Introduction

AI applications increasingly rely on semantic search to find relevant information based on meaning rather than exact keyword matches. This module covers how to implement vector search directly in Azure Cosmos DB for NoSQL.

By doing this, your AI applications can store high-dimensional vector embeddings alongside your standard JSON document data, allowing you to perform efficient similarity queries within the same database infrastructure.

The Real-World Scenario

Imagine you are building an AI-powered knowledge base for a customer support team.

Agents need to find relevant documentation, past tickets, and troubleshooting guides using natural language.

• The limitation of traditional keyword search: Keyword search fails when the user describes the problem differently than the documentation does. For example, a search for "can't connect to WiFi" might completely miss an article titled "wireless network troubleshooting".
• The semantic search solution: Each document is passed through an embedding model (like Azure OpenAI) to convert its text into high-dimensional vectors that capture the semantic meaning. When an agent asks a question, that question is also converted to a vector. The system then finds documents with mathematically similar vectors.

As a result, you get relevant articles even if the exact terminology differs.

Azure Cosmos DB for NoSQL integrates this capability, so you don't need to provision and sync a separate, dedicated vector database.

Learning Objectives

In this module, you will learn to:

1. Store and retrieve vector embeddings in Azure Cosmos DB containers with properly configured vector policies.
2. Execute vector similarity queries using the VectorDistance function to find semantically similar documents.
3. Combine vector search with metadata filters (like product category or date range) and full-text search using hybrid queries.
4. Implement change feed processing to automatically refresh embeddings when the source documents change.

Note: The implementations rely on the azure-cosmos Python SDK. It's recommended to consult the official SDK documentation for the most up-to-date syntax.