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
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 |
|---|---|
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.
|
Model | The name of the GenAI LLM model used to generate the output. |
Is Model Fine Tuned | Select one of the following: |
Embedding Connection | Select the GenAI embedding provider that the system will use to generate embeddings. |
Usage | Select one of the following:
You can also set a default connection to be used for MDX calculations, MDX queries. To configure this, select the appropriate checkboxes as needed:
|
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 Rows Summary | Enter the value to configure maximum values for row summary. NOTE: The default value is 100. |
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 |
|---|---|
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 | 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: |
Embedding Connection | Specify the name of the GenAI embedding provider that the system will use to generate embeddings. |
Usage | Select one of the following:
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:
You can also set a default connection to be used for MDX calculations, MDX queries. To configure this, select the appropriate checkboxes as needed:
|
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 Rows Summary | Enter the value to configure maximum values for row summary. NOTE: The default value is 100. |
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. |