LLM Powered Document Chat 📚

LLM Powered Document Chat is a web-based application powered by Streamlit and large language models (LLMs). It enables users to engage in a chat-based interaction with document repositories, allowing for information retrieval in a conversational manner.

Description

This application takes in multiple PDF files, extracts the text, and generates chunks of the text. These chunks are embedded into vectors and stored. Using these vectors and a Long-Short-Term-Memory model (LLM), the application creates a conversation chain that allows the user to ask questions and receive answers based on the content of the uploaded PDFs.

Installation

Clone the repository:

git clone [email protected]:thissayantan/gpt-pdf.git
cd gpt-pdf

The project uses Poetry for dependency management. If you don't have it installed, you can install it as follows:

For macOS / Linux / BashOnWindows:

curl -sSL https://install.python-poetry.org | python -

For Windows PowerShell:

(Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -

Configure Poetry to create virtual environments within your project's directory:

poetry config virtualenvs.in-project true

Create a new virtual environment:

poetry shell

Install the necessary dependencies:

poetry install

Set up the environment variables (including OpenAI API key, if applicable) in a .env file.

Usage

Start the Streamlit application:

streamlit run app.py

In the interface, select the LLM and embeddings model, upload documents, and start the conversation.

Example

# Start the application
streamlit run app.py

# In the application:
# 1. Choose an LLM from the dropdown (e.g., "OpenAI").
# 2. Choose an embeddings model (e.g., "OpenAI").
# 3. Upload a PDF or text document.
# 4. Click "Process" to process the documents and prepare the chat.
# 5. Type your question into the chat box and press enter to receive a response.

Contributions

Feel free to submit a pull request to contribute to this project.

License

This project is licensed under MIT License. For more information, please see the LICENSE file.

Contact

If you have any questions or suggestions, feel free to open an issue or pull request.