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HelixDB is a powerful, open-source graph-vector database built in Rust, designed for intelligent data storage for Retrieval-Augmented Generation (RAG) and AI applications. It combines graph database features with vector search, making it directly relevant to AI and machine learning workflows that require vector data management.
Neo4j is a graph database that has added vector search capabilities, providing unique and effective approaches for retrieval augmented generation (RAG) and other AI applications.
Qdrant is a dedicated vector database and similarity search engine supporting advanced filtering and efficient retrieval, suitable for faceted search and retrieval-augmented generation. It offers self-hosted and cloud deployment options, making it highly relevant for vector search applications.
Trieve provides an all-in-one infrastructure for vector search, recommendations, retrieval-augmented generation (RAG), and analytics, accessible via API for seamless integration.
Arroy is an open-source library for efficient similarity search and management of vector embeddings, useful in vector database systems.
Bleve is an open-source search library with experimental support for vector search, enabling hybrid search and retrieval in applications.
HelixDB is an open-source graph-vector database built in Rust, designed for intelligent data storage for Retrieval-Augmented Generation (RAG) and AI applications. It combines features of graph databases with vector search capabilities, making it suitable for AI and machine learning workflows that require efficient vector data management.
HelixDB is open-source software licensed under the AGPL-3.0 license and is free to use.
open-source, graph-database, vector-search, rag, rust