LLM Entity Extract

Updated in 2026.18

LLM Entity Extract Node configuration panel

Description

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 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. 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. To output the extracted entity, add a Say Node 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 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`.

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 Node, this Node is already instructed to generate a JSON output when this option is selected 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.

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 or Azure 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" } }`

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.

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.

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 or Azure OpenAI.	`{ "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 <token>" } }`

Forcing Model VersionsYou 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:

Create an Anthropic LLM resource for Claude, for example, claude-opus-4-5-20251101.
Create a Flow and add an LLM Entity Extract Node to it.
In the LLM Entity Extract Node, select the model claude-opus-4-5-20251101 from the Large Language Model list.
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.
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:

Examples

Extract a Booking Code from User Input

The user input is stored under input.text:

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:

XYZ987

Extract a Product Code from User Input

The user input is stored under input.text:

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:

P45K2Q

More Information

Question Node: LLM-extracted Entity

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