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    1. Home
    2. Cloud & Managed
    3. Azure Cache for Redis Vector Search

    Azure Cache for Redis Vector Search

    Vector search capabilities in Azure Cache for Redis enabling high-performance similarity search and semantic caching. Supports HNSW and FLAT indexes with integration into Azure AI ecosystem.

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    Websitelearn.microsoft.com
    PublishedMar 20, 2026

    Categories

    1 Item
    Cloud & Managed

    Tags

    4 Items
    #Azure#Redis#Cloud#Managed

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    Overview

    Azure Cache for Redis provides vector embedding and similarity search capabilities, enabling semantic caching and real-time vector search in Azure cloud applications.

    Features

    Indexing Algorithms:

    • HNSW (Hierarchical Navigable Small World)
    • FLAT (brute-force for smaller datasets)

    Distance Metrics:

    • Cosine similarity
    • Euclidean distance (L2)
    • Inner product

    Integration:

    • Azure OpenAI
    • Azure AI services
    • Semantic Kernel
    • LangChain

    Use Cases

    Semantic Caching:

    • Cache LLM responses
    • Reduce API costs
    • Sub-millisecond retrieval

    Real-Time Search:

    • Product recommendations
    • Content similarity
    • User profiling

    Vector Functions

    • EMBED_TEXT_768: 768-dimensional embeddings
    • EMBED_TEXT_1024: 1024-dimensional embeddings
    • Custom embedding integration

    Benefits

    • Fully managed service
    • High availability (99.9% SLA)
    • Azure ecosystem integration
    • Enterprise security

    Availability

    Azure Cache for Redis (Premium/Enterprise tiers)