InkChatGPT

A Simple LLM app that demonstrates a Retrieval-Augmented Generation (RAG) model for question-answering using LangChain, ChromaDB, and OpenAI's language models.

The app allows users to upload documents (PDFs or text files), and then ask questions related to the content of those documents. The RAG model retrieves relevant passages from the documents and generates an answer based on the retrieved context.

Running RAG to ask question about certain information inside the Document. For this demo, I use asked the model about the How to Build Your Career in AI PDF from Andrew Ng.

It's magic! 🪄

Features

Document Upload: Supports uploading PDF and text files.
Question Answering: Ask questions related to the uploaded documents.
Retrieval-Augmented Generation (RAG): Utilizes a RAG model for context-aware question answering.
LangChain Integration: Leverages LangChain for building the RAG model.
ChromaDB: Efficient vector storage and retrieval using ChromaDB.
OpenAI Language Models: Use gpt-3.5-turbo embedding model for answer generation.
Streamlit UI: Interactive and user-friendly web interface.

Installation

Clone the repository:

git clone https://github.com/vinhnx/InkChatGPT.git

Navigate to the project directory:

cd InkChatGPT

Create a new Python environment and activate it (e.g., using venv or conda).
Install the required packages:

pip install streamlit langchain chromadb openai tiktoken pypdf

or, run

pip install -r requirements.txt

Usage

Open VSCode and Create a New Python Environment:

Open Visual Studio Code.
Open the Command Palette by pressing Ctrl+Shift+P (Windows/Linux) or Cmd+Shift+P (macOS).
Search for "Python: Create Environment" and select it.
Choose the option to create a new virtual environment.
Give the environment a name (e.g., "llm-rag-env") and select the Python interpreter version you want to use.

Select the Python Interpreter:

Once the virtual environment is created, you'll be prompted to select the Python interpreter.
From the Command Palette, search for "Python: Select Interpreter" and choose the interpreter you just created (e.g., "llm-rag-env").

Open the Project Folder:

From the File menu, choose "Open Folder" or "Open..." and navigate to the project folder containing the app.py file.

Configure the App:

Navigate to OpenAI Platform > API Keys to generate an API Key to run the model
Create a .env file locally, this file is hidden and will be automatically ignored by .gitignore

touch .env

Inside .env file, pass the API Key into OPENAI_API_KEY value

OPENAI_API_KEY={YOUR_API_KEY_HERE}

Run the Streamlit App:

In the terminal, navigate to the project directory if you're not already there.
Run the following command to start the Streamlit app:
```
streamlit run app.py
```
This will start the Streamlit app and provide you with a local URL to access the app in your web browser.

Use the App:

Open the provided local URL in your web browser.
You should see the InkChatGPT interface.
Follow the instructions in the app to upload documents and ask questions.

Contributing

Contributions are welcome! If you'd like to improve the InkChatGPT, please follow these steps:

Fork the repository
Create a new branch: git checkout -b feature/my-feature
Commit your changes: git commit -am 'Add some feature'
Push to the branch: git push origin feature/my-feature
Create a new Pull Request

License

This project is licensed under the MIT License.

Contact

I'm Vinh, @vinhnx on almost everywhere. Feel free to reach out with any questions or feedback!