Vector Search notebook tutorials

Vertex AI Matching Engine has been renamed to Vector Search. We are in the process of updating content to reflect the new branding.

A list of Jupyter Notebook tutorials is provided to help you get started using Vector Search.

Create a Vector Search index

In this notebook, you learn how to create an approximate nearest neighbor (ANN) index, query against the index, and validate its output performance. Run in Colab | View on GitHub

Create multimodal embeddings with the Vertex AI multimodal embeddings model and deploy to Vector Search

This example demonstrates how to create text-to-image embeddings using the DiffusionDB dataset and the Vertex AI multimodal embeddings model. In this notebook, you learn how to encode custom text embeddings, create an approximate nearest neighbor (ANN) index, and query. Run in Colab | View on GitHub

Use Vector Search and Vertex AI text embeddings for StackOverflow Questions

This example demonstrates how to encode text embeddings using the Vertex AI embeddings for text service and the StackOverflow dataset. These embeddings are uploaded to Vector Search. In this notebook, you learn how to encode text embeddings, create an Approximate Nearest Neighbor (ANN) index, and query against indexes. Run in Colab | View on GitHub

What's next

• See other Vertex AI notebook tutorials in the Tutorials overview
• Explore more resources in the Generative AI GitHub repo