USER MANUALS

Denodo Assistant

This page explains how to enable the Denodo Assistant features that use large language models. The page Denodo Assistant Features That Use Large Language Models lists these features.

To use these features, first you have to set up the connection to the API of the large language model (LLM) and then, activate this set of features.

LLM Configuration

In the LLM Configuration area of the Denodo Assistant section, you set up the large language model provider that the Denodo Assistant will use.

Denodo Assistant LLM provider configuration

When setting up a provider, click Test Configuration to ensure the configuration is correct. This sends a test request to the AI service.

Denodo supports these LLM providers: Amazon Bedrock, Azure OpenAI Service, OpenAI and Other (OpenAI API Compatible).

Amazon Bedrock

  • Authentication. Authentication method for accessing Amazon Bedrock. Choose between AWS IAM credentials or Denodo AWS instance credentials.

    • AWS IAM credentials. Use this option if you want to specify the AWS credentials.

      • AWS access key ID. Unique identifier that specifies the user or entity making the request.

      • AWS secret access key. Secret string that is used to sign the requests you make to AWS.

      • AWS IAM role ARN (optional). IAM identity with specific permissions.

      • AWS region. The AWS region where access to the Amazon Bedrock service is available.

    • Denodo AWS instance credentials. Use this option if the AWS credentials are configured in the instance where Denodo is running.

      • AWS IAM role ARN (optional). IAM identity with specific permissions.

      • AWS region. The AWS region where access to the Amazon Bedrock service is available.

  • Context window (tokens). The maximum number of tokens allowed by the model selected.

  • Custom Temperature: Provides advanced control over the temperature parameter used in LLM requests. You can turn off temperature entirely (required for certain reasoning-focused models such as OpenAI’s o3) or specify a fixed temperature value (necessary for models like GPT-5). This option isn’t needed for the certified models in the list, as their temperature behavior is managed internally.

  • Max Output Tokens: The maximum number of tokens that the model is allowed to generate in a single response.

  • Model id. Unique identifier of a model. At the moment, only Amazon Bedrock models from the Claude family of model provider Anthropic are supported. By default, some model IDs use the “us.” prefix to denote the AWS region for inference. You can replace this prefix with your region’s code if needed.

  • Use custom URL. When enabled, allows configuring a custom endpoint URL.

  • Endpoint URL. Endpoint URL for LLM requests.

For more information on the AWS authentication parameters go to the Amazon AWS security credentials reference.

Azure OpenAI

  • Azure resource name. The name of your Azure resource.

  • Azure deployment name. The deployment name you chose when you deployed the model.

  • API version. The API version to use for this operation. This follows the YYYY-MM-DD format.

  • API key. The API key.

  • Context window (tokens). The maximum number of tokens allowed by the model you deployed.

  • Custom Temperature: Provides advanced control over the temperature parameter used in LLM requests. You can turn off temperature entirely (required for certain reasoning-focused models such as OpenAI’s o3) or specify a fixed temperature value (necessary for models like GPT-5). This option isn’t needed for the certified models in the list, as their temperature behavior is managed internally.

  • Max Output Tokens: The maximum number of tokens that the model is allowed to generate in a single response.

  • Specify URI: When enabled, allows configuring a custom Chat Completions URI.

  • Chat Completions URI. The custom Chat Completions endpoint.

  • Authentication. When Specify URI is enabled, allows to configure whether the custom API requires authentication with API key.

For more information on the Azure OpenAI Service API parameters go to the Azure OpenAI Service REST API reference.

When using Specify URI, we define that a specific API is eligible if it implements the official Chat Completions Azure OpenAI Service API (see https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#chat-completions). Not all the parameters of the Chat Completions Azure OpenAI Service API are needed for the custom API to be compatible with the Denodo Assistant:

  • Request body. The Denodo Assistant will make requests with a request body having the following parameters: messages and temperature.

  • Response body. The Denodo Assistant needs the following parameters in the response body: id, object, created, choices and usage

OpenAI

  • API key. This is the OpenAI API key. This parameter is required.

  • Organization ID. If configured, a header to specify which organization is used for an API request will be sent. This is useful for users who belong to multiple organizations. This parameter is not required.

  • Model. The model which is going to be used to generate the query. The dropdown values are the ones tested by Denodo. However, if you want to try an untested OpenAI model, you can configure it by pressing the edit icon. Note that some models shown may not work depending on your organization’s OpenAI account.

  • Context window (tokens). The maximum number of tokens allowed by the model selected.

  • Custom Temperature: Provides advanced control over the temperature parameter used in LLM requests. You can turn off temperature entirely (required for certain reasoning-focused models such as OpenAI’s o3) or specify a fixed temperature value (necessary for models like GPT-5). This option isn’t needed for the certified models in the list, as their temperature behavior is managed internally.

  • Max Output Tokens: The maximum number of tokens that the model is allowed to generate in a single response.

For more information on the OpenAI API parameters, go to the OpenAI API reference https://platform.openai.com/docs/api-reference/authentication.

Other (OpenAI API Compatible)

  • Authentication. Configure this option depending on whether the custom API requires authentication.

  • API key. The API key. If the authentication is turned on, this parameter is required.

  • Organization ID. If configured, a header to specify which organization is used for an API request will be sent. This is useful for users who belong to multiple organizations. Only available when the authentication is turned on. This parameter is not required.

  • Chat Completions URI. The Chat Completions endpoint. This parameter is required.

  • Model. The model which is going to be used to generate the query. This parameter is required.

  • Context window (tokens). The maximum number of tokens allowed by your custom model. This parameter is required.

  • Custom Temperature: Provides advanced control over the temperature parameter used in LLM requests. You can turn off temperature entirely (required for certain reasoning-focused models such as OpenAI’s o3) or specify a fixed temperature value (necessary for models like GPT-5). This option isn’t needed for the certified models in the list, as their temperature behavior is managed internally.

  • Max Output Tokens: The maximum number of tokens that the model is allowed to generate in a single response. This parameter is required.

The Other (OpenAI API Compatible) option allows the Denodo Assistant to send and process requests from APIs following the OpenAI Chat Completions API approach. We define that an API follows this approach if it implements the official Chat Completions OpenAI API (see https://platform.openai.com/docs/api-reference/chat). Not all the parameters of the Chat Completions OpenAI API are needed for the API to be compatible with the Denodo Assistant:

  • Request body. The Denodo Assistant will make requests with a request body having the following parameters: model, messages and temperature.

  • Response body. The Denodo Assistant needs the following parameters in the response body: id, object, created, choices and usage.

In addition to OpenAI models such as gpt-oss-20b, several others support the Chat Completions API, including Google’s Gemini and models that can be served using Ollama, such as Llama 3.3, Mistral, and Phi-4. The following examples demonstrate how to configure both a Gemini model and models served via Ollama.

  • Vertex AI Platform. Follow these steps to use a deployed Gemini model via the Vertex AI Platform.

    1. Select Other (OpenAI API Compatible) as the Provider.

    2. If authentication is required, enter the API key. You can retrieve a temporary access token using the following command in Google Cloud Shell:

      gcloud auth print-access-token

    3. Specify the URI of the chat completions endpoint for the Vertex AI API. This URI must include your specific {location} and {project} values: https://{location}-aiplatform.googleapis.com/v1beta1/projects/{project}/locations/{location}/endpoints/openapi/chat/completions.

    4. In the Model field, enter the full name of your deployed Gemini model, such as google/gemini-2.0-flash.

    By following these steps, Denodo can securely connect to and utilize your deployed Vertex AI model.

  • Ollama. Follow these steps to use a local LLM hosted by the Ollama server.

    Before you begin, ensure you have the following:

    1. The Ollama server is installed and running on your machine.

    2. You have pulled the model you wish to use (e.g., gpt-oss:20b or llama3.2:3b). The model must be available on your local Ollama server.

      ollama pull gpt-oss:20b

    Follow these steps to complete the configuration:

    1. Select Other (OpenAI API Compatible) as the Provider.

    2. Leave the API Key field blank, as a locally running Ollama server typically does not require authentication.

    3. Specify the URI for the Ollama chat completions endpoint. This will usually be your local machine’s address and the default Ollama port (11434), followed by the API path: http://localhost:11434/v1/chat/completions.

    4. In the Model field, enter the name of the model you have pulled in Ollama, such as gpt-oss:20b or llama3.2:3b.

    By following these steps, Denodo can connect to your local Ollama server and utilize the selected model.

HTTP Proxy Configuration

In the Http Proxy Configuration area of the LLM configuration section, you set up the proxy that the Denodo Assistant will use:

  • Mode. Specifies the proxy mode the Denodo Assistant will use. There are 3 modes:

    • DEFAULT. Denodo Assistant’s requests will be routed through a proxy if the vdp server proxy is activated in the section OTHERS > HTTP Proxy of Server Configuration.

    • MANUAL. The requests will use a proxy specific to the Denodo Assistant. When this option is selected, you must provide the proxy parameters specific for the Denodo Assitant’s requests.

    • OFF. The Denodo Assistant will not use a proxy.

If the proxy configured requires Basic Authorization, add this property to the JVM properties of the Virtual DataPort server and the web container:

-Djdk.http.auth.tunneling.disabledSchemes=""

For more information about configuring the JVM properties see Denodo Platform Configuration section.

General

In the Denodo Assistant > General, you configure the Denodo assistant.

Denodo Assistant configuration

Denodo Assistant configuration parameters

  • Enable Denodo Assistant features that use a large language model. This enables or disables the Denodo Assistant’s features that use a large language model.

Note

  • Enabling the Assistant allows the server to send metadata information to the configured LLM when using it. Only if the data usage is enabled, data of the view is also going to be processed and sent to the LLM in order to improve the results. Make sure your organization is aware and agrees on this.

  • Enabling this feature will result in incurring expenses for requests made to an external LLM API. Make sure that the organization is fully cognizant of this financial implication prior to enabling this option.

  • Language Options. The language in which the Denodo Assistant will answer. You can choose either the server’s i18n language (see the section OTHERS > Server i18n of Server configuration) or an alternative language.

  • Use a different language for column name generation. When enabled, the Assistant will generate the column names in a different language from the one selected before. See the Suggest Field Names section for more information.

  • Use sample data. When enabled, the Assistant will send actual data of the views to the LLM (i.e. rows that the views return). This can help improve the Assistant’s responses. For this option to work, check that the Cache Module is enabled. If enabled, enter the Sample data size. This is the number of rows retrieved for sampling. The sampling process will create a sample selecting a subset of the result rows. When enabled, for the feature Suggest Field Descriptions, the suggested field descriptions will typically include real data examples of the fields of type text. Moreover, suggested view description <Suggest View Description> may sometimes also include some data samples.

  • Add extra instructions to the metadata requests. When enabled, the Denodo Assistant will take the user’s input into account if it is relevant to the functionality as additional context when suggesting View Descriptions, Field Descriptions, Field Names and View Tags (both for the view itself and its fields). This can be useful for incorporating the company’s context or business naming conventions.

Note

Enabling the use sample data allows the Assistant to send actual data of the view(s) used. Make sure your organization is aware and agrees on this.

Vector Database Integration

In the Vector Database Integration area of the Denodo Assistant section, you set up the embeddings model and vector database that the Denodo Assistant will use.

Denodo Assistant Vector Database Integration

When configuring a vector database and embedding model, click Test Configuration to ensure the configuration is correct. This will embed a test document into the vector database and remove it after to verify the configuration.

The section Features that use the Vector Integration explains which features require the vector integration.

It is important to note that the vector database integration will use the collections vdp_views and vdp_test_config and none of these should be used externally since they will be modified and deleted in the process of using the vector integration features.

Index Configuration

The Index Configuration allows users to configure how Denodo handles the indexation:

  • Batch Size. Number of elements to index at once.

  • Refresh metadata interval. Interval in seconds at which the system looks for updates and refreshes the necessary metadata.

Vector Database Configuration

Denodo supports configuring Pgvector as the vector database.

  • Database URI. The connection URL to the database.

  • Authentication type. Authentication type to use when connecting to de vector database. At the moment, the supported authentication types are: login and password, login and password from password vault (single secret) and login and password from password vault (one secret per field).

Embedding Model Configuration

Denodo supports these embedding model providers: Amazon Bedrock, Azure OpenAI Service, OpenAI and Other (OpenAI API Compatible).

Azure OpenAI

  • Azure OpenAI endpoint. Endpoint for the Azure OpenAI API.

  • Azure Deployment name. Name of the deployed Azure OpenAI embedding model.

  • Authentication. Authentication method for accessing Azure. Choose between API Key or Username and password.

    • API key. The API key.

    • Username and password. Username and password for Azure identity-based authentication.

  • Headers. Additional headers to include in API requests.

  • Max tokens. Maximum tokens allowed by the deployed model.

  • Proxy Mode. Proxy to use for the embedding requests. There are 3 modes:

    • DEFAULT. Denodo Assistant’s embedding requests will be routed through a proxy if the vdp server proxy is activated in the section OTHERS > HTTP Proxy of Server Configuration.

    • MANUAL. The embedding requests will use a proxy specific to the Denodo Assistant. When this option is selected, you must provide the proxy parameters specific for the Denodo Assitant’s embedding requests.

    • OFF. The Denodo Assistant embedding requests will not use a proxy.

For more information on the Azure OpenAI Service API parameters go to the Azure OpenAI Service REST API reference.

Amazon Bedrock

  • Authentication. Authentication method for accessing Amazon Bedrock. Choose between AWS IAM credentials or Denodo AWS instance credentials.

    • AWS IAM credentials. Use this option if you want to specify the AWS credentials.

      • AWS access key ID. Unique identifier that specifies the user or entity making the request.

      • AWS secret access key. Secret string that is used to sign the requests you make to AWS.

      • AWS IAM role ARN (optional). IAM identity with specific permissions.

      • AWS region. The AWS region where access to the Amazon Bedrock service is available.

    • Denodo AWS instance credentials. Use this option if the AWS credentials are configured in the instance where Denodo is running.

      • AWS IAM role ARN (optional). IAM identity with specific permissions.

      • AWS region. The AWS region where access to the Amazon Bedrock service is available.

  • Max tokens. Maximum tokens allowed by the deployed model.

  • Model name. Embedding model to use.

  • Use custom URL. When enabled, allows configuring a custom endpoint URL.

  • Endpoint URL. Endpoint URL for embedding model requests.

For more details on Amazon Titan embedding models go to the Amazon Titan embedding models reference. For more information on the AWS authentication parameters go to the Amazon AWS security credentials reference.

OpenAI

  • API key. The API key.

  • Max tokens. Maximum tokens allowed by the deployed model.

  • Model. Embedding model to use. The dropdown values are the ones tested by Denodo. However, if you want to try an untested OpenAI model, you can configure it by pressing the edit icon. Note that some models shown may not work depending on your organization’s OpenAI account.

  • Organization ID. Specifies OpenAI organization.

For more details, see the OpenAI embedding models documentation https://platform.openai.com/docs/guides/embeddings#embedding-models.

For OpenAI and Other (OpenAI API Compatible) embedding models, you can set up a proxy without authentication using the following JVM properties of the Virtual DataPort server and the web container:

-Dhttps.proxyHost=proxyHost.com
-Dhttps.proxyPort=1234

Other (OpenAI API Compatible)

  • API key. The API key.

  • Max tokens. Maximum tokens allowed by the deployed model.

  • Model. Embedding model to use.

  • URI. Custom endpoint for OpenAI.

The Other (OpenAI API Compatible) option allows the Denodo Assistant to send and process requests from APIs following the OpenAI Create Embeddings approach. We define that an API follows this approach if it adheres to the official Create Embeddings API (see https://platform.openai.com/docs/api-reference/embeddings/create).

There are models that adhere to the OpenAI Create Embeddings approach and are not made by OpenAI. These include Ollama models, such as nomic-embed-text, mxbai-embed-large or all-minilm, which can even be run locally. To configure these models, ensure you use the v1 endpoint: /v1.

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