Weaviate Python V4 Usage Notebook

This minimal notebook illustrates a very simple tour of Weaviate V4 python client.

Dependencies

• It depends on a local Weaviate container for which a docker-compose has been provided.
  • You should run it either locally or via an SSH tunnel on the 8077 and 50051 ports.
• It also depends on your OpenAI API key being provided (in the .env file).
  • Copy/rename the .env-example file to a .env file.
• The data directory contains description data about approximately 2000 ebooks. Most are English but there also are some Italian books.

Setup

• First of all, run poetry install to add the required python packages.
• Just run the notebook mybooks in the booksimil directory step by step observing its behaviors.

Note: The current version will throw an error if the data insertion portion is run again with the collection already created.

• To run all again either change the collection name in the .env file or delete the collection in the Weaviate database.

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Program Description

This program will read a collection of metadata about approx. 2000 books, will create a Weaviate collection for it using OpenAI's text2vec "text-embedding-ada-002" model. The rest of the notebook will show a few different types of semantic and generative queries.

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