Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.helix-db.com/llms.txt

Use this file to discover all available pages before exploring further.

For the complete documentation index optimized for AI agents, see llms.txt.
Vector indexes enable approximate nearest neighbor (ANN) search on numeric array properties. Helix supports vector indexes on both nodes and edges. Values are normalized to float32 for indexing, and supported distance metrics are cosine, euclidean, and manhattan. The query vector must match the configured index dimension exactly. Vector search is approximate by design, trading a small amount of recall for significantly faster search over large datasets. The third argument to the index-creation builders is an optional tenant property name; pass None::<&str> for a global index, or see Multi-Tenancy for the partitioned variant. The DSL fragments below are bare traversals. To execute them, wrap each one in a read_batch() or write_batch() route as shown in Querying.

​
Vector Index — Nodes

Declare the index. The second argument is the property that holds the embedding: 
g().create_vector_index_nodes("Doc", "embedding", None::<&str>)
Insert a node with both the application data and the embedding. PropertyInput::from(Vec<f32>) works through the generic conversion path from any type that PropertyValue accepts: 
g().add_n(
    "Doc",
    vec![
        ("title", PropertyInput::from("Intro to vector search")),
        ("embedding", PropertyInput::from(vec![0.12f32, 0.85, -0.04])),
    ],
)
kNN search returns the top k nearest nodes. Pass None::<PropertyValue> for the tenant argument when the index is not partitioned: 
g().vector_search_nodes(
    "Doc",
    "embedding",
    vec![0.12f32, 0.85, -0.04],
    10,
    None::<PropertyValue>,
)

​
Vector Index — Edges

Edges can carry embeddings too — useful when the relationship itself is what you want to retrieve by similarity (e.g., citation context, relation-typed embeddings). 
g().create_vector_index_edges("CITES", "embedding", None::<&str>)

g().n(NodeRef::param("source"))
    .add_e(
        "CITES",
        NodeRef::param("target"),
        vec![("embedding", PropertyInput::from(vec![0.12f32, 0.85, -0.04]))],
    )

g().vector_search_edges(
    "CITES",
    "embedding",
    vec![0.12f32, 0.85, -0.04],
    10,
    None::<PropertyValue>,
)

​
Parameterized Searches

The plain vector_search_nodes / vector_search_edges builders take a literal Vec<f32> and a usize for k. For routes that receive the query vector and limit from request parameters, use the _with variants. They accept PropertyInput::param(...) for the vector and Expr::param(...) for k: 
g().vector_search_nodes_with(
    "Doc",
    "embedding",
    PropertyInput::param("queryVector"),
    Expr::param("limit"),
    None::<PropertyInput>,
)
The same pattern applies to vector_search_edges_with.