Overview
Vector Search can search from billions of semantically similar or semantically related items. A vector similarity-matching service has many use cases such as implementing recommendation engines, search engines, chatbots, and text classification. Semantic matching can be simplified into a few steps. First, you must generate embedding representations of many items (done outside of Vector Search). Secondly, you upload your embeddings to Google Cloud, and then link your data to Vector Search. After your embeddings are added to Vector Search, you can create an index to run queries to get recommendations or results.
The use of embeddings is not limited to words or text. You can generate semantic embeddings for many kinds of data, including images, audio, video, and user preferences. For generating a multimodal embedding with Vertex AI, see Get multimodal embeddings. In this lab, you will learn how to use Vertex AI Embeddings for text to create text embeddings and use them to create a vector search index.
Objectives
In this lab, you will perform the following tasks:
- Create a Vertex AI Notebook Instance
- Clone and run the lab notebook
- Create text embeddings
- Create and deploy vector search index
- Query the index
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