# vector-database

Production RAG engine with hybrid search and knowledge graphs

R2R is a production-grade RAG engine from SciPhi AI that combines hybrid search with knowledge graph extraction and agentic retrieval capabilities. It provides a complete pipeline from document ingestion through retrieval and generation, supporting vector, keyword, and graph-based search strategies. The managed API and self-hosted options make it accessible for both rapid prototyping and production deployments requiring advanced retrieval beyond simple vector similarity.

RAG-based document QA with multi-user support and agent reasoning

Kotaemon is an open-source RAG-powered document question-answering interface backed by Cinnamon AI. It supports multi-user workspaces with access controls, advanced retrieval pipelines including hybrid search and knowledge graph extraction, and agentic reasoning for complex multi-step queries. The web UI handles PDFs, Office documents, and images with citations pointing to exact source passages, making it suitable for both individual research and team knowledge management.

Multi-model database for the AI era — document, graph, vector, and relational in one

SurrealDB is a multi-model database that natively combines document, graph, relational, key-value, and vector storage in a single engine. It eliminates the need for separate databases by handling structured queries, graph traversals, full-text search, and vector similarity in one SQL-like query language called SurrealQL. Built in Rust for performance and safety, it supports real-time subscriptions, row-level permissions, and embedded or distributed deployment modes.

Serverless vector and full-text search on object storage

turbopuffer is a serverless vector and full-text search engine built on object storage that delivers 10x lower costs than traditional vector databases. Used by Anthropic, Cursor, Notion, and Atlassian for production search workloads. Manages 2+ trillion vectors across 8+ petabytes with automatic scaling and no infrastructure management. Funded by Thrive Capital.

Hybrid search and ML ranking engine at scale

Vespa is an open-source serving engine with 6K+ GitHub stars for hybrid search combining vector similarity, BM25 text ranking, and structured filtering in a single query. Built by Yahoo for web-scale, it handles billions of documents with millisecond latency. Features real-time indexing, ML model serving, tensor computation, and ACID-compliant writes. Supports custom ranking models, query federation, and geographic search. Used for recommendation systems, personalization, and RAG.

Vector similarity search for PostgreSQL

pgvector is an open-source PostgreSQL extension with 14K+ GitHub stars adding vector similarity search to your existing Postgres database. Store embeddings alongside relational data, perform exact and approximate nearest neighbor search using L2, inner product, cosine, and L1 metrics. Supports HNSW and IVFFlat indexes for fast similarity queries at scale. Eliminates the need for a separate vector database by bringing vector capabilities into existing PostgreSQL infrastructure.

Open-source embedding database — the AI-native way to store and query embeddings.

Chroma is an open-source embedding database designed for simplicity and developer experience. Runs in-memory, as a Python library, or as a client-server deployment. Popular for prototyping RAG applications, local development, and lightweight vector search. Integrates natively with LangChain, LlamaIndex, and OpenAI.

Fully managed vector database built for AI applications at production scale.

Pinecone is the leading managed vector database designed for high-performance similarity search at scale. Purpose-built for AI applications including RAG, recommendation systems, and semantic search. Offers serverless and pod-based architectures with automatic scaling, filtering, and namespacing. No infrastructure management required.

Open-source search engine — fast, typo-tolerant, easy to use.

Typesense is an open-source, typo-tolerant search engine optimized for instant search experiences. Written in C++ for maximum performance. Features built-in vector search for semantic/hybrid queries, geo-search, faceting, and curation. Popular for e-commerce search, documentation sites, and SaaS applications.

High-performance vector database written in Rust for similarity search at scale.

Qdrant is a high-performance vector similarity search engine and database written in Rust. Designed for production-grade AI applications with advanced filtering, payload indexing, and distributed deployment. Supports billion-scale vector collections with sub-second query times. Popular choice for RAG, recommendation systems, and anomaly detection.

Open-source vector database for AI-native applications and semantic search.

Weaviate is an open-source vector database purpose-built for AI applications. Supports vector, keyword, and hybrid search with built-in vectorization modules for OpenAI, Cohere, Hugging Face, and more. Used for RAG pipelines, semantic search, recommendation engines, and multimodal search. Written in Go for high performance.