# Semantic Vector Search

Use `lakebase_vector` for approximate nearest-neighbor retrieval over embeddings. It retains pgvector's vector types, distance operators, and query syntax; the index access method is `lakebase_ann`.

## Contents

- [Create the extension](#create-the-extension) — enable `lakebase_vector` and its `pgvector` dependency
- [Prepare embeddings](#prepare-embeddings) — define the vector column and keep embedding dimensions consistent
- [Build the index](#build-the-index) — match the distance metric, operator class, and query operator
- [Tune the index](#tune-the-index) — configure index-build options and concurrent index management
- [Query](#query) — rank by vector distance or filter by a similarity radius
- [Tune search](#tune-search) — inspect the index and tune recall against latency
  - [Use prefilter selectively](#use-prefilter-selectively) — apply selective filters before ANN scoring

## Create the Extension

Lakebase Search requires Postgres 16 or later. Enable the extension before creating vector columns or indexes:

```sql
CREATE EXTENSION IF NOT EXISTS lakebase_vector CASCADE;
```

`lakebase_vector` installs `pgvector` through `CASCADE`. It relies on a preloaded library that Neon enables by default; if the project customized its preloaded-library list, confirm the library remains enabled.

## Prepare Embeddings

Use any embedding provider whose vector dimensions and distance metric match the schema and index:

```sql
CREATE TABLE documents (
  id bigint GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
  title text NOT NULL,
  body text NOT NULL,
  embedding vector(1536)
);
```

Replace `1536` with the embedding model's dimension. Generate stored-document and query embeddings with the same model and preprocessing. Keep embedding generation outside SQL unless the architecture already provides an in-database embedding function.

## Build the Index

Choose the operator class and query operator as a matched pair:

| Metric | Common use | Operator class | Distance operator |
| --- | --- | --- | --- |
| Cosine | Most text embeddings | `vector_cosine_ops` | `<=>` |
| L2 / Euclidean | Absolute distance matters; vectors do not need normalization | `vector_l2_ops` | `<->` |
| Inner product | Unit-normalized vectors; matches cosine for unit vectors | `vector_ip_ops` | `<#>` |

```sql
CREATE INDEX documents_embedding_ann ON documents
  USING lakebase_ann (embedding vector_cosine_ops);
```

## Tune the Index

The default index options suit most workloads:

- `build_mode = 'standard'` balances recall and index build time. Use `quality` for better recall when a longer build is acceptable.
- `lists = 'auto'` chooses the IVF partition layout from the number of indexed vectors. Choose between `auto` and a manual value case by case: test both on the target dataset and use the value that better meets recall and performance targets.

To prioritize recall over index build time:

```sql
CREATE INDEX documents_embedding_ann_quality ON documents
  USING lakebase_ann (embedding vector_cosine_ops)
  WITH (build_mode = 'quality');
```

To override the automatic partition layout instead:

```sql
CREATE INDEX documents_embedding_ann_lists ON documents
  USING lakebase_ann (embedding vector_cosine_ops)
  WITH (lists = '1024');
```

For a large table, use `CREATE INDEX CONCURRENTLY` to avoid locking out writes while creating the index. For a frequently changing table, periodically use `REINDEX INDEX CONCURRENTLY` to rebuild the index with minimal write locking.

## Query

Generate the query embedding with the same model and preprocessing used for stored documents, then bind it as a parameter:

```sql
SELECT id, title, embedding <=> $1::vector AS distance
FROM documents
ORDER BY distance
LIMIT $2;
```

Distance sorts ascending: a smaller value is a closer match. Keep the query operator consistent with the index operator class.

To filter by a similarity radius, use the matching boolean range operator in `WHERE` and the distance operator in `ORDER BY`:

```sql
SELECT id, title
FROM documents
WHERE embedding <<=>> sphere($1::vector, 0.5)
ORDER BY embedding <=> $1::vector
LIMIT $2;
```

The cosine range operator `<<=>>` returns a boolean; do not use it as the ranking expression.

## Tune Search

Inspect the index before overriding defaults:

```sql
SELECT lakebase_ann_index_info('documents_embedding_ann');
```

This reports `lists`, `default_probes`, and `default_epsilon`. Small datasets use exact flat search before IVF lists are built. In that state, `lists` and `default_probes` are empty. Leave `lakebase_ann.probes` set to its default of `'auto'`; `lakebase_ann.epsilon` still controls full-precision reranking during flat search.

For an IVF index, `lakebase_ann.probes` controls how many partitions are searched at each level. Higher values generally improve recall at the cost of speed. Its default is `'auto'`. When `lists` is not empty, the shape of `probes` must match the shape of `lists`: use one value for a one-level index or two comma-separated values for a two-level index. At each level, the `probes` value must be no larger than the corresponding `lists` value. A mismatched or out-of-range value causes an error.

`lakebase_ann.epsilon` controls how many candidates are reranked using full-precision distances. Higher values rerank more candidates and take longer. Its default is `'auto'`, which works well for most workloads.

### Use Prefilter Selectively

By default, PostgreSQL applies non-vector filters after the ANN index returns candidate rows. Enable prefilter when a filter is cheap to evaluate and removes most rows. Leave it off for loose or expensive filters.

```sql
BEGIN;
SET LOCAL lakebase_ann.prefilter = on;

SELECT id, title
FROM documents
WHERE id % 100 = 0
ORDER BY embedding <=> $1::vector
LIMIT $2;
COMMIT;
```

Start with `probes` and `epsilon` set to `'auto'`. Benchmark manual probe values against representative query embeddings and choose the smallest values that satisfy recall and tail-latency targets. Keep session settings and the query in the same transaction when using a connection pool or stateless driver.

Source: [`lakebase_vector` documentation](https://neon.com/docs/extensions/lakebase-vector).