Vector Embeddings in Graph Databases: Overview and Engine Capabilities

Table of Contents

• 1. What are vector embeddings as a data type

• 2. Index types and engineering trade-offs

• 3. Operations enabled by embeddings and impact on GraphRAG

• 4. Vendors overview

  • 4.1 Neo4j

  • 4.2 AWS Neptune

  • 4.3 Memgraph

  • 4.4 Ultipa

  • 4.5 TigerGraph

  • 4.6 Dgraph

  • 4.7 ArangoDB

  • 4.8 GraphDB (Ontotext)

  • 4.9 JanusGraph

  • 4.10 OrientDB

• 5. Summary and outlook

• 6. Summary table

• 7. Sources (URLs)

1. What are vector embeddings as a data type

Vector embeddings are numeric arrays that record the meaning or features of an object in a high-dimensional space. Instead of being scalar values like integers or strings, embeddings are multidimensional floating-point vectors (commonly float32) produced by machine learning models (text encoders, image encoders, graph embedding methods). The distance or angle between two vectors — measured with metrics such as cosine similarity, dot product, or Euclidean (L2) distance — expresses semantic or structural similarity between the underlying objects.

Embeddings vary in length (typical sizes: 64, 128, 256, 512, 1024, 1536, 4096) depending on the model and tradeoffs between capacity and runtime. Because they are vectors, the meaningful database operations are similarity/distance calculations and nearest-neighbour retrieval rather than ordering, grouping, or exact equality.

From an engineering standpoint, storing and searching millions of high-dimensional vectors requires specialised indexes and attention to memory, update cost, and accuracy tradeoffs. Embeddings are therefore treated as a distinct data type that demands vector-aware storage and query primitives (index creation, vector fields, k-NN search, ANN tuning).

2. Index types and engineering trade-offs

Searching vectors at scale is solved by approximate nearest neighbour (ANN) structures rather than linear scans. Common index types and their engineering trade-offs:

• HNSW (Hierarchical Navigable Small World): a graph-based ANN algorithm with excellent recall/latency tradeoffs and support for incremental inserts. HNSW typically uses more memory but gives fast queries and supports dynamic datasets.

• IVF / PQ (Inverted File + Product Quantisation): partitions vectors into clusters and compresses them, enabling very large collections with lower memory. Usually requires batch reindexing and tuning for effective recall.

• Brute-force / exact: linear scan with exact results — only practical for small datasets or as a fallback.

• Hybrid designs: combine pre-filtering by metadata or graph constraints with ANN search on a filtered subset. This reduces ANN cost and helps integrate vector search with graph predicate constraints.

• Key engineering tradeoffs you must evaluate:

• Accuracy vs latency: ANN improves latency but is approximate. Measure recall/latency curves.

• Memory vs throughput: higher recall usually needs larger in-memory structures.

• Update semantics: HNSW supports incremental inserts; PQ/IVF often require rebuilds. Choose based on update frequency.

• Operational considerations: index persistence, backup/restore, replication, and how indexes behave during node failures or cluster scaling. These differ between vendors.

3. Operations enabled by embeddings and impact on GraphRAG

Embeddings enable operations beyond classic graph traversal and property filtering. Relevant operations and what they mean for GraphRAG (graph retrieval-augmented generation):

• Similarity search (k-NN / top-k): find nodes or documents whose embeddings are nearest to a query vector — the bread-and-butter operation for semantic retrieval in RAG.

• Hybrid retrieval: combine vector similarity with graph filters and traversals; for example, find the top-k semantically similar nodes that are within two hops of a given entity or satisfy a domain predicate. This is crucial for contextually accurate RAG.

• Clustering & topic grouping: cluster nodes in embedding space to discover latent topics or communities complementary to graph structure. Useful for index sharding, candidate pre-filtering, or analytics.

• Classification / nearest-centroid classification: assign labels by comparing node embeddings to class centroids. Often used in pipelines where fast inference is needed.

• Ranking & re-scoring: use embedding similarity as a signal alongside graph metrics (degree, centrality) to rank results for RAG prompt assembly. Combining similarity with graph-based relevance helps reduce hallucinations and increases factual grounding.

• Anomaly detection: outliers in embedding space can indicate anomalous nodes or unexpected content.

• Operational pattern for GraphRAG: use the vector index as the recall layer (retrieve semantically relevant candidates), then use graph traversals and schema constraints to filter/enrich results before assembling RAG prompts — this hybrid sequence is the recommended pattern for production RAG systems.

4. Vendors overview

4.1 Neo4j

• Neo4j provides native vector indexes (node vector indexes) and integrated vector search; vector indexes reached general availability in Neo4j 5.13 and the implementation uses HNSW for ANN.

• Embeddings are stored as node properties and can be queried with k-NN semantics; Cypher enables hybrid queries that combine vector similarity with relationship traversals and property filters.

• Neo4j’s Graph Data Science (GDS) and GenAI ecosystem make it easy to combine embeddings with graph analytics (clustering, community detection) and ML pipelines.

4.2 AWS Neptune

• Neptune Analytics supports vector similarity search and vector indexes for Neptune Analytics graphs; it integrates with Neptune ML, GraphStorm, and other AWS ML services.

• Neptune Analytics supports only one vector index per graph.

• Neptune is attractive for organisations embedded in AWS because it reduces the need for an external vector DB, though its index creation/update model and limits should be reviewed for dynamic/online update workloads.

4.3 Memgraph

• Memgraph provides vector search capabilities and documentation showing vector search examples and demos; vector indices are available and integrations with Cypher-style queries enable hybrid retrieval.

• Memgraph’s design emphasises in-memory performance and real-time operations; check their docs for index isolation semantics and transaction details when mixing vector and transactional graph updates.

4.4 Ultipa

• Ultipa documents and announcements indicate native vector index support and vector server architecture; their GQL supports creating vector indexes and running vector queries.

• They provide operational guidance for deploying vector servers alongside the graph engine for indexing and search at scale.

4.5 TigerGraph

• TigerGraph has published work (TigerVector) and recent papers indicating that TigerGraph integrated vector support and ANN in GSQL.

• If you consider TigerGraph, validate the exact version and deployment model (MPP index framework) for your scale and hybrid query needs.

4.6 Dgraph

• Dgraph has added vector embedding capabilities and community content and guides (examples showing HNSW indexing like @index(hnsw) in schema).

• Confirm the Dgraph version and @index(hnsw) syntax in the official Dgraph docs for production features.

4.7 ArangoDB

• ArangoDB (multi-model) includes vector search functions in AQL and vector index support in recent versions (vector search functions such as APPROX_NEAR_COSINE() and APPROX_NEAR_L2()).

• ArangoDB docs and blog posts show examples combining vector search with graph traversals and LangChain integrations.

4.8 GraphDB (Ontotext)

• GraphDB (Ontotext) is RDF/ontology focused. It provides a similarity plugin based on statistical Random Indexing and has a Retrieval Connector that exports RDF content to external vector stores for embedding-based searches.

• GraphDB itself is not embedding-native in the modern sense (i.e., it does not let you plug arbitrary ML embeddings into a native vector index).

4.9 JanusGraph

• JanusGraph does not provide a native vector data type or native ANN index. JanusGraph supports external index backends (Elasticsearch, Solr, Lucene).

• Teams commonly pair JanusGraph with an external vector search system (Elasticsearch k-NN plugin, Pinecone, Milvus, FAISS) to implement semantic search.

4.10 OrientDB

• OrientDB is multi-model and flexible but does not provide built-in vector/ANN indexing.

• If you want embeddings with OrientDB you will typically run an external vector index/search engine and store references in OrientDB.

5. Summary and outlook

Vector embeddings are a distinct, now-critical data type for knowledge systems. Vendors are converging on two patterns: (1) native vector support (Neo4j, Neptune, Memgraph, Ultipa, TigerGraph, ArangoDB, and Dgraph in their newer versions) that allows compact hybrid queries and simpler ops; and (2) hybrid approaches where the graph stores pointers and a separate vector DB handles ANN queries (common for JanusGraph, OrientDB, GraphDB workflows). For production GraphRAG, prefer an engine that either has native vector+hybrid query support or an operationally mature, well-documented external integration pattern.

6. Summary table

7. Sources

• Neo4j vector indexes (Cypher manual): https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/

• Neo4j developer guide: https://neo4j.com/developer/genai-ecosystem/vector-search/

• Neo4j blog vector search: https://neo4j.com/blog/genai/vector-search-deeper-insights/

• AWS Neptune vector similarity: https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-similarity.html

• AWS Neptune vector indexing: https://docs.aws.amazon.com/neptune-analytics/latest/userguide/vector-index.html

• Memgraph vector search docs: https://memgraph.com/docs/querying/vector-search

• Memgraph blog vector demo: https://memgraph.com/blog/vector-search-memgraph-knowledge-graph-demo

• Ultipa vector index docs: https://www.ultipa.com/docs/gql/vector-index
  Ultipa announcement: https://www.ultipa.com/article/technical/ultipa-now-supports-vector-search

• TigerGraph TigerVector paper: https://arxiv.org/abs/2501.11216

• Dgraph community vector intro: https://discuss.dgraph.io/t/intro-to-dgraph-vector-embeddings/19598

• ArangoDB vector functions: https://docs.arangodb.com/3.13/aql/functions/vector/
  ArangoDB vector blog: https://arangodb.com/2024/11/vector-search-in-arangodb-practical-insights-and-hands-on-examples/

• GraphDB semantic similarity: https://graphdb.ontotext.com/documentation/11.1/semantic-similarity-searches.html

• GraphDB retrieval connector: https://graphdb.ontotext.com/documentation/11.1/retrieval-graphdb-connector.html

• JanusGraph Elasticsearch backend: https://docs.janusgraph.org/index-backend/elasticsearch/

• OrientDB docs: https://orientdb.dev/ and https://github.com/orientechnologies/orientdb-docs