Storing and Retrieving Embeddings in Azure Cosmos DB
The lesson demonstrates how to configure vector policies at container creation, covering embedding property paths, data types (float32, float16, int8, uint8), dimension counts, and the three vector index types: flat, quantized flat, and diskANN, plus index exclusion patterns.
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