> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# destination_column()

> Store embeddings directly in the source table as a new column

Store embeddings directly in the source table as a new column. This approach requires a one-to-one relationship
between source data and embeddings, so chunking must be disabled. Ideal when source text is short or chunking is done
upstream.

Key features for this function are:

* Adds vector column directly to source table
* No separate view created
* Requires `chunking_none()` (no chunking)
* Exactly one embedding per row
* Simpler schema with fewer objects

## Workflow

1. Application inserts data with NULL in embedding column
2. Vectorizer detects the NULL value
3. Vectorizer generates embedding
4. Vectorizer updates row with embedding value

## Samples

### Basic usage

Store embeddings in a column for pre-chunked data:

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'product_descriptions'::regclass,
    destination => ai.destination_column('description_embedding'),
    loading => ai.loading_column('description'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768),
    chunking => ai.chunking_none()  -- Required for column destination
);
```

### With specific embedding model

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'short_text_data'::regclass,
    destination => ai.destination_column('text_embedding'),
    loading => ai.loading_column('text'),
    embedding => ai.embedding_ollama('nomic-embed-text', 768),
    chunking => ai.chunking_none()
);
```

## Arguments

| Name               | Type   | Default | Required | Description                                                          |
| ------------------ | ------ | ------- | -------- | -------------------------------------------------------------------- |
| `embedding_column` | `NAME` | -       | ✔        | Name of the column to add to the source table for storing embeddings |

## Returns

A JSON configuration object for use in [`create_vectorizer()`][create_vectorizer].

## Important notes

* **Chunking must be disabled**: Use `chunking => ai.chunking_none()` when using column destination
* **One embedding per row**: This approach cannot handle multiple chunks per source row
* **Best for short text**: Ideal when text is already chunked or naturally short (\< 512 tokens)

## Related functions

* [`destination_table()`][destination_table]: alternative approach with separate embeddings table
* [`chunking_none()`][chunking_none]: required chunking configuration for column destination
* [`create_vectorizer()`][create_vectorizer]: main function using this configuration

[chunking_none]: /api-reference/pgai/vectorizer/chunking_none

[create_vectorizer]: /api-reference/pgai/vectorizer/create_vectorizer

[destination_table]: /api-reference/pgai/vectorizer/destination_table