> ## 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.

# Vector indexes

> Create dimensioned vector indexes and run nearest-neighbor search

<div className="flex flex-wrap gap-2"><Badge color="blue" size="sm">Guide</Badge></div>

Vector indexes rank node or edge embeddings by distance. Every definition requires a
non-zero dimension and a distance metric.

## Supported metrics

* Cosine
* Euclidean
* Manhattan

The indexed value and every query vector must have exactly the declared dimension.

## Create a vector index

<CodeGroup>
  ```rust Rust theme={"languages":{"custom":["languages/helixql.json"]}}
  write_batch()
      .var_as(
          "index",
          g().create_vector_index_nodes(
              "Doc",
              "embedding",
              std::num::NonZeroUsize::new(3).expect("non-zero dimension"),
              VectorDistanceMetric::Cosine,
              None::<&str>,
          ),
      )
      .returning(["index"]);
  ```

  ```ts TypeScript theme={"languages":{"custom":["languages/helixql.json"]}}
  writeBatch()
    .varAs(
      "index",
      g().createVectorIndexNodes(
        "Doc",
        "embedding",
        3,
        VectorDistanceMetric.Cosine,
        null,
      ),
    )
    .returning(["index"]);
  ```

  ```go Go theme={"languages":{"custom":["languages/helixql.json"]}}
  helix.WriteQuery("create_doc_vector_index").
  	VarAs(
  		"index",
  		helix.G().CreateVectorIndexNodes(
  			"Doc",
  			"embedding",
  			3,
  			helix.VectorDistanceCosine,
  		),
  	).
  	Returning("index")
  ```

  ```python Python theme={"languages":{"custom":["languages/helixql.json"]}}
  (
      write_batch()
      .var_as(
          "index",
          g().create_vector_index_nodes(
              "Doc",
              "embedding",
              3,
              VectorDistanceMetric.COSINE,
          ),
      )
      .returning(["index"])
  )
  ```

  ```json JSON theme={"languages":{"custom":["languages/helixql.json"]}}
  {
    "request_type": "write",
    "query_name": "create_doc_vector_index",
    "query": {
      "write": {
        "entries": [{
          "query": {
            "name": "index",
            "root": {
              "create_index": {
                "spec": {
                  "node_vector": {
                    "label": "Doc",
                    "property": "embedding",
                    "dimension": 3,
                    "metric": "cosine"
                  }
                },
                "if_not_exists": true
              }
            }
          }
        }],
        "returns": ["index"]
      }
    }
  }
  ```
</CodeGroup>

Pass a tenant property as the final argument to partition the index.

## Search

Start with the SDK's vector-search operation for label `Doc`, property `embedding`,
a query vector, and a result limit of `10`. `$distance` is available on the hit
stream; project it before traversing away from the hit if it must remain in the
response.

## Operational notes

* Creation returns before the backfill necessarily finishes.
* Malformed source vectors can block the operation.
* A new generation remains hidden until validation and activation succeed.
* Dropping an index is also a durable lifecycle operation.

## Next steps

<CardGroup cols={2}>
  <Card title="Project search results" icon="table-columns" href="/database/helix-db/query-guides/projections">
    Preserve ranked hit metadata before continuing a traversal.
  </Card>

  <Card title="Vector prefiltering" icon="filter" href="/database/helix-db/query-guides/filtering#vector-prefiltering">
    Rank only an exact traversal-defined candidate set.
  </Card>

  <Card title="Troubleshoot index operations" icon="wrench" href="/database/helix-cloud/operate/troubleshooting#index-operation-is-blocked">
    Resolve blocked builds and lifecycle errors.
  </Card>
</CardGroup>