> ## Documentation Index
> Fetch the complete documentation index at: https://docs-classic.cognigy.com/llms.txt
> Use this file to discover all available pages before exploring further.
# LLM Entity Extract
[Updated in 2026.20.0](https://docs-classic.cognigy.com/release-notes/2026.20.md)
## Description
> [!WARNING]
>
> As of Cognigy 2026.18.0, the **Temperature** parameter is deprecated. The removal is planned for April 2027. For models that don't support this parameter, activate the **Ignore deprecated parameters** toggle to exclude it from the request to the LLM provider. Otherwise, the request to the LLM provider might fail. If you plan to use this parameter, migrate it to the [Custom Options section](#custom-options) and activate the **Ignore deprecated parameters** toggle. Otherwise, Custom Options might not override it for every provider and model combination.
>
> For example, OpenAI models such as `gpt-5`, `gpt-5.1`, and `gpt-5.2` don't support these parameters. Similar models from other LLM providers might also not support these parameters. Check your LLM provider's documentation for model-specific support.
This Node uses a Large Language Model (LLM) to extract entities, such as product codes, booking codes, or customer IDs, from `input.text` of the Input object.
The Node supports both chat and voice use cases by processing text and transcribed speech inputs.
Before using this Node, set the LLM provider in the [Settings](https://docs-classic.cognigy.com/ai/agents/develop/gen-ai-and-llms/generative-ai.md).
You can configure the Node to either use the default model defined in the Settings or choose a specific configured LLM.
To view the extracted entity in the Interaction Panel, activate [debug mode](https://docs-classic.cognigy.com/insights/explorers/conversations.md#debug-mode).
To output the extracted entity, add a [Say Node](https://docs-classic.cognigy.com/ai/agents/develop/node-reference/basic/say.md) below the LLM Entity Extract Node in the Flow editor. In the **Text** field of the Say Node, use the key you specified in the [Storage Options](#storage-options) section, for example, `{{input.extractedEntity}}`.
## Parameters
| Parameter | Type | Description |
| - | - | - |
| Large Language Model | List | Select a model or use the default one. |
| Entity Name | CognigyScript | The name of the entity to extract. For example, `customerID`. |
| Entity Description | CognigyScript | A sentence that describes the entity. For example, `An alphanumeric string of 6 characters, such as ABC123 or 32G5FD`. |
| Example Input | Text | Examples of text inputs. For example, `My ID is AB54EE, is that ok?`, `That would be ah bee see double 4 three`, `I guess it's 49 A B 8 K`. Alternatively, you can click **Show JSON Editor** and add input examples in the code field. |
| Extracted Entity | CognigyScript | Examples of extracted entities. For example, `AB54EE`, `ABC443`, `49AB8K`. |
> [accordion] Advanced
>
> | Parameter | Type | Description |
> | - | - | - |
> | Ignore deprecated parameters | Toggle | Excludes the deprecated **Temperature** parameter from the request to the LLM provider. Activate this toggle if your model doesn't support this parameter. If you activate this toggle for a model that supports it, the model uses a default value. |
> | Temperature | Indicator | The appropriate sampling temperature for the model. Higher values mean the model will take more risks. |
> | Timeout | Number | The maximum number of milliseconds to wait for a response from the LLM provider. |
> | Response Format | Select | Choose the format for the model's output result. You can select one of the following options:- **None** — No response format is specified. Use this option if the LLM provider doesn't accept the response format you're using, or if you want to use the provider's default format. This option is selected by default.
- **Text** — the model returns messages in text format.
- **JSON Object** — the model returns messages in JSON format. In contrast to the [LLM Prompt](https://docs-classic.cognigy.com/ai/agents/develop/node-reference/service/llm-prompt.md) Node, this Node is already instructed to generate a JSON output when this option is selected. For Amazon Bedrock and Anthropic models, you can [set the JSON structure](#custom-options) with the `output_config` parameter in **Custom Options**. Note that not all LLMs support this format, which may cause model calls to fail. For more information, refer to the LLM provider's API documentation.
|
> [accordion] Custom Options
>
> These options let you pass parameters that this Node doesn't expose, or override existing configurations.
>
> | Parameter | Type | Description |
> | - | - | - |
> | Custom Model Options | JSON | Sets custom parameters for the LLM configuration, such as `temperature`, `top_k`, or `seed`. These parameters override the ones set in this Node. For the full list of parameters that your model supports, refer to the LLM provider's API documentation, for example, [OpenAI](https://platform.openai.com/docs/api-reference/chat/create) or [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/). Example: `{ "seed": 42, "temperature": 0.2 }` |
> | Custom Request Options | JSON | Sets custom parameters for the request sent to the LLM provider, such as HTTP headers. These parameters override the ones set in this Node. For more information, refer to the LLM provider's API documentation. Example: `{ "headers": { "Authorization": "Bearer mytoken" } }` |
> [accordion] Storage Options
>
> | Parameter | Type | Description |
> | - | - | - |
> | How to handle the result | Select | Determine how to handle the prompt result:- **Store in Input** — stores the result in the Input object.
- **Store in Context** — stores the result in the Context object.
|
> | Input Key to store Result | CognigyScript | The parameter appears when **Store in Input** is selected. The result is stored in the `extractedEntity` Input object by default. You can specify another key. |
> | Context Key to store Result | CognigyScript | The parameter appears when **Store in Context** is selected. The result is stored in the `extractedEntity` Context object by default. You can specify another key. |
> [accordion] Debugging Options
>
> When using the Interaction Panel, you can trigger two types of debug logs. These logs are only available when using the Interaction Panel and aren't intended for production debugging. You can also combine both log types.
>
> | Parameter | Type | Description |
> | - | - | - |
> | Show Token Count | Toggle | Send a debug message containing the input, output, and total token count. The message appears in the Interaction Panel when debug mode is activated. Cognigy uses the GPT-3 tokenizer, so actual token usage may vary depending on the model. The parameter is inactive by default. |
> | Log Request and Completion | Toggle | Send a debug message containing the LLM provider and the subsequent completion. The message appears in the Interaction Panel when Debug Mode is enabled. The parameter is inactive by default. |
> [accordion] Custom Options
>
> These options let you use parameters that aren't included in the LLM Entity Extract Node or overwrite set configurations.
>
> | Parameter | Type | Description | Example |
> | - | - | - | - |
> | Custom Options | JSON | Set custom parameters to the LLM configuration. You can set individual parameters as well as entire functions. These parameters customize the behavior of the model, such as adjusting `temperature`, `top_k`, or `presence_penalty`. This field overrides the parameters set in the LLM Entity Extract Node. To view the full list of available parameters for your model, refer to the LLM provider's API documentation, for example, [OpenAI](https://platform.openai.com/docs/api-reference/chat/create) or [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/). For Amazon Bedrock and Anthropic models, you must use the `output_config` parameter to [customize the JSON structure in the output](#custom-options). | `{ "model": "gpt-4o", "temperature": 0.7, "max_tokens": 4000 }` |
> | Custom Request Options | JSON | Set custom parameters to customize the request to the LLM provider. These parameters override the configurations set in this Node. For example, setting a `{ "stream": true }` parameter overrides the **Output result immediately** option. For more information, refer to the LLM provider's API documentation. | `{ "stream": true }`, `{ "headers": { "Authorization": "Bearer " } }` |
>
> **Forcing Model Versions**
>
> You can force the LLM Entity Extract Node to use a specific model version by including it in the Custom Options.
> This means that the LLM Entity Extract Node will use the specified version of the language model instead of the default or any other available versions. This allows for more control over the behavior of the LLM Entity Extract Node, ensuring it uses the desired model version for generating prompts or responses.
>
> You can use models from any LLM provider supported by Cognigy, including those not yet directly integrated.
> However, you can only replace a model with another from the same provider.
>
> Let's consider an example with Anthropic:
> You can force the LLM Entity Extract Node to use the model version `claude-opus-4-1-20250805`, despite the LLM resource defaulting to the `claude-opus-4-5-20251101` model:
>
> 1. [Create an Anthropic LLM resource](https://docs-classic.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/anthropic.md) for Claude, for example, `claude-opus-4-5-20251101`.
> 2. Create a Flow and add an LLM Entity Extract Node to it.
> 3. In the LLM Entity Extract Node, select the model `claude-opus-4-5-20251101` from the **Large Language Model** list.
> 4. In the **Custom Model Options** field, add `{ "model": "claude-opus-4-1-20250805" }` to force the use of the `claude-opus-4-1-20250805` model.
> 5. Click **Save Node**.
>
> The LLM Entity Extract Node now utilizes the `claude-opus-4-1-20250805` model.
>
> Below, you'll find documentation for supported models:
>
> * [Aleph Alpha Models](https://docs.aleph-alpha.com/)
> * [Anthropic Models](https://docs.anthropic.com/claude/docs/models-overview)
> * [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models)
> * [OpenAI Models](https://platform.openai.com/docs/models)
>
> **Forcing JSON Output on Amazon Bedrock and Anthropic**
>
> For [Amazon Bedrock](https://docs-classic.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/amazon-bedrock.md) and [Anthropic](https://docs-classic.cognigy.com/ai/agents/develop/gen-ai-and-llms/providers/anthropic.md) models, add the `output_config` parameter to the **Custom Options** field to force the model to return a specific JSON response.
>
> For example, to force the model to return JSON matching a specific schema:
>
> ```json theme={null}
> {
> "output_config": {
> "format": {
> "type": "json_schema",
> "schema": {
> "type": "object",
> "properties": { "answer": {} },
> "required": ["answer"]
> }
> }
> }
> }
> ```
>
> With this configuration, a prompt such as `What is 1+1?` returns a response like `{"answer": 2}` instead of plain text.
## Examples
> [accordion] Extract a Booking Code from User Input
>
> The user input is stored under `input.text`:
>
> ```text theme={null}
> Hello, my booking code is XYZ987. Can you confirm my reservation?
> ```
>
> LLM Entity Extract Node configuration:
>
> * **Entity Name**: `bookingCode`
> * **Entity Description**: `An alphanumeric booking code of 6 characters, such as XYZ123 or ABC987`
> * **Example Input**:
> * `My booking code is XYZ123.`
> * `It's X Y Z 1 2 3.`
> * **Extracted Entity**:
> * `XYZ123`
>
> The extracted entity is stored under `input.extractedEntity`:
>
> ```text theme={null}
> XYZ987
> ```
> [accordion] Extract a Product Code from User Input
>
> The user input is stored under `input.text`:
>
> ```text theme={null}
> I’d like to order the product with code P45K2Q. Can you add it to my cart?
> ```
>
> LLM Entity Extract Node configuration:
>
> * **Entity Name**: `productCode`
> * **Entity Description**: `An alphanumeric product code, such as P12A3B or X45K2Q`
> * **Example Input**:
> * `The product code is P12A3B.`
> * `That would be P 1 2 A 3 B.`
> * **Extracted Entity**:
> * `P12A3B`
>
> The extracted entity is stored under `input.extractedEntity`:
>
> ```text theme={null}
> P45K2Q
> ```
## More Information
* [Question Node: LLM-extracted Entity](https://docs-classic.cognigy.com/ai/agents/develop/node-reference/basic/question.md#)
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