Data consistency, scalability, and performance are critical for data-intensive applications, which is why OpenAI chose to build the ChatGPT service on top of Azure Cosmos DB. You, too, can take advantage of its integrated vector database, as well as its single-digit millisecond response times,...
Azure Cosmos DB documentation Overview Welcome to Azure Cosmos DB FAQ Try Azure Cosmos DB free Choose an API Distributed NoSQL Distributed relational Integrated vector databases What is a vector database Vector database in Azure Cosmos DB for NoSQL ...
throughput. Containers that you created in a shared throughput database can't be updated to have dedicated throughput. To change a container from shared to dedicated throughput, you must create a new container and copy data to it. Thecontainer copyfeature in Azure Cosmos DB can make this ...
Azure Cosmos DB 作為向量資料庫 Azure Cosmos DB Conf 2024 在此會話中,我們會深入探討 Azure Cosmos DB 作為向量資料庫的使用率。 我們會探索其處理大規模向量數據、提供低延遲、高輸送量和全域分散式延展性的功能。 我們討論其多重模型支援,其允許以各種格式儲存和查詢向量數據。 我們也提供如何使用 Azure Cosmos...
Vector database in Azure Cosmos DB for MongoDB Related concepts AI Applications Quickstart - build a RAG chatbot AI agent Real-time custom content generation Azure AI Advantage free trial NoSQL MongoDB What is Azure Cosmos DB for MongoDB?
Microsoft DiskANN in Azure Cosmos DB, where we examine the impressive capabilities of Microsoft DiskANN, a state-of-the-art indexing system designed for accurate and cost-efficient vector search at any scale. We start off with a review of key vector search and database concepts, then follow by...
Pgvector supports two types of approximate indexes: Inverted File with Flat Compression (IVFFlat) index Hierarchical Navigable Small World (HNSW) index In this tutorial, you will: Create an IVFFlat and an HNSW index in an Azure Cosmos DB for PostgreSQL table. Wri...
Azure Cosmos DB Blog The latest news, updates and technical insights from the Azure Cosmos DB team
Since its conception in 2010, as a cloud-born database, we have carefully designed and engineered Azure Cosmos DB to exploit the three fundamental properties of the cloud.
What is data modeling and why should I care? How is modeling data in Azure Cosmos DB different to a relational database? How do I express data relationships in a non-relational database? When do I embed data and when do I link to data?