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用例Data & Research

创建者::Azure Cosmos DB

网站learn.microsoft.com(在新窗口中打开)

工作原理

The official Azure Cosmos DB plugin bundles a curated set of agent skills covering data modeling, partition key design, query optimization, SDK best practices (singleton client, bulk operations, direct mode), indexing strategies (composite, spatial, exclude unused), vector search (DiskANN, flat, quantized flat), full-text search (BM25, hybrid queries), global distribution (multi-region writes, conflict resolution), security (Entra ID, managed identity, RBAC, network restrictions), throughput and scaling (autoscale, serverless), monitoring and diagnostics, and design patterns (change feed, transactional batch). It gives your agent the knowledge to generate reliable Cosmos DB code and apply best practices end to end.

Choose an orders partition key

Compare partition keys against order access patterns and expected growth to recommend a model that avoids hot partitions.

Reduce database query costs

Turn a costly query into a prioritized set of query and indexing changes with a plan to measure the improvement.

Design product similarity search

Create a vector search design for a product catalog with an index recommendation and queries that respect catalog filters.

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将组织的数据和工具整合至 OpenAI 产品,同时加速提升团队能力。