> ## 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_classify_simple()

> Classify text into categories with a simplified response

Classify text into categories using Cohere's models with custom examples. This function returns a simplified table
format with the input text, predicted label, and confidence score.

## Samples

### Classify sentiment

Categorize text as positive or negative:

```sql theme={"dark"}
SELECT *
FROM ai.cohere_classify_simple(
    'embed-english-v3.0',
    ARRAY['I love this product', 'This is terrible', 'Pretty good overall'],
    examples => '[
        {"text": "This is amazing", "label": "positive"},
        {"text": "Absolutely wonderful", "label": "positive"},
        {"text": "This is awful", "label": "negative"},
        {"text": "Terrible experience", "label": "negative"}
    ]'::jsonb
);
```

Returns:

```text theme={"dark"}
       input        | prediction | confidence
--------------------+------------+------------
 I love this product| positive   |       0.98
 This is terrible   | negative   |       0.95
 Pretty good overall| positive   |       0.72
```

### Classify support tickets

Categorize customer inquiries:

```sql theme={"dark"}
SELECT *
FROM ai.cohere_classify_simple(
    'embed-english-v3.0',
    ARRAY[
        'My password is not working',
        'When will my order arrive?',
        'I want to cancel my subscription'
    ],
    examples => '[
        {"text": "Cannot log in", "label": "technical"},
        {"text": "Forgot my password", "label": "technical"},
        {"text": "Where is my package", "label": "shipping"},
        {"text": "Delivery status", "label": "shipping"},
        {"text": "Cancel my account", "label": "billing"},
        {"text": "Refund request", "label": "billing"}
    ]'::jsonb
);
```

### Batch classification

Classify multiple texts at once:

```sql theme={"dark"}
INSERT INTO classified_feedback (feedback_text, category, confidence)
SELECT input, prediction, confidence
FROM ai.cohere_classify_simple(
    'embed-english-v3.0',
    ARRAY(SELECT feedback FROM pending_feedback LIMIT 100),
    examples => (SELECT classification_examples FROM model_config WHERE model = 'feedback')
);
```

## Arguments

| Name                   | Type      | Default | Required | Description                                                             |
| ---------------------- | --------- | ------- | -------- | ----------------------------------------------------------------------- |
| `model`                | `TEXT`    | -       | ✔        | The Cohere model to use (e.g., `embed-english-v3.0`)                    |
| `inputs`               | `TEXT[]`  | -       | ✔        | Array of texts to classify                                              |
| `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                               |
| `examples`             | `JSONB`   | `NULL`  | ✖        | Training examples as array of `{"text": "...", "label": "..."}` objects |
| `truncate_long_inputs` | `TEXT`    | `NULL`  | ✖        | How to handle long inputs: `START`, `END`, `NONE`                       |
| `verbose`              | `BOOLEAN` | `FALSE` | ✖        | Enable verbose logging for debugging                                    |

## Returns

`TABLE`: A table with the following columns:

| Column       | Type     | Description                                      |
| ------------ | -------- | ------------------------------------------------ |
| `input`      | `TEXT`   | The input text that was classified               |
| `prediction` | `TEXT`   | The predicted category label                     |
| `confidence` | `FLOAT8` | Confidence score (0.0 to 1.0) for the prediction |

## Related functions

* [`cohere_classify()`][cohere_classify]: full API response with additional metadata
* [`cohere_embed()`][cohere_embed]: generate embeddings for custom classification

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

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