Develop AI solutions with Azure Cosmos DB for NoSQL
This learning path guides you through developing AI solutions using Azure Cosmos DB for NoSQL: building a data foundation with the resource model, SDK integration, CRUD, and SQL queries; implementing vector search for embeddings, similarity queries, hybrid search, and change-feed sync; and optimizing query performance via indexing and consistency levels.
Modules
Expand each module and click a topic to open detailed study notes.
Build queries for Azure Cosmos DB for NoSQLLearn how to connect to Azure Cosmos DB for NoSQL using the SDK, perform data operations on items, and write efficient SQL queries to retrieve document data for AI applications.
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Learn how to connect to Azure Cosmos DB for NoSQL using the SDK, perform data operations on items, and write efficient SQL queries to retrieve document data for AI applications.
See moreLearn how to connect to Azure Cosmos DB for NoSQL using the SDK, perform data operations on items, and write efficient SQL queries to retrieve document data for AI applications.
Implement vector search on Azure Cosmos DB for NoSQLLearn how to store vector embeddings, execute similarity queries using the VectorDistance function, combine vector search with metadata filters and hybrid search, and use the change feed to keep embeddings synchronized.
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Learn how to store vector embeddings, execute similarity queries using the VectorDistance function, combine vector search with metadata filters and hybrid search, and use the change feed to keep embeddings synchronized.
See moreLearn how to store vector embeddings, execute similarity queries using the VectorDistance function, combine vector search with metadata filters and hybrid search, and use the change feed to keep embeddings synchronized.
Optimize query performance for Azure Cosmos DB for NoSQLLearn how to optimize query performance by analyzing query patterns, configuring range and composite indexes, selecting vector index types, and choosing consistency levels that balance freshness with cost efficiency.
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Learn how to optimize query performance by analyzing query patterns, configuring range and composite indexes, selecting vector index types, and choosing consistency levels that balance freshness with cost efficiency.
See moreLearn how to optimize query performance by analyzing query patterns, configuring range and composite indexes, selecting vector index types, and choosing consistency levels that balance freshness with cost efficiency.