---
title: Querying Vectors
description: Run nearest-neighbor vector search inside the ParadeDB index
canonical: https://docs.paradedb.com/documentation/vector/querying
---

<Note>
  This is a beta feature available in versions `0.25.0` and above. See [How
  Vector Search Works](/documentation/vector/overview) for background, and
  [Indexing Vectors](/documentation/indexing/indexing-vectors) to set up the
  index used in these examples.
</Note>

Vector search returns the rows whose embeddings are closest to a query vector. In ParadeDB, this is an `ORDER BY <distance> ... LIMIT k` query over a vector column in the ParadeDB index.

When one of ParadeDB's [search operators](/documentation/filtering) is present at the same level as the `ORDER BY ... LIMIT`, ParadeDB can accelerate the vector search query.

## Unfiltered Nearest Neighbors

When you are not filtering, use `pdb.all()`, which matches every row:

<CodeGroup>
```sql SQL
SELECT id, description
FROM mock_items
WHERE id @@@ pdb.all()
ORDER BY embedding <=> '[1, 2, 3, 4, 5, 6, 7, 8]'
LIMIT 5;
```

```ts Drizzle
// Vector search support for Drizzle is coming very soon.
```

```python Django
# Vector search support for Django is coming very soon.
```

```python SQLAlchemy
# Vector search support for SQLAlchemy is coming very soon.
```

```ruby Rails
# Vector search support for Rails is coming very soon.
```

```cs EF Core
// Vector search support for EF Core is coming very soon.
```

</CodeGroup>

The `<=>` operator computes cosine distance, so this returns the five rows whose embeddings are nearest the query vector `[1, 2, 3, 4, 5, 6, 7, 8]`.

This operator must match the operator class the column was indexed with. Otherwise, ParadeDB cannot use the index to order results and falls back to a slower brute force sort.

| Operator | Distance      | Operator class      |
| -------- | ------------- | ------------------- |
| `<->`    | L2            | `vector_l2_ops`     |
| `<=>`    | Cosine        | `vector_cosine_ops` |
| `<#>`    | Inner product | `vector_ip_ops`     |

## Filtered Nearest Neighbors

To search within a subset of rows, replace `pdb.all()` with a real predicate using any of the [search operators](/documentation/filtering#non-text-fields).
For instance, the following query finds the nearest neighbors among results whose `category` contains the term `footwear`.
Note the lowercase `footwear` — the default tokenizer lowercases terms, so `Footwear` would not match.

<CodeGroup>
```sql SQL
SELECT id, description
FROM mock_items
WHERE category === 'footwear'
ORDER BY embedding <=> '[1, 2, 3, 4, 5, 6, 7, 8]'
LIMIT 5;
```

```ts Drizzle
// Vector search support for Drizzle is coming very soon.
```

```python Django
# Vector search support for Django is coming very soon.
```

```python SQLAlchemy
# Vector search support for SQLAlchemy is coming very soon.
```

```ruby Rails
# Vector search support for Rails is coming very soon.
```

```cs EF Core
// Vector search support for EF Core is coming very soon.
```

</CodeGroup>

<Note>
  Every field you filter on (e.g. `category` above) must also be part of the
  ParadeDB index. Filters on unindexed fields cannot be evaluated by the
  ParadeDB index, forcing Postgres to recheck them afterward and eliminating the
  benefit of combining vector search with [filtering](/documentation/filtering).
</Note>

## Deterministic Results

Rows whose embeddings are equidistant from the query vector are returned in an arbitrary order. When the `LIMIT` cuts through such a group, which of the tied rows come back can change between runs. Add a tiebreaker column after the distance to make the order stable:

```sql
SELECT id, description
FROM mock_items
WHERE id @@@ pdb.all()
ORDER BY embedding <=> '[1, 2, 3, 4, 5, 6, 7, 8]', id
LIMIT 5;
```

The tiebreaker is applied only within a group of equal distances, so it never changes which rows are nearest. Multiple tiebreakers and `DESC` are both supported, and every tiebreaker column must be in the ParadeDB index to keep the Top K optimization.

## Verifying Pushdown

Use `EXPLAIN` to confirm ParadeDB is accelerating the vector search. Look for a `Custom Scan` with an `Exec Method` of `TopKScanExecState` in the query plan:

```sql
EXPLAIN SELECT id, description
FROM mock_items
WHERE category === 'footwear'
ORDER BY embedding <=> '[1, 2, 3, 4, 5, 6, 7, 8]'
LIMIT 5;
```

<Accordion title="Expected Response">
```csv
                               QUERY PLAN
------------------------------------------------------------------------
 Limit
   ->  Custom Scan (ParadeDB Base Scan) on mock_items
         Table: mock_items
         Index: search_idx
         Exec Method: TopKScanExecState
         Scores: false
            TopK Order By: embedding <=> vector asc
            TopK Limit: 5
         Tantivy Query: {"with_index":{"query":{"term":{"field":"category","value":"footwear"}}}}
```
</Accordion>

Notice that the `category === 'footwear'` filter was pushed down into the same index scan that performs the vector search. Without pushdown, the filter would run as a separate step after the nearest neighbors were fetched.

If you do not see a `TopKScanExecState`, the query fell back to a less efficient sort. This usually means the distance operator does not match the index operator class, the query is missing a `LIMIT`, or an `ORDER BY` or filter field is not indexed.
