# Azure Cache for Redis Vector Search

> Vector search plugin for Azure Cache for Redis via RediSearch module, supporting HNSW/Flat indexes, hybrid lexical+vector with BM25 fusion, metadata filtering. Suited for enterprise semantic caching, real-time RAG, and recommendations. Integrated caching layer provides sub-ms latency vs standalone vector DBs like Weaviate.

- **URL:** https://learn.microsoft.com/en-us/azure/azure-cache-for-redis/cache-overview-vector-similarity
- **Category:** Full Text Vector Search Engines
- **Tags:** Redis Vss, Hybrid BM25, Real Time Cache, Redisearch, Azure, Redis, Cloud, Managed, Metadata Filtering, Rag Optimized, Knn Plugin, Hybrid Lexical Vector
- **Updated:** 2026-04-28 11:38
- **Canonical page:** https://vectordb.ever.works/items/azure-cache-redis-vector

## Details

## 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)

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