Refreshing Embeddings with the Change Feed
The lesson explains how to use the Cosmos DB change feed with Azure Functions to automatically recalculate embeddings when document text changes, covering input/output bindings, idempotency, handling deletes, race conditions, and dead letter queues for a robust enterprise setup.
Previous Module
Introducing Azure Cosmos DB
This Module
AI Vector Search on Azure Cosmos DB
Introduction to Integrated Vector Search
Storing and Retrieving Embeddings in Azure Cosmos DB
Executing Vector Similarity Queries
Hybrid Search with Metadata Filtering
Refreshing Embeddings with the Change Feed
Optimizing Query Performance in Azure Cosmos DB for NoSQL
Understanding Index Types in Azure Cosmos DB
Configuring Range and Composite Indexes
Tuning Vector Indexes for Embedding Workloads
Reducing RU Costs with Strategic Indexing