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Streamlit-Weaviate Connection

Weaviate Weaviate

This project is a submission for the Streamlit Connections Hackathon 2023. It delivers a Streamlit connector for the open-source vector database, Weaviate.

Overview

The Streamlit-Weaviate Connector enables developers to connect to a Weaviate database with the following Python code:

   conn = st.experimental_connection(
       "weaviate",
       type=WeaviateConnection,
       url=os.getenv("WEAVIATE_URL"),
       api_key=os.getenv("WEAVIATE_API_KEY"),
   )

This project also includes a Streamlit demo, "Magic Chat", designed to search through Magic The Gathering cards with various search options, such as BM25, Semantic Search, Hybrid Search and Generative Search. The live demo is accessible through Streamlit Community Cloud

Screenshot of the demo

📚 Quickstart Guide

🔧 Installation

This project uses poetry for dependency management. You can find more details more poetry in its documentation.

  1. Create a new Python virtual environment:
  • Ensure you have python >=3.10.0 installed
  • python3 -m venv env
  • source env/bin/activate
  • pip install poetry
  1. Install the project:
  • Install the project using poetry
  •      poetry add git+https://github.com/weaviate/st-weaviate-connection.git

🔗 Basic Usage

The project includes a demonstration notebook to showcase basic functionalities of the connector (see here demo notebook) and a streamlit app illustrating the implementation and usage of the connector.

Before you run the Jupyter notebook or the Streamlit app, create a .env file in the root directory of the project and add your Weaviate cluster and OpenAI API credentials:

Set environment variables:

WEAVIATE_URL= YOUR WEAVIATE_CLUSTER_URL
WEAVIATE_API_KEY= YOUR WEAVIATE_API_KEY
OPENAI_KEY= (ONLY NEEDED FOR STREAMLIT APP)

To set up your Weaviate cluster, follow either of these methods:

All connector functionality can be found in the connection.py python file. Documentation about st.experimental_connection can be found here.

✨ Streamlit

You can start the Streamlit app with the following command:

streamlit run streamlit_app.py

📦 Data Management

To use the demo locally, you need to import Magic card data into your Weaviate cluster. Inside the data directory, we provide three scripts.

  • add_card_schema.py Adds a card schema to your Weaviate cluster.
  • delete_card_schema.py Deletes the card schema and all saved objects.
  • retrieve_magic_cards.py Uses the Scryfall API to retrieve card information and saves them to your cluster.

💖 Open Source Contribution

Now, you're all set to use the Weaviate Connector for Streamlit. Happy coding!

We encourage open-source contributions. Feel free to suggest improvements, provide feedback, create issues, and submit bug reports!