Optimizing Query Performance in Azure Cosmos DB for NoSQL
The lesson introduces optimizing Cosmos DB for AI workloads, explaining that hybrid search scenarios combine metadata filtering with vector search, and that the predictable query pattern of RAG makes it easy to tune indexes for maximum performance and minimal RU consumption.
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