Showing tag results for RAG

Today we’re thrilled to announce the public preview of Semantic Reranker in Azure Cosmos DB for NoSQL, a new AI-powered capability that improves the relevancy of your search results with just a few lines of code. If you’ve ever run a vector, full-text, or hybrid search and wished the most relevant documents bubbled to the very top, this one’s for y...

Today we’re thrilled to announce the public preview of two new toolkits for Azure Cosmos DB: the Agent Memory Toolkit and the Agentic Retrieval Toolkit. If you’re building AI agents and retrieval-augmented generation (RAG) apps, these toolkits are designed to take you from prototype to production faster, giving your agents durable memory and your R...

Azure Cosmos DB enables scalable AI-driven document processing, addressing one of the biggest barriers to operational scale in today’s enterprise AI landscape. Organizations continue to manage massive volumes of structured and unstructured content—contracts, regulatory filings, operational records, images, and field documentation—yet many workflows...

Vector search with Azure Cosmos DB  In Part 1 and Part 2 of this series, we explored vector search with Azure Cosmos DB and best practices for scaling it to 1 billion vector datasets while maintaining low query latency. In Part 3, we saw how we can create efficient sharded DiskANN indices for multi-tenant apps.  In Part 4, we’ll look at the eff...

Vector Search with Azure Cosmos DB Azure Cosmos DB NoSQL features advanced vector indexing and search capabilities powered by DiskANN, a suite of highly scalable, accurate, and cost-effective approximate nearest neighbor (ANN) algorithms for low-latency vector search at any scale. Azure Cosmos DB features key capabilities important for building...