---
title: Create an Index
---

Search over massive collections of vectors can be slow. HNSW (Hierarchical Navigable Small World) is an algorithm that significantly
accelerates vector search times.

## Basic Usage

An HNSW index can be created over any column with the `vector` or `sparsevec` type.

```sql
CREATE INDEX ON <schema_name>.<table_name>
USING hnsw (<column_name> <distance_metric>);
```

<ParamField body="table_name" required>
  The name of the table being indexed.
</ParamField>
<ParamField body="column_name" required>
  The name of the column being indexed. Must be of type `vector`.
</ParamField>
<ParamField body="distance_metric" required>
  The distance metric used for measuring similarity between two vectors. For the
  `vector` data type, use `vector_l2_ops` for L2 distance, `vector_ip_ops` for
  inner product, and `vector_cosine_ops` for cosine distance. For the
  `sparsevec` data type, use `sparsevec_l2_ops` for L2 distance,
  `sparsevec_ip_ops` for inner product, and `sparsevec_cosine_ops` for cosine
  distance.
</ParamField>
<ParamField body="schema_name">
  The name of the schema, or namespace, of the table. If not provided, the
  search path is used as a default.
</ParamField>

## Index Options

The following example demonstrates how to pass options when creating the HNSW index:

```sql
CREATE INDEX ON <schema_name>.<table_name>
USING hnsw (<column_name> <distance_metric>)
WITH (m = 16, ef_construction = 64);
```

<ParamField body="m" default={16}>
  The maximum number of connections per layer. A higher value increases recall
  but also increases index size and construction time.
</ParamField>
<ParamField body="ef_construction" default={64}>
  A higher value creates a higher quality graph, which increases recall but also
  construction time.
</ParamField>
