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Azure AI Search delivers managed vector storage and semantic search for AI applications on Microsoft Azure. It features serverless scaling, hybrid keyword-vector search, semantic reranking, and integrations with Azure OpenAI. Suited for RAG pipelines and enterprise AI, it provides built-in AI enrichment and security advantages over self-hosted databases like Milvus.

Microsoft's approach to RAG that uses knowledge graphs to enhance retrieval. GraphRAG builds structured representations of documents enabling better context understanding and multi-hop reasoning for complex queries.

Native vector search capabilities in SQL Server 2022 and Azure SQL, enabling vector similarity search alongside traditional relational data. Supports storing vectors as varbinary and performing approximate nearest neighbor queries.

Microsoft research extension to DiskANN algorithm that enables efficient label-based filtering during vector search, allowing precise results with metadata constraints without sacrificing performance.

Open-source SDK from Microsoft that enables developers to build AI agents and integrate LLMs into applications with support for multi-agent orchestration, function calling, and memory management across C#, Python, and Java.

Microsoft's state-of-the-art multilingual text embedding model supporting 100 languages with 1024-dimensional embeddings, trained on 1 billion multilingual text pairs for robust cross-lingual retrieval.

Open-source text embedding models from Microsoft supporting 100+ languages. Features small, base, and large variants with weakly-supervised contrastive pre-training. This is an OSS model family released by Microsoft Research.