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.
- Clone the repository:
git clone https://github.com/shaadclt/LlamaIndex-Linear-LogisticRegression-Helper.git
cd LlamaIndex-Linear-LogisticRegression-Helper
- Install required dependencies:
pip install -r requirements.txt
- Setup .env
Create a .env file in the project directory and add the necessary environment variables:
# .env
OPENAI_API_KEY=your_openai_api_key
- Run the streamlit application
streamlit run main.py
- Enter your query in the provided text input.
- Click the "Submit" button to query.
- View the response provided.
If you'd like to contribute to the project, please follow the standard GitHub workflow:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and submit a pull request.
This project is licensed under the MIT License.