> ## Documentation Index
> Fetch the complete documentation index at: https://axiom.co/docs/llms.txt
> Use this file to discover all available pages before exploring further.

<AgentInstructions>

## Submitting Feedback

If you encounter incorrect, outdated, or confusing documentation on this page, submit feedback:

POST https://axiom.co/docs/feedback

```json
{
  "path": "/apl/aggregation-function/phrases",
  "feedback": "Description of the issue"
}
```

Only submit feedback when you have something specific and actionable to report.

</AgentInstructions>

# phrases

> This page explains how to use the phrases aggregation function in APL.

The `phrases` aggregation extracts and counts common phrases or word sequences from text fields across a dataset. It analyzes text content to identify frequently occurring phrases, helping you discover patterns, trends, and common topics in your data.

You can use this aggregation to identify common user queries, discover trending topics, extract key phrases from logs, or analyze conversation patterns in AI applications.

## For users of other query languages

If you come from other query languages, this section explains how to adjust your existing queries to achieve the same results in APL.

<AccordionGroup>
  <Accordion title="Splunk SPL users">
    In Splunk SPL, there’s no built-in phrases function, but you might use the `rare` or `top` commands on tokenized text.

    <CodeGroup>
      ```sql Splunk example theme={null}
      | rex field=message "(?<words>\w+)"
      | top words
      ```

      ```kusto APL equivalent theme={null}
      ['sample-http-logs']
      | summarize phrases(uri, 10)
      ```
    </CodeGroup>
  </Accordion>

  <Accordion title="ANSI SQL users">
    In ANSI SQL, you would need complex string manipulation and grouping to extract common phrases.

    <CodeGroup>
      ```sql SQL example theme={null}
      SELECT 
        phrase,
        COUNT(*) as frequency
      FROM (
        SELECT UNNEST(SPLIT(message, ' ')) as phrase
        FROM logs
      )
      GROUP BY phrase
      ORDER BY frequency DESC
      LIMIT 10
      ```

      ```kusto APL equivalent theme={null}
      ['sample-http-logs']
      | summarize phrases(uri, 10)
      ```
    </CodeGroup>
  </Accordion>
</AccordionGroup>

## Usage

### Syntax

```kusto theme={null}
summarize phrases(column, max_phrases)
```

### Parameters

| Name          | Type   | Required | Description                                                    |
| ------------- | ------ | -------- | -------------------------------------------------------------- |
| `column`      | string | Yes      | The column containing text data from which to extract phrases. |
| `max_phrases` | long   | Yes      | The maximum number of top phrases to return.                   |

### Returns

Returns a dynamic array containing the most common phrases found in the specified column, ordered by frequency.

## Example

Extract common phrases from GenAI conversation text to identify trending topics and patterns.

**Query**

```kusto theme={null}
['otel-demo-genai']
| extend conversation_text = genai_concat_contents(['attributes.gen_ai.input.messages'], ' | ')
| summarize common_phrases = phrases(conversation_text, 20)
```

[Run in Playground](https://play.axiom.co/axiom-play-qf1k/query?initForm=%7B%22apl%22%3A%22%5B%27otel-demo-genai%27%5D%20%7C%20extend%20conversation_text%20%3D%20genai_concat_contents\(%5B%27attributes.gen_ai.input.messages%27%5D%2C%20%27%20%7C%20%27\)%20%7C%20summarize%20common_phrases%20%3D%20phrases\(conversation_text%2C%2020\)%22%7D)

**Output**

| Count | common\_phrases              |
| ----- | ---------------------------- |
| 3     | first query                  |
| 3     | cover related future queries |
| 3     | use title case               |

This query identifies the most common phrases in GenAI conversations, helping you discover trending topics and user needs.

## List of related functions

* [make\_list](/apl/aggregation-function/make-list): Creates an array of all values. Use this when you need all occurrences rather than common phrases.
* [make\_set](/apl/aggregation-function/make-set): Creates an array of unique values. Use this for distinct values without frequency analysis.
* [topk](/apl/aggregation-function/topk): Returns top K values by a specific aggregation. Use this for numerical top values rather than phrase extraction.
* [count](/apl/aggregation-function/count): Counts occurrences. Combine with group by for manual phrase counting if you need more control.
* [dcount](/apl/aggregation-function/dcount): Counts distinct values. Use this to understand the variety of phrases before extracting top ones.