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Intent Recognition with External Embedding Model

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An embedding is a sequence of numbers that represents the meaning of sentences and is used by the NLU for tasks such as intent classification.

The Intent Recognition with External Embedding Model feature allows using the text-embedding-3-model hosted by OpenAI and Azure OpenAI, replacing the default Cognigy embedding models:

  • It is designed for complex projects with a large number of intents and example sentences.
  • It enables you to set up the intent model faster because you spend less time selecting and refining example sentences.
  • There is a cost associated with using third-party models. It involves making external requests to a third-party service, which may incur expenses and latency.

Note

The Azure OpenAI and OpenAI models are multilingual, and the NLU will work even if the Cognigy Flow is set in a different language than the user input. We recommend creating individual Flows for each localization.

Prerequisites

  • Add the text-embedding-3-large model provided by Azure OpenAI or OpenAI. Note that if you use an external embedding model provider, Cognigy will send user inputs to the selected service. Ensure that your data processing policy reflects this practice.

Set Up a Third-Party NLU model

To set up a third-party NLU model, follow these steps:

  1. Open the Cognigy.AI interface.
  2. In the left-side menu, select an Project where you want to use a third-party NLU model.
  3. In the left-side menu of the Project, navigate to Manage > Settings.
  4. Go to the NLU Settings section.
  5. From the Embedding Model Provider list, select a model that you added as part of the Prerequisites.
  6. Confirm changes.

Train Flows

Changing the NLU embedding model requires all Flows in the Project to be retrained. Otherwise, intent recognition will fail because the new embeddings won't match the old ones used for training.

During Flow training, only enabled intents are considered. Intents marked as disabled will be skipped.

Select the number of Flows that need training:

Train a Flow

If you have only one Flow in your Project, follow these steps:

  1. In the left-side menu of the Project, navigate to Build > Flows.
  2. On the Flows page, select the Flow that you want to train.
  3. In the upper-right corner, select NLU.
  4. On the Intents tab, click Build Model.

Once the model building process is complete, the intent recognition capability of the Flow will be improved, allowing for more accurate understanding of user inputs.

Train Multiple Flows

If you have more one Flow in your Project, follow these steps:

  1. In the left-side menu of the Project, navigate to Build > Flows.
  2. On the Flows page, check if the Train all Flows button exists. If the Train all Flows button is not enabled for your environment, specify the FEATURE_TRAIN_ALL_PROJECT_FLOWS feature flag in the values.yaml file for on-premises installations or contact Cognigy technical support.
  3. Click Train all Flows.

Once the model building process is complete, the intent recognition capability of the Flow will be improved, allowing for more accurate understanding of user inputs.

Note that during the training process, the order of Flows is not predefined. Flows are retrieved from the database sequentially, following their default storage order.

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