> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ag2.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# autogen.agentchat.contrib.capabilities.transforms.TextMessageCompressor

<h2 id="autogen.agentchat.contrib.capabilities.transforms.TextMessageCompressor" class="doc doc-heading">
  <code class="doc-symbol doc-symbol-heading doc-symbol-class" />

  <span class="doc doc-object-name doc-class-name">TextMessageCompressor</span>
</h2>

```python theme={null}
TextMessageCompressor(
    text_compressor: TextCompressor | None = None,
    min_tokens: int | None = None,
    compression_params: dict = {},
    cache: AbstractCache | None = None,
    filter_dict: dict[str, Any] | None = None,
    exclude_filter: bool = True
)
```

A transform for compressing text messages in a conversation history.<br />It uses a specified text compression method to reduce the token count of messages, which can lead to more efficient
processing and response generation by downstream models.<br />

<b>Parameters:</b>

| Name                 | Description                                                                                                                                                 |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `text_compressor`    | **Type:** [TextCompressor](/docs/api-reference/autogen/agentchat/contrib/capabilities/text_compressors/TextCompressor) \| None<br /><br />**Default:** None |
| `min_tokens`         | **Type:** int \| None<br /><br />**Default:** None                                                                                                          |
| `compression_params` | **Type:** dict<br /><br />**Default:** \{}                                                                                                                  |
| `cache`              | **Type:** [AbstractCache](/docs/api-reference/autogen/cache/AbstractCache) \| None<br /><br />**Default:** None                                             |
| `filter_dict`        | **Type:** dict\[str, typing.Any] \| None<br /><br />**Default:** None                                                                                       |
| `exclude_filter`     | **Type:** bool<br /><br />**Default:** True                                                                                                                 |

### Instance Methods

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### apply\_transform

```python theme={null}
apply_transform(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]
```

Applies compression to messages in a conversation history based on the specified configuration.<br />The function processes each message according to the `compression_args` and `min_tokens` settings, applying
the specified compression configuration and returning a new list of messages with reduced token counts
where possible.<br />

<b>Parameters:</b>

| Name       | Description                                                                                         |
| ---------- | --------------------------------------------------------------------------------------------------- |
| `messages` | A list of message dictionaries to be compressed.<br /><br />**Type:** list\[dict\[str, typing.Any]] |

<b>Returns:</b>

| Type                          | Description                                                                                                           |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| list\[dict\[str, typing.Any]] | List\[Dict]: A list of dictionaries with the message content compressed according to the configured method and scope. |

<br />

<code class="doc-symbol doc-symbol-heading doc-symbol-method" />

#### get\_logs

```python theme={null}
get_logs(
    self,
    pre_transform_messages: list[dict[str, Any]],
    post_transform_messages: list[dict[str, Any]]
) -> tuple[str, bool]
```

<b>Parameters:</b>

| Name                      | Description                             |
| ------------------------- | --------------------------------------- |
| `pre_transform_messages`  | **Type:** list\[dict\[str, typing.Any]] |
| `post_transform_messages` | **Type:** list\[dict\[str, typing.Any]] |

<br />