Tuning Vector Indexes for Embedding Workloads
The lesson explains how to choose between flat, quantizedFlat, and diskANN vector indexes based on container size and dimension count, recommends diskANN for production, and covers compressing embeddings to float16 to halve storage with minimal accuracy loss.
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