# Rag Optimized

> Browse 12 items tagged with Rag Optimized.

## Items

- [Elasticsearch Vector Search](https://vectordb.ever.works/items/elasticsearch-vector-search) — Lucene KNN vector plugin for Elasticsearch search engine, enabling hybrid lexical+vector search, BM25 fusion, HNSW/IVF indexes for ANN. Used for enterprise search, RAG, multimodal apps. Integrated vs standalone like Weaviate: superior hybrid text handling but higher resource footprint.
- [Qdrant Vector Database](https://vectordb.ever.works/items/qdrant-vector-database) — Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as RAG, recommendation systems, advanced semantic search, anomaly detection, and AI agents. It provides scalable storage and retrieval of vector embeddings with features like filtering, hybrid search, and production‑grade APIs for integrating with machine learning workloads.
- [Supermemory](https://vectordb.ever.works/items/supermemory) — State-of-the-art AI agent memory system using ASMR technique that achieved ~99% accuracy on LongMemEval benchmark with multi-agent orchestrated pipeline.
- [Azure Cache for Redis Vector Search](https://vectordb.ever.works/items/azure-cache-redis-vector) — 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.
- [OpenSearch Vector Search](https://vectordb.ever.works/items/opensearch-vector-search) — k-NN vector plugin for OpenSearch (Lucene-based), supporting hybrid lexical+vector, BM25 fusion, HNSW/IVF indexes, multimodal. For enterprise RAG, semantic search. Integrated vs standalone like Weaviate: excels in hybrid text+vector but heavier footprint.
- [AlayaDB](https://vectordb.ever.works/items/alayadb) — Hybrid database-inference engine that converts documents to tensors via LLM forward pass, storing in a KV cache for optimized retrieval. Features integrated storage and inference with advanced indexing for fast context retrieval in RAG pipelines. Suited for LLM applications and semantic search; differs from Milvus by embedding inference, more specialized than Qdrant's pure vector storage.
- [Redis LangCache](https://vectordb.ever.works/items/redis-langcache) — 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.
- [Manu — A Cloud Native Vector Database Management System](https://vectordb.ever.works/items/manu-a-cloud-native-vector-database-management-system) — VLDB 2022 paper introducing Manu, a cloud-native vector database management system designed for scalable similarity search in cloud environments with separated storage and compute architecture.
- [PostgreSQL (with pgvector)](https://vectordb.ever.works/items/postgresql-with-pgvector) — Powerful open-source object-relational database system that, with the pgvector extension, serves as a capable vector database for AI applications. Widely used from small projects to large-scale enterprise systems, and offered as managed services by major cloud providers.
- [VAST AI OS](https://vectordb.ever.works/items/vast-ai-os) — GPU-accelerated platform from VAST Data that includes a native vector database, designed for enterprise AI workloads including multi-agent systems, video-reasoning, and high-volume RAG. It combines vector embeddings with structured data and metadata in unified tables, enabling hybrid queries across modalities without orchestration layers or external indexes.
- [VAST CNode-X](https://vectordb.ever.works/items/vast-cnode-x) — GPU-accelerated server from VAST Data that combines the VAST AI OS with NVIDIA data-processing libraries and onboard NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Designed for enterprise AI workloads requiring high-throughput vector search, data vectorization, and inference, it leverages the NVIDIA AI Data Platform reference design.
- [Vexless — Serverless Vector Data Management](https://vectordb.ever.works/items/vexless-serverless-vector-data-management) — SIGMOD 2024 paper introducing Vexless, a serverless vector data management system built on cloud functions that decouples compute and storage for elastic, pay-per-use vector similarity search.

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