SemaDB

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.

Orama is a lightweight search engine that supports vector and hybrid search functionalities, suitable for browser, server, or edge environments.

Solr is a mature open-source search engine that has incorporated vector search capabilities, making it relevant for enterprises looking to implement vector-based search alongside traditional keyword search.

Typesense is an open-source search engine that supports hybrid search, including vector search capabilities, providing an alternative to proprietary vector search solutions.

Lightweight npm package providing an embedded vector database for Node.js applications. Offers vector similarity search with HNSW, BM25 full-text search, hybrid search using weighted fusion or Reciprocal Rank Fusion (RRF), multi-namespace support, CRUD operations, metadata filtering, concurrency safety, and persistent storage to disk. Designed for RAG pipelines and semantic search use cases.

A self-learning, self-optimizing vector database with graph intelligence, local AI runtime, and PostgreSQL integration. It improves search quality over time using GNNs that learn from queries and feedback, supports hybrid search, Graph RAG, DiskANN, and deploys as a single file anywhere including browsers and edge devices. Open-source under MIT license, free forever.