jacky-xbb / supabase-vector-py

how to use LangChain and Supabase to create a vector store for Documents using OpenAI embeddings.

Supabase Vector Store

This project demonstrates how to use LangChain and Supabase to create a vector store for Documents using OpenAI embeddings. The text data is split into manageable chunks and stored in Supabase for efficient retrieval.

Prerequisites

• Python 3.12
• Pipenv

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/supabase-vector-store.git
   cd supabase-vector-store

2. Install dependencies using Pipenv:

   pipenv install

3. Create a .env file in the root directory and add your Supabase and OpenAI credentials:

   SUPABASE_API_URL=your_supabase_api_url
   SUPABASE_API_KEY=your_supabase_api_key
   OPENAI_API_KEY=your_openai_api_key
   OPENAI_API_URL=your_openai_api_url
   

Usage

1. Ensure you have a text file named personal-info.txt in the root directory with the content you want to process.

2. Install the dependencies using Pipenv:

   pipenv install

3. Run the script:

   pipenv run python vector.py

4. If the script runs successfully, you should see the message:

   Documents stored successfully.
   

License

This project is licensed under the MIT License.

Contributing

Feel free to open issues or submit pull requests for improvements or bug fixes.

Acknowledgements

• LangChain
• Supabase
• OpenAI