Redis is an open-source, in-memory data store widely used for caching, real-time analytics, and as a primary database for low-latency applications. With the addition of vector similarity search via Redis Vector (formerly part of Redisearch), Redis has evolved into a multi-purpose platform that supports modern AI and machine learning workloads, including semantic search, recommendation engines, and hybrid retrieval systems.

Redis combines its core strengths—speed, simplicity, and in-memory performance—with the ability to store and query high-dimensional vector embeddings, making it suitable for AI applications that require fast, real-time results over small to medium vector datasets.

Key benefits include:

• Fast vector search using HNSW (Hierarchical Navigable Small World) indexing

• Hybrid queries combining structured filters and vector similarity

• Native integration with Redis ecosystem and real-time operations

What are the main features of Redis for vector search?

In-memory vector storage and retrieval

Redis supports storing dense vector embeddings as fields within documents, allowing ultra-fast access times.

• Ideal for use cases needing low-latency inference (e.g., chatbots, semantic ranking)

• Works well with datasets that fit into memory

• Supports vectors in float32 or binary formats

Similarity search with HNSW indexing

Redis uses the HNSW algorithm for approximate nearest neighbor (ANN) vector search.

• Efficient k-NN search in high-dimensional spaces

• Configurable parameters to balance accuracy and performance

• Scalable for small to medium-sized datasets (typically <100M vectors)

Hybrid queries with filtering and scoring

Redis enables hybrid search, combining vector similarity with traditional filters and metadata.

• Query syntax supports numeric, tag, and geo filters alongside vector scores

• Useful for e-commerce, recommendation, and personalized search

• Results can be ranked using a combination of vector distance and field relevance

Real-time updates and low-latency performance

As an in-memory store, Redis delivers sub-millisecond response times for reads and writes.

• Supports real-time vector ingestion, updates, and deletions

• No need to rebuild indexes after every update

• Suitable for applications that require fast feedback loops

Part of a modular and extensible ecosystem

Vector search in Redis is available via Redis Stack, which also includes search, JSON, time series, and graph modules.

• Works seamlessly with Redis JSON to store structured data and embeddings together

• Can be deployed on-premises or via Redis Cloud

• Integrates with popular frameworks like LangChain, OpenAI, and Hugging Face

Why choose Redis for vector search?

• Blazing-fast performance: Built for real-time, low-latency applications

• Multi-model database: Store vectors alongside structured, time series, or document data

• Open source and cloud-ready: Flexible deployment options

• Hybrid search support: Combine structured filters with vector similarity

• Strong ecosystem: Backed by a large community, tools, and language clients