along with the user's question, to generate contextually accurate answers.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Requires an OpenAI API key set in a .env file.
• Run the application: streamlit run app.py

Highlighted Details

• Utilizes Langchain for LLM orchestration.
• Employs Streamlit for a user-friendly GUI.
• Focuses on answering questions strictly related to the uploaded PDF content.

Maintenance & Community

This repository is for educational purposes and is not intended for further contributions. It serves as support material for a YouTube tutorial.

Licensing & Compatibility

The repository does not explicitly state a license. Given its educational purpose and reliance on OpenAI's API, commercial use or integration into closed-source projects may be restricted by OpenAI's terms of service and the lack of a clear open-source license.

Limitations & Caveats

The project is explicitly stated to be for educational purposes only and not intended for contributions, suggesting limited ongoing development or support.