Search Extract Output

Updated in 2026.5

Search Extract Output Node configuration panel

Description

As of Cognigy.AI 4.69.0, the topK.order property of Knowledge Chunks in the search result generated by Search Extract Output Nodes is deprecated.

This Node searches through data within the Knowledge AI solution, extracts a relevant answer via a generative AI model, and creates an output.

Prerequisites

Configure a model that supports Knowledge Search in the Knowledge AI Settings section in Manage > Settings.

Parameters

Mode

There are three modes that offer different levels of data extraction and presentation:

Search & Extract & Output
Search & Extract
Search Only

Performs a search, extracts an answer, and outputs the result as text or adaptive card.In the Search & Extract & Output mode, the following steps are involved:

The system performs a knowledge search and retrieves relevant information.
The retrieved data is sent to a Language Model (LLM) for further processing.
The LLM extracts key points or answers from the search results, filtering out unnecessary details.
The extracted information is stored in the Input or Context objects.
The system automatically outputs the result from the LLM as either plain text or an adaptive card.

For this mode, select models from the list of supported providers that cover the LLM Prompt Node, Answer Extraction, and Knowledge Search cases.

Performs a search, extracts an answer, but doesn’t perform an automatic output. Our customers can then manually create the output they desire.In the Search & Extract mode, the following steps are involved:

The system performs a knowledge search and retrieves relevant information.
The retrieved data is directly sent to the LLM for extraction of key points or answers.
The LLM extracts the relevant information and writes it to the Input or Context objects.

For this mode, select models from the list of supported providers that cover both the LLM Prompt Node, Answer Extraction, and Knowledge Search cases.

Performs a knowledge search and retrieves information without extraction or automatic output.In the Search only mode, the following steps are involved:

The system performs a knowledge search and retrieves relevant information.
The search response is stored in the Input or Context objects.

For this mode, select models from the list of supported providers that cover the Knowledge Search case.

Knowledge Store

Select a store from the list to allow the Node to retrieve the data.

Search Settings

Parameter	Type	Description
Context-Aware Search	Select	Considering the transcript’s context for the search. It allows an AI Agent to answer follow-up questions. You can select one of the following options: On — is the default option. When this setting is enabled, it affects tokenization costs, which impact the amount of memory, computational power, and overall expenses needed for running an OpenAI or Azure OpenAI model. To get a better understanding of how the feature works in practice, see Examples. Off — the setting is turned off.
Transcript Steps	Slider	Number of prior transcript steps to use in a context-aware search. This parameter is active when the Context-Aware Search setting is enabled. One transcript step consists of a pair: one user input and one AI Agent output. For example, if you select a quantity of 2, the system considers the 2 most recent pairs of user input and AI Agent output.
Where to store the result	Select	The storage for the search results. Select a storage: default — the content is stored in the Input object (`input.knowledgeSearch`). Input — the content is stored in the Input object. Context — the content is stored in the Context.
String to search for	CognigyScript	The string to search for in the Knowledge Store. A default value is stored in `input.text`.
Top K	Slider	Specify how many of the knowledge search results are returned. The results are stored under `input.knowledgeSearch.topK`. Any `\n` and `\t` instances in the Knowledge Chunk text are replaced with spaces in the results.
Source Tags	CognigyScript	The tags serve to refine the scope of your knowledge search, allowing you to include only the most pertinent sections of the knowledge base and, as a result, improve the accuracy of search outputs. Before specifying tags, ensure that they were provided during the creation of the Knowledge Sources. Add Tags by specifying each Tag separately and pressing `Enter`. The maximum number of tags is 5. When you specify multiple Source Tags, the Search Extract Output Node defaults to an `AND` operator, meaning it only considers Sources that have all the specified Tags. This approach ensures the search results are precise and highly relevant to the end user’s query. To change this behavior, go to the Match Types for Source Tags parameter.
Match type for Source Tags	Select	The operator to filter Knowledge Sources by Source Tags. Select one of the following options: AND — the default value, requires all tags to match across multiple Knowledge Sources. Consider the following example: there are Knowledge Sources with Tags `S-a`, `S-b`, and `S-c`. When you use the `AND` operator to filter by `S-a` and `S-b`, only Sources with both Tags `S-a` and `S-b` are included in the search results. OR — requires at least one tag to match across multiple Knowledge Sources. Consider the following example: there are Knowledge Sources with Tags `S-a`, `S-b`, and `S-c`. When you use the `OR` operator to filter by `S-a` or `S-b`, any Source with either Tag `S-a` or `S-b` is included in the search results.

Source Tags

You can add CognigyScript within Source Tags.
Source Tags are always lower-case, only alpha-numerics (a-z, 0-9), underscores, and hyphens are allowed.
When using CognigyScript for Source Tags, it is important to lowercase them. For example, by using the following: {{input.text.toLowerCase()}}.

Extract Settings

Parameter	Type	Description
Prompt	CognigyScript	The prompt for generating completions. This prompt is designed for ChatGPT-based models. The behavior of this prompt may vary depending on the model used. You can change the prompt at your discretion. In the prompt, the following tags are used to retrieve information from the Knowledge Store and Input object to ensure that the LLM receives all the necessary information to generate the most accurate response: `@answerLanguage` – the language that refers to the `input.language` value from the Input object for the current locale. `@foundDocuments` – the top-k knowledge results determined from the end user’s question. `@userInput` – the message that the end user sent to the AI Agent in the conversation.
Temperature	Slider	The appropriate sampling temperature for the model. Higher values mean the model takes more risks.
Maximal Tokens	Slider	The maximum number of tokens to generate in the completion.
Presence Penalty	Slider	Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model’s likelihood of talking about new topics.
Frequency Penalty	Slider	Number between -2.0 and 2.0. The penalty assigns a lower probability to tokens frequently appearing in the generated text, encouraging the model to generate more diverse and unique content.
Use Stops	Toggle	Whether to use a list of stop words to let Generative AI know where the sentence stops.
Stops	Text	Up to 4 sequences where the API stops generating further tokens. The returned text doesn’t contain the stop sequence.
Timeout	Number	The maximum number of milliseconds to wait for a response from the LLM provider.
Timeout Message	CognigyScript	The message to output if the timeout is reached.

Output Settings

Parameter	Type	Description
OutputMode	Select	This parameter allows you to choose between two options for the output: Text — select this option if you want the system to provide a basic text-based response as the output. Adaptive Card — select this option if you want a visual representation of an Adaptive Card as the output. Adaptive Cards are interactive and display rich content with buttons, images, and more.
Output Fallback Text	CognigyScript	The text to output if no answer was found.

Error Handling

Parameter	Type	Description
Select Error Handling Approach	Select	You can select one of the Error Handling options: Stop Flow Execution — terminate the current Flow execution. Continue Flow Execution — allow the Flow to continue executing, bypassing the error and proceeding to the next steps. Go to Node — redirect the workflow to a specific Node in the Flow, which can be useful for error recovery or customized error handling.
Error Message (optional)	Text	Active when the Continue Flow Execution option is selected. Optional message to output if the Search or Extract service fails.
Select Flow	Select	Active when the Go to Node option is selected. Select a Flow from the available options.
Select Node	Select	Active when the Go to Node option is selected. Select a Node from the available options.

Custom Options

These options let you use parameters that aren’t included in the Search Extract Output Node or overwrite set configurations.

Parameter	Type	Description	Example
Custom Options	JSON	Set custom parameters for 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 only the parameters set to generative models in the Search Extract Output Node; the field doesn’t support custom options for embedding models used in the knowledge search. 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 Search Extract Output Node to use a specific model version by including it in the Custom Options. This means that the Search Extract Output Node will use the specified version of the language model instead of the default or any other available version. This allows for more control over the behavior of the Search Extract Output 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 that aren’t 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 Search Extract Output Node to use the model version claude-opus-4-1-20250805, although the LLM resource defaults 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 a Search Extract Output Node to it.
In the Search Extract Output 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 Search Extract Output Node now utilizes the claude-opus-4-1-20250805 model.Below, you’ll find documentation for supported models:

Debugging Options

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 enabled. Cognigy.AI uses the GPT-3 tokenizer algorithm, so actual token usage may vary depending on the model used. The parameter is inactive by default.
Log Request and Completion	Toggle	Send a debug message containing the request sent to 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.

Examples

Context-Aware Search

By considering the context, the search can help users find information within a transcript more quickly, as it understands the nuances of the content. When enabling a context-aware search system, you can configure the number of prior transcript steps to use in the search to suit your specific requirements. The number of prior transcript steps affects the depth of context considered when retrieving search results. In the following table, a dialogue between the user and the AI Agent is presented, showing the agent’s responses in both the enabled and disabled modes of the Context-Aware Search feature.

Context-Aware Search is off	Context-Aware Search is on
user: What animals are allowed on a plane? AI Agent: Passengers are allowed to bring a maximum of two animals (dogs or cats) on the plane, either in the cabin or in the cargo hold, as long as they are in approved transport containers. user: Give me more details! AI Agent: Unfortunately, I can’t answer your question.	user: What animals are allowed on a plane? AI Agent: Passengers are allowed to bring a maximum of two animals (dogs or cats) on the plane, either in the cabin or in the cargo hold, as long as they are in an approved transport container. user: Give me more details! AI Agent: The approved transport container for animals on a plane must have a maximum size of 118 cm (55 x 40 x 23 cm) or 47 in (22 x 16 x 9 in) and a total weight, including the animal, not exceeding 8 kg. The container should be escape-proof, leak-proof, bite-proof, and lined with absorbent material. Only one transport container per passenger is allowed in the cabin, and the number of animals per container is limited to one, with exceptions for specific cases.

Overview

AI Agents

Human Escalation

Platform Features

For Developers

Administration and Installation

Description

Prerequisites

Parameters

Mode

Knowledge Store

Debugging Options

Examples

Context-Aware Search

More Information

​Description

​Prerequisites

​Parameters

​Mode

​Knowledge Store

​Debugging Options

​Examples

​Context-Aware Search

​More Information

Description

Prerequisites

Parameters

Mode

Knowledge Store

Debugging Options

Examples

Context-Aware Search

More Information