Configuring an LLM connection

LLM Connection is the configuration that enables a system to communicate with a Large Language Model (LLM), such as those developed by OpenAI, Azure OpenAI, AWS Bedrock, and so on. An LLM connection is used to:

Translate natural language into analytical queries.
Power chat-based or voice-based interfaces.
Enhance user experience through intelligent, language-based interaction with data.

An LLM connection allows:

Sending natural language inputs (e.g., user queries) to the LLM.
Receiving generated outputs like MDX/SQL queries, summaries, or explanations.
Enabling features such as conversational analytics, natural language querying, or smart recommendations.

To configure the GenAI LLM connection to use Kyvos Dialogs.

Configuring a connection

From Kyvos 2025.10 onwards, you can add AWS Bedrock as a supported provider for embedding connections. You can create and manage Bedrock-based connections, as with other supported providers such as Azure OpenAI and OpenAI.

Configuring a connection

To configure the GenAI LLM connection, perform the following steps:

From the Toolbox, click Connections.
From the Actions menu ( ⋮ ) click Add Connection.
Select the name of the GenAI provider from the Provide list. The system will use the provider to generate output.
To create an LLM connection, enter details as:
After you finish configuring the settings using the table shown below the screenshot, click Save.

OpenAI

Parameter/Field	Description

Parameter/Field	Description
Name	A unique name that identifies your GenAI connections.
Category	Select the required category from the list.
Provider	Select the OpenAI provider from the list. The system will this provider to generate output.
URL	The LLM Service URL of the provider-specific endpoint for generating output.
API EndPoint	Specify which endpoint to be used to generate AI-powered conversational responses.
Authentication Key	For OpenAI, only the Authentication Key filed is displayed. Specify a unique key for authenticating and authorizing requests to the provider's endpoint. NOTE: If the key is not specified, the last provided Authentication Key will be used. To change, enter an Authentication Key.
Authentication Type	You can select Authentication key, Client ID, Bearer Token, and OAuth 2.0. Based on your selection, the following will be displayed. Authentication Key: Enter a unique key for authenticating and authorizing requests to the provider's endpoint. Client ID: Provide the client ID of the User-Assigned Managed Identity for authenticating and authorizing requests to the provider's endpoint. Bearer Token: Provide an access token used to securely authenticate and authorize requests to the Azure OpenAI Service. NOTE: When using Bearer Token authentication, connections for MDX generation are not supported. OAuth 2.0: Provide the Token URL, Client ID, Secret ID for authenticating and authorizing requests to the provider's endpoint.
Model	The name of the GenAI LLM model used to generate the output.
Is Model Fine Tuned	Select one of the following: Yes: Select this option to fine-tune the model. No: Select this option if you do not want to fine-tune the model.
Embedding Connection	Select the GenAI embedding provider that the system will use to generate embeddings.
Usage	Select one of the following: MDX Generations: Select this option to use the feature for MDX calculations or MDX queries. Conversational Analytics: Select this option to use the feature for Conversational AI usage. You can also set a default connection to be used for MDX calculations, MDX queries. To configure this, select the appropriate checkboxes as needed: Default Connection for MDX Generation: Select this checkbox to set the default connection for MDX calculations and MDX queries. Default Connection for Conversational Analytics: Select this checkbox to use the feature for Conversational AI usage.
Allow Sending Data for LLM	Select Yes or No to specify whether the generated questions should include values or not.
Generate Content	Select Title, Summary, or Key Insight to determine the content to be generated, such as the title, summary, and key insights. NOTE: For summary and key insights, the value for 'Allow Sending Data for NLG', should be set to 'Yes'.
Max Data Points for Summary	Enter the value to configure maximum number of data points involved in generating the summary. NOTE: The default value is 5000.
Input Prompt Token Limit	Specify the maximum tokens allowed for a prompt in a single request for the current provider. NOTE: The default value is 8000. The minimum value is 0.
Output Prompt Token Limit	Specify the maximum number of tokens shared between the prompt and output, which varies by model. One token is approximately four characters for English text. NOTE: The default value is 8000. The minimum value is 0.
Max Retry Count	Specify maximum retry count attempted so that we get correct query. NOTE: The default value is 0.
Summary Records Threshold	Specify similarity threshold for query autocorrection. NOTE: The default value is 0.2 The minimum value is 2.
LLM Temperature	Specify the LLM temperature, which controls the level of randomness in the output. Lowering the temperature results in less random completions. The responses of the model become increasingly deterministic and repetitive as it approaches zero. It is recommended to adjust either the temperature or top-p, but not both simultaneously.
Is Reasoning Model	Specify whether model used is reasoning model.
Properties	Click Properties to view or set properties.

Azure OpenAI

Parameter/Field	Description

Parameter/Field	Description
Connection Name	A unique name that identifies your GenAI connections.
Provider	Select the Azure OpenAI provider from the list. The system will this provider to generate output.
URL	The LLM Service URL of the provider-specific endpoint for generating output.
API EndPoint	Specify which endpoint to be used to generate AI-powered conversational responses.
Authentication Type	You can select Authentication key, Client ID, Bearer Token, and OAuth 2.0. Based on your selection, the following will be displayed. Authentication Key: Enter a unique key for authenticating and authorizing requests to the provider's endpoint. Client ID: Provide the client ID of the User-Assigned Managed Identity for authenticating and authorizing requests to the provider's endpoint. Bearer Token: Provide an access token used to securely authenticate and authorize requests to the Azure OpenAI Service. NOTE: When using Bearer Token authentication, connections for MDX generation are not supported. OAuth 2.0: Provide the Token URL, Client ID, Secret ID for authenticating and authorizing requests to the provider's endpoint.
Authentication Key	Specify a unique key for authenticating or authorizing requests to the provider’s endpoint.
Model	The name of the GenAI LLM model used to generate the output.
Is Model Fine Tuned	Select one of the following: Yes: Select this option to fine-tune the model. No: Select this option if you do not want to fine-tune the model.
Embedding Connection	Specify the name of the GenAI embedding provider that the system will use to generate embeddings.
Usage	Select one of the following: MDX Generations Conversational Analytics You can specify whether you want to use the feature for MDX calculations or MDX queries. If you want to create expressions for calculated measures or members, select MDX calculations. If you want to query on the semantic model, choose the MDX queries option. However: If you select the MDX calculations option, the Kyvos Dialogs option will appear in the Calculated Measures dialog box for creating expressions for calculated measures and members. If you select the MDX queries option, the Kyvos Dialogs option will appear in the Query Playground page. You can also set a default connection to be used for MDX calculations, MDX queries. To configure this, select the appropriate checkboxes as needed: Default Connection for Natural Language Summary: Select this checkbox to set the default connection for Conversational AI usage. Default Connection for MDX Generation: Select this checkbox to set the default connection for MDX calculations and MDX queries. Default Connection for Natural Language Querying: Select this checkbox to set the default connection for natural language queries.
Allow Sending Data for LLM	Select Yes or No to specify whether the generated questions should include values or not.
Max Data Points for Summary	Enter the value to configure maximum number of data points involved in generating the summary. NOTE: The default value is 5000.
Input Prompt Token Limit	Specify the maximum tokens allowed for a prompt in a single request for the current provider. NOTE: The default value is 8000. The minimum value is 0.
Output Prompt Token Limit	Specify the maximum number of tokens shared between the prompt and output, which varies by model. One token is approximately four characters for English text. NOTE: The default value is 8000. The minimum value is 0.
Max Retry Count	Specify maximum retry count attempted so that we get correct query. NOTE: The default value is 0.
Summary Records Threshold	Specify similarity threshold for query autocorrection. NOTE: The default value is 0.2 The minimum value is 2.
LLM Temperature	Specify the LLM temperature, which controls the level of randomness in the output. Lowering the temperature results in less random completions. The responses of the model become increasingly deterministic and repetitive as it approaches zero. It is recommended to adjust either the temperature or top-p, but not both simultaneously.
Top P	This property manages through diversity through nucleus sampling. Setting it to 0.5indicates that half of all likelihood-weighted option will be considered. It is recommended to adjust either this parameter or the temperature parameter, but not both simultaneously.
Frequency Penalty	This property specifies a number between -2.0 and 2.0. Positive values penalize new tokens based on their frequency in the existing text. This reduces the likelihood of the model repeating the same line verbatim.
Presence Penalty	This property specifies a number between -2.0 and 2.0. Positive values penalize new tokens based on their appearance in the text so far, thereby increasing the model's likelihood of discussing new topics.
Is Reasoning Model	Specify whether model used is reasoning model.
Properties	Click Properties to view or set properties.

AWS Bedrock

Parameter/Field	Description

Parameter/Field	Description
Connection Name	A unique name that identifies your GenAI connections.
Category	Select the LLM from the list.
Provider	Select the AWS Bedrock provider from the list. The system will this provider to generate output.
Access ID	Enter the access ID for accessing AWS services.
Secret ID	Enter the secret ID for accessing AWS services.
AWS Region	Enter the AWS region where the model is present.
Model	The name of the GenAI LLM model used to generate the output.
Is Model Fine Tuned	Select one of the following: Yes: Select this option to fine-tune the model. No: Select this option if you do not want to fine-tune the model.
Usage	Select one of the following: MDX Generations Natural Language Querying Natural Language Summary You can specify whether you want to use the feature for MDX calculations or MDX queries. If you want to create expressions for calculated measures or members, select MDX calculations. If you want to query on the semantic model, choose the MDX queries option. However: If you select the MDX calculations option, the Kyvos Dialogs option will appear in the Calculated Measures dialog box for creating expressions for calculated measures and members. If you select the MDX queries option, the Kyvos Dialogs option will appear in the Query Playground page. You can also set a default connection to be used for MDX calculations, MDX queries. To configure this, select the appropriate checkboxes as needed: Default Connection for Natural Language Summary: Select this checkbox to set the default connection for Conversational AI usage. Default Connection for MDX Generation: Select this checkbox to set the default connection for MDX calculations and MDX queries. Default Connection for Natural Language Querying: Select this checkbox to set the default connection for natural language queries.
Allow Sending Data for LLM	Select Yes or No to specify whether the generated questions should include values or not.
Max Data Points for Summary	Enter the value to configure maximum number of data points involved in generating the summary. NOTE: The default value is 5000.
Input Prompt Token Limit	Specify the maximum tokens allowed for a prompt in a single request for the current provider. NOTE: The default value is 8000. The minimum value is 0.
Output Prompt Token Limit	Specify the maximum number of tokens shared between the prompt and output, which varies by model. One token is approximately four characters for English text. NOTE: The default value is 8000. The minimum value is 0.
Max Retry Count	Specify maximum retry count attempted so that we get correct query. NOTE: The default value is 0.
Summary Records Threshold	Specify similarity threshold for query autocorrection. NOTE: The default value is 0.2 The minimum value is 2.
LLM Temperature	Specify the LLM temperature, which controls the level of randomness in the output. Lowering the temperature results in less random completions. The responses of the model become increasingly deterministic and repetitive as it approaches zero. It is recommended to adjust either the temperature or top-p, but not both simultaneously.