Introduction to Integrated Vector Search
The lesson explains how Azure Cosmos DB integrates vector search without a separate vector database, covering embeddings as points in higher-dimensional space, similarity search based on meaning, and the three distance functions: cosine, dot product, and Euclidean distance.
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