Implement full-text and vector search in Azure Cosmos DB for NoSQL

Module
9 Units

Advanced

Developer

AI Engineer

Azure Cosmos DB

Azure OpenAI Service

Add keyword and semantic retrieval to an Azure Cosmos DB for NoSQL container without adding a search service. Configure full-text and vector policies, rank results with BM25 and vector similarity, and keep embeddings aligned with the text they came from.

Learning objectives

After completing this module, you're able to:

Configure full-text policies and indexes and query text with BM25 scoring functions.
Design vector data and configure vector indexes, including vector partitioning.
Implement vector search with the VectorDistance function and keep embeddings current with the change feed.

Prerequisites

Familiarity with vector embeddings and their role in AI applications, and experience calling an embedding API such as Azure OpenAI
Working knowledge of Azure Cosmos DB for NoSQL containers, items, indexing policies, and queries
Programming experience with Python or C#

Introduction min
Implement full-text search min
Design vector data and policies min
Configure vector indexes and partitioning min
Implement vector search min
Keep embeddings current with the change feed min
Exercise: Build native search over a product catalog min
Module assessment min
Summary min

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