shaadclt / LlamaIndex-Linear-LogisticRegression-Helper

This Streamlit application utilizes the LlamaIndex framework for document indexing and querying linear and logistic regression-related information. It uses the OpenAI GPT-3.5 Turbo model for generating embeddings and incorporates various components for efficient document retrieval. Users can input their queries and receive responses.

LlamaIndex Linear and Logistic Regression Helper

This Streamlit application utilizes the LlamaIndex framework for document indexing and querying linear and logistic regression-related information. It uses the OpenAI GPT-3.5 Turbo model for generating embeddings and incorporates various components for efficient document retrieval. Users can input their queries and receive responses based on the indexed documents.

Getting Started

1. Clone the repository:

git clone https://github.com/shaadclt/LlamaIndex-Linear-LogisticRegression-Helper.git
cd LlamaIndex-Linear-LogisticRegression-Helper

2. Install required dependencies:

pip install -r requirements.txt

3. Setup .env

Create a .env file in the project directory and add the necessary environment variables:

# .env
OPENAI_API_KEY=your_openai_api_key

4. Run the streamlit application

streamlit run main.py

Usage

1. Enter your query in the provided text input.
2. Click the "Submit" button to query.
3. View the response provided.

Contributing

If you'd like to contribute to the project, please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and submit a pull request.

License

This project is licensed under the MIT License.