# Graph Database

> Browse 18 items tagged with Graph Database.

## Items

- [FalkorDB GraphRAG](https://vectordb.ever.works/th/items/falkordb-graphrag) — A unified knowledge graph and vector database solution built on Redis that seamlessly integrates graph traversal and vector similarity search for building advanced GenAI applications with both relational reasoning and semantic search capabilities.
- [ApertureDB](https://vectordb.ever.works/th/items/aperturedb) — Graph-vector DB for multimodal data (images/videos/docs/embeddings) with hybrid vector similarity + graph traversal + metadata/keyword filtering. Enables complex multimodal RAG queries. Combines FAISS vectors with graphs unlike pure vector DBs like Qdrant.
- [ArangoDB](https://vectordb.ever.works/th/items/arangodb) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports AQL queries with HNSW vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [HelixDB](https://vectordb.ever.works/th/items/helixdb) — Open-source graph-enhanced vector database built in Rust enabling hybrid graph+vector search for KG-RAG applications. Supports graph queries for knowledge graph traversal combined with vector similarity search. Unlike pure vector databases, it natively models relationships for multi-hop reasoning and connected data retrieval.
- [HugeGraph](https://vectordb.ever.works/th/items/hugegraph) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports graph queries with HNSW/DiskANN vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [Kuzu](https://vectordb.ever.works/th/items/kuzu) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports Cypher queries with HNSW vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [Memgraph](https://vectordb.ever.works/th/items/memgraph) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports Cypher queries with HNSW vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [Neo4j](https://vectordb.ever.works/th/items/neo4j) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports Cypher queries with HNSW vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [ruvector-graph](https://vectordb.ever.works/th/items/ruvector-graph) — Graph-enhanced vector database enabling hybrid graph+vector search for KG RAG applications. Supports Cypher queries with HNSW vector indexes for efficient multi-hop retrieval over knowledge graphs. Unlike pure vector databases like Pinecone, it natively models relationships for superior connected data reasoning and traversal.
- [RuVector](https://vectordb.ever.works/th/items/ruvector) — Self-optimizing on-device vector database with HNSW, graph RAG, and WASM deployment for low-latency edge AI ops across browsers/IoT/mobile. Supports real-time self-learning retrieval; lighter and offline vs cloud Qdrant.
- [GraphRAG](https://vectordb.ever.works/th/items/graphrag) — Retrieval-Augmented Generation approach that combines graph databases with vector search for enhanced context retrieval. Uses graph structures to capture relationships between entities while leveraging vector embeddings for semantic search.
- [Neo4j Vector Search](https://vectordb.ever.works/th/items/neo4j-graph-vector-search) — Vector similarity search in Neo4j enabling GraphRAG by combining knowledge graphs with vector embeddings.
- [CozoDB](https://vectordb.ever.works/th/items/cozodb) — General-purpose, transactional, relational-graph-vector database that uses Datalog for queries. Embeddable but capable of handling large amounts of data and concurrency with HNSW indices for high-performance vector similarity searches.
- [NebulaGraph](https://vectordb.ever.works/th/items/nebulagraph) — Open-source distributed graph database designed for super large-scale graphs with billions of vertices and trillions of edges. Outperforms Neo4j on larger datasets while providing graph database capabilities for AI applications.
- [Neo4j Vector Index](https://vectordb.ever.works/th/items/neo4j-vector-index) — Vector search capabilities in Neo4j graph database using HNSW indexing. Enables combining knowledge graphs with semantic similarity search for hybrid retrieval that leverages both graph relationships and vector embeddings.
- [AllegroGraph](https://vectordb.ever.works/th/items/allegrograph) — A database that incorporates neuro-symbolic AI and offers a managed service (AllegroGraph Cloud) for neuro-symbolic AI knowledge graphs, indicating its relevance to advanced AI applications, likely including vector capabilities.
- [Neo4j Vector Search](https://vectordb.ever.works/th/items/neo4j-vector-search) — An enhancement to the Neo4j graph database providing vector search capabilities through dedicated indexes.
- [Graph-based Methods](https://vectordb.ever.works/th/items/graph-based-methods) — A category of vector database solutions and algorithms leveraging graph-based approaches for efficient similarity search and vector indexing, which are core to many vector database implementations in AI applications.

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