Executing Vector Similarity Queries
The lesson shows how to perform RAG by calculating the prompt embedding and running a SQL query using the VectorDistance function to find the closest documents, then injecting the retrieved text into the LLM context for retrieval augmented generation.
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