Configuring Vector Search in Spanner GSP1288

Overview
Imagine your applications searching your Spanner database and quickly identifying related data, even if the provided search phase is not actually included in the stored text! This is now possible by leveraging the power of Vertex AI text embeddings to conduct vector search within Spanner.

Spanner is a fully managed database service that offers transactional consistency at global scale and automatic, synchronous replication for high availability. In addition, you can leverage artificial intelligence (AI) functionality in Spanner to accomplish tasks such as building Generative AI applications and surfacing data in your Spanner database based on relevance to your specific search terms.

Vector search is a methodology that can be used to quickly find similar items based on their semantic meaning (rather than exact keyword matching) and can be applied to many types of data including audio, images, videos, and text. For text specifically, vector search enables you to find similar text items without needing their contents to match the exact text or phrase used in the search.

In this lab, you learn the fundamentals of configuring vector search in Spanner by first generating and storing text embeddings (vectors containing numerical representations of semantic meaning of text), and then using those text embeddings to perform fast similarity searches. This hands-on lab was adapted from the codelab titled Getting started with Spanner Vector Search and uses a dataset of bicycle products to highlight how vector search can be leveraged in Spanner to find the products most relevant to a search phrase without needing an exact match in the text.

What you'll do
In this lab, you learn how to:
- Create an embeddings model in Spanner and configure it to a Vertex AI model endpoint
- Create a table and load data in Spanner
- Generate and store text embeddings in Spanner
- Perform vector search in Spanner using text embeddings
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