RediSearch

Redis Vector Search (part of Redis Stack) enables vector similarity search on Redis with HNSW indexing, hybrid BM25+vector, and metadata filtering. It leverages Redis caching for low-latency real-time apps like semantic search. Vs dedicated DBs like Pinecone, Redis offers multi-model (JSON/KV + vectors) but requires more config for scale.

Redis Stack with vector search via RediSearch module, HNSW/Flat indexes on in-memory store. Features: Hybrid BM25+vector, real-time cache, multi-tenancy. Use cases: Caching LLM responses, high-throughput RAG. Comparisons: Faster than disk DBs for hot data; vs Memgraph: simpler key-value.

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.

ClickHouse is a columnar OLAP database with vector indexes (ANN via AMM, brute-force), supporting SQL queries over vectors + structured data at petabyte scale. Excels in aggregations with vectors. For analytics workloads with embeddings; faster ingestion than Postgres pgvector for big data.

Redis as vector database via RediSearch module supports HNSW/Flat indexes for real-time vector search in key-value store. Features: sub-ms latency, JSON payloads, modules ecosystem; use cases: caching + search hybrids. Vs dedicated VDBs, Redis excels in low-latency but limited scale for pure vectors.

BatANN system by Dang et al. for high-throughput distributed disk-based vector search. Supports scalable ANN in distributed environments.