Master the Semantic Kernel vector store in C# with Azure AI Search, Qdrant, and InMemoryVectorStore for RAG and semantic search.

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Semantic Kernel's IVectorStore abstraction in C# provides a unified API for working with multiple vector database backends including InMemoryVectorStore, Azure AI Search, and Qdrant. The post covers defining vector store records with attributes (VectorStoreKey, VectorStoreData, VectorStoreVector), setting up each provider via dependency injection, performing CRUD operations and batch methods, running semantic similarity searches with optional metadata filtering, and implementing hybrid search combining vector and keyword queries. A comparison table helps developers choose between providers based on scale, infrastructure preferences, and cost. The recommended approach is to develop with InMemoryVectorStore and graduate to Azure AI Search or Qdrant for production workloads.

Semantic Kernel Vector Store in C#: Azure AI Search, Qdrant, and Beyond

InMemoryVectorStore: Development and Testing

Azure AI Search: Production-Grade Vector Search