> ## 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.

# cohere_embed()

> Generate vector embeddings using Cohere's multilingual models

Generate vector embeddings from text using Cohere's enterprise-grade embedding models. Cohere embeddings excel at
multilingual semantic search, clustering, and classification tasks.

## Samples

### Generate an embedding

Create a vector embedding:

```sql theme={"dark"}
SELECT ai.cohere_embed(
    'embed-english-v3.0',
    'PostgreSQL is a powerful database'
);
```

### Specify input type

Optimize embeddings for your use case:

```sql theme={"dark"}
-- For search queries
SELECT ai.cohere_embed(
    'embed-english-v3.0',
    'best database for time-series',
    input_type => 'search_query'
);

-- For documents to be searched
SELECT ai.cohere_embed(
    'embed-english-v3.0',
    'PostgreSQL is a relational database',
    input_type => 'search_document'
);
```

### Store embeddings in a table

Generate and store embeddings for your data:

```sql theme={"dark"}
UPDATE documents
SET embedding = ai.cohere_embed(
    'embed-english-v3.0',
    content,
    input_type => 'search_document'
)
WHERE embedding IS NULL;
```

### Multilingual embeddings

Use multilingual models for non-English content:

```sql theme={"dark"}
SELECT ai.cohere_embed(
    'embed-multilingual-v3.0',
    'La base de datos PostgreSQL es poderosa',
    input_type => 'search_document'
);
```

## Arguments

| Name                   | Type      | Default | Required | Description                                                                      |
| ---------------------- | --------- | ------- | -------- | -------------------------------------------------------------------------------- |
| `model`                | `TEXT`    | -       | ✔        | The Cohere embedding model to use (e.g., `embed-english-v3.0`)                   |
| `input_text`           | `TEXT`    | -       | ✔        | Text to embed                                                                    |
| `api_key`              | `TEXT`    | `NULL`  | ✖        | Cohere API key. If not provided, uses configured secret                          |
| `api_key_name`         | `TEXT`    | `NULL`  | ✖        | Name of the secret containing the API key                                        |
| `input_type`           | `TEXT`    | `NULL`  | ✖        | Type of input: `search_query`, `search_document`, `classification`, `clustering` |
| `truncate_long_inputs` | `TEXT`    | `NULL`  | ✖        | How to handle long inputs: `START`, `END`, `NONE`                                |
| `verbose`              | `BOOLEAN` | `FALSE` | ✖        | Enable verbose logging for debugging                                             |

## Returns

`vector`: A pgvector compatible vector containing the embedding.

## Related functions

* [`cohere_rerank()`][cohere_rerank]: rerank search results for better relevance
* [`openai_embed()`][openai_embed]: alternative with OpenAI models

[cohere_rerank]: /api-reference/pgai/model-calling/cohere/cohere_rerank

[openai_embed]: /api-reference/pgai/model-calling/openai/openai_embed