> ## Documentation Index > Fetch the complete documentation index at: https://docs.truestate.io/llms.txt > Use this file to discover all available pages before exploring further. # Get Pipeline Versions ## OpenAPI ````yaml https://api.truestate.io/openapi.json get /pipelines/{pipeline_id}/versions/ openapi: 3.1.0 info: title: TrueState Backend API summary: >- TrueState is an AI platform for business users to create high-impact AI solutions. description: > This **[TrueState](https://www.truestate.io/)** backend API allows our clients to automate and more powerfully retrieve and modify data from the TrueState platform. Example use cases include: - Accessing embeddings for a dataset. - Automating the process of dataset creation and model retraining as your data changes. - Much more. Our backend is built using the [FastAPI](https://fastapi.tiangolo.com/) framework in python using [Auth0](https://auth0.com/) and [Client API Keys](https://docs.truestate.io/docs/api/client-api-keys) for internal an external authentication, respectively. Our backend is designed to be secure, scalable, easy to use and well documented _(each router with its own linked docs page)_. Clients can only access data from their own organisation, as setup in Auth0 our the Client API Keys. See how **[here](https://docs.truestate.io/docs/api/overview)**. **PS**: You can view this API documentation in either **[Swagger](swagger/docs)** or **[Redoc](redoc/)**, but only the swagger lets you test calls. contact: name: True State Support url: http://docs.truestate.io/ email: support@truestate.io license: name: Private url: https://www.apache.org/licenses/LICENSE-2.0.html version: 0.11.78 servers: [] security: [] tags: - name: assistants description: Management of assistants - name: api-keys description: Management of each user personal Client API Keys, used to access this API. - name: models description: Management of models - name: secrets description: Management of client secrets for accessing external services - name: deployments description: Management of deployments. - name: pipelines description: Management of pipelines. - name: pipeline-runs description: Management of pipeline runs. - name: automations description: Management of Automations. - name: automation-sessions description: Management of Automation Sessions. - name: chatbots description: Chatbot endpoints. - name: check-auto-recharge description: '' - name: classification-classes description: >- Management of zero shot classifications (using ai to apply new categories to a dataset). - name: conversations description: Management of conversations - name: conventions description: Management of conventions - best practices for actuators and agents - name: credit-usage description: Management of Credits. - name: dashboards description: Management of Dashboards. - name: data description: Bigquery inference services - name: data-dictionary description: Data Dictionary endpoints. - name: datasets description: Dataset endpoints. - name: documents description: Document endpoints. - name: default description: Default health checks. - name: embedding-search description: '' - name: inference description: Model inference services - name: messages description: Management of messages - name: notebooks description: '' - name: notebook-services description: '' - name: organisations description: Management of Organisations. - name: payment-methods description: Management of Payment methods. - name: private-tasks description: '' - name: private-pipelines description: '' - name: private-data description: '' - name: renew-seats description: '' - name: tagging-lists description: Management of Tagging Lists. - name: tasks description: '' - name: task-templates description: Management of task templates (definitions and schedules). - name: agents description: '' - name: user description: Management of User. - name: user-context description: Management of User Context. - name: private-messages description: '' - name: private-llm-call description: '' - name: admin-organisations description: '' - name: private-warehouser description: '' - name: private-pipeline-tracking description: '' - name: private-pipeline-runs description: '' - name: private-reports description: '' - name: private-bootstrap description: '' - name: reports description: '' - name: private-conversations description: '' - name: private-datasets description: '' - name: private-dashboards description: '' - name: private-conventions description: '' - name: private-documents description: '' paths: /pipelines/{pipeline_id}/versions/: get: tags: - pipelines summary: Get Pipeline Versions operationId: get_pipeline_versions_pipelines__pipeline_id__versions__get parameters: - name: pipeline_id in: path required: true schema: type: string format: uuid title: Pipeline Id - name: offset in: query required: false schema: type: integer default: 0 title: Offset - name: limit in: query required: false schema: type: integer default: 10 title: Limit - name: Api-key in: header required: false schema: anyOf: - type: string - type: 'null' title: Api-Key responses: '200': description: Successful Response content: application/json: schema: type: array items: $ref: '#/components/schemas/PipelineVersionRead' title: >- Response Get Pipeline Versions Pipelines Pipeline Id Versions Get '404': description: Not found '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: PipelineVersionRead: properties: id: type: string format: uuid title: Id pipeline_id: type: string format: uuid title: Pipeline Id steps: items: $ref: '#/components/schemas/PipelineStepRead' type: array title: Steps created_at: type: string format: date-time title: Created At type: object required: - id - pipeline_id - steps - created_at title: PipelineVersionRead HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError PipelineStepRead: properties: id: type: string format: uuid title: Id name: type: string title: Name config: anyOf: - $ref: '#/components/schemas/DataTransformConfig' - $ref: '#/components/schemas/EmbeddingConfig' - $ref: '#/components/schemas/BulkPromptConfig' - $ref: '#/components/schemas/TabularRegressorTrainingConfig' - $ref: '#/components/schemas/TabularRegressorInferenceConfig' - $ref: '#/components/schemas/EnsembleRegressorTrainingConfig-Output' - $ref: '#/components/schemas/EnsembleRegressorInferenceConfig' - $ref: '#/components/schemas/TabularClassifierTrainingConfig' - $ref: '#/components/schemas/TabularClassifierInferenceConfig' - $ref: '#/components/schemas/EnsembleClassifierTrainingConfig-Output' - $ref: '#/components/schemas/EnsembleClassifierInferenceConfig' - $ref: '#/components/schemas/HyperparameterSearchCategoryConfig-Output' - $ref: '#/components/schemas/HyperparameterSearchRegressorConfig-Output' - $ref: '#/components/schemas/IntegrationConfig-Output' - $ref: '#/components/schemas/IntegrationExportConfig-Output' - $ref: '#/components/schemas/TabularRegressorShapConfig' - $ref: '#/components/schemas/BatchAutomationConfig' - $ref: '#/components/schemas/LinearRegressorTrainingConfig' - $ref: '#/components/schemas/LinearRegressorInferenceConfig' - $ref: '#/components/schemas/ClusteringInferenceConfig' - $ref: '#/components/schemas/ClusteringTrainingConfig' - $ref: '#/components/schemas/OptimisationConfig' title: Config created_at: type: string format: date-time title: Created At type: object required: - name - config - created_at title: PipelineStepRead ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError DataTransformConfig: properties: job_type: type: string const: data-transform title: Job Type default: data-transform query_type: $ref: '#/components/schemas/DataTransformQueryType' query: type: string title: Query input_datasets: items: type: string type: array title: Input Datasets output_datasets: items: type: string type: array title: Output Datasets type: object required: - query_type - query - input_datasets - output_datasets title: DataTransformConfig description: >- Configuration for a data transformation job. This job type is used to transform data from one dataset to another. This can be done by running a SQL query or by running a pandas script. This job accepts multiple input datasets and maps them to possible multiple output datasets. It is typically used to perform ETL operations including data cleaning and feature engineering. Attributes: job_type (JobType): Must be 'data-transform'. query_type (DataTransformQueryType): Indicates whether the query is an SQL query or something else. query (str): The SQL or other query to be executed during data transformation. Include a comment at the beginning of the query explaining the query in a way that is easy to understand. Preface the query with a -- using standard SQL comment syntax. Do not reference BigQuery in the comment. input_datasets (list[DatasetName]): List of input datasets. output_datasets (list[DatasetName]): List of output datasets. EmbeddingConfig: properties: job_type: type: string const: apply-embeddings title: Job Type default: apply-embeddings column_name: type: string title: Column Name input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - column_name - input_dataset - output_dataset title: EmbeddingConfig description: >- Configuration for an embedding job. This job type is used to embed data into a vector space by running an embedding model. It will apply the embedding model to the column specified by the column_name attribute on the input dataset and output the results to the output dataset. A new column will be added to the output dataset with the generated embeddings. This dataset can be searched using a vector search engine. Attributes: job_type (JobType): Must be 'apply-embeddings'. column_name (str): The name of the column to embed. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. BulkPromptConfig: properties: job_type: type: string const: bulk-prompt title: Job Type default: bulk-prompt prompt: type: string title: Prompt input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset base_model_type: $ref: '#/components/schemas/BulkPromptBaseModelType' max_new_tokens: type: integer title: Max New Tokens default: 4096 output_column_name: type: string title: Output Column Name default: generated_text type: object required: - prompt - input_dataset - output_dataset - base_model_type title: BulkPromptConfig description: >- Configuration for a bulk prompt job. This job type is used to apply a prompt to a dataset. The prompt is a Python-style f-string which accepts column names as variables within the {} brackets. The formatted prompt is applied to each record of the dataset and fed to an LLM. The LLM's response is then added as a new column to the output dataset. Attributes: job_type (JobType): Must be 'bulk-prompt'. prompt (str): The prompt to apply to the dataset. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. base_model_type (BulkPromptBaseModelType): The type of base model to use. max_new_tokens (int): The maximum number of tokens to generate. output_column_name (str): The name of the column to output the generated text to. TabularRegressorTrainingConfig: properties: job_type: type: string const: tabular-regressor-training title: Job Type default: tabular-regressor-training input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model regressor_config: $ref: '#/components/schemas/XGBoostRegressorConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - regressor_config title: TabularRegressorTrainingConfig description: >- Configuration for a tabular regressor training job. This job type is used to train a tabular regressor model on a dataset. The model will be trained using the feature columns specified by the input_feature_column_names attribute on the input dataset to predict the target column specified by the output_target_column_name attribute on the input dataset. It uses the XGBoost regressor with settings customisable by the regressor_config attribute. It predicts a quantitative value. Attributes: job_type (JobType): Must be 'tabular-regressor-training'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset. output_model (ModelName): The output model. TabularRegressorInferenceConfig: properties: job_type: type: string const: tabular-regressor-inference title: Job Type default: tabular-regressor-inference input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: TabularRegressorInferenceConfig description: >- Configuration for a tabular regressor inference job. This job type is used to infer the regression of a model on a dataset. This job accepts a model trained using the TabularRegressorTrainingConfig and applies it to a dataset. The model will be applied to the columns specified by the input_feature_column_names attribute on the input dataset and output the results to the output dataset. The output dataset will have a new column called 'output_column_name' which will contain the regression for each record. Attributes: job_type (JobType): Must be 'tabular-regressor-inference'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. EnsembleRegressorTrainingConfig-Output: properties: job_type: type: string const: ensemble-regressor-training title: Job Type default: ensemble-regressor-training input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model ensemble_config: $ref: '#/components/schemas/EnsembleRegressorConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - ensemble_config title: EnsembleRegressorTrainingConfig description: >- Configuration for an ensemble regressor. This configuration defines the XGBoost regressor configurations to use for each model in the ensemble. The ensemble size is determined by the number of configurations provided. Attributes: regressor_configs (list[TabularRegressorConfig]): List of XGBoost regressor configurations to use for each model. Each model in the ensemble will use the corresponding config (cycling through if needed). EnsembleRegressorInferenceConfig: properties: job_type: type: string const: ensemble-regressor-inference title: Job Type default: ensemble-regressor-inference input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: EnsembleRegressorInferenceConfig description: >- Configuration for an ensemble regressor inference job. This job type is used to infer the regression of an ensemble model on a dataset. This job accepts a model trained using the EnsembleRegressorTrainingConfig and applies it to a dataset. The model will be applied to the columns specified by the input_feature_column_names attribute on the input dataset and output the results to the output dataset. The output dataset will have a new column called 'output_column_name' which will contain the regression for each record. Attributes: job_type (JobType): Must be 'ensemble-regressor-inference'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. TabularClassifierTrainingConfig: properties: job_type: type: string const: tabular-classifier-training title: Job Type default: tabular-classifier-training input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model classifier_config: $ref: '#/components/schemas/XGBoostClassifierConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - classifier_config title: TabularClassifierTrainingConfig description: >- Configuration for a tabular classifier training job. This job type is used to train a tabular classifier model on a dataset. The model will be trained using the feature columns specified by the input_feature_column_names attribute on the input dataset to predict the label column specified by the output_target_column_name attribute on the input dataset. It uses the XGBoost classifier with settings customisable by the classifier_config attribute. It predicts a categorical value. Attributes: job_type (JobType): Must be 'tabular-classifier-training'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset. output_model (ModelName): The output model. TabularClassifierInferenceConfig: properties: job_type: type: string const: tabular-classifier-inference title: Job Type default: tabular-classifier-inference input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: TabularClassifierInferenceConfig description: >- Configuration for a tabular classifier inference job. This job type is used to infer the classification of a model on a dataset. This job accepts a model trained using the TabularClassifierTrainingConfig and applies it to a dataset. The model will be applied to the columns specified by the input_feature_column_names attribute on the input dataset and output the results to the output dataset. The output dataset will have a new column called 'output_column_name' which will contain the classification for each record. Attributes: job_type (JobType): Must be 'tabular-classifier-inference'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. EnsembleClassifierTrainingConfig-Output: properties: job_type: type: string const: ensemble-classifier-training title: Job Type default: ensemble-classifier-training input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model ensemble_config: $ref: '#/components/schemas/EnsembleClassifierConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - ensemble_config title: EnsembleClassifierTrainingConfig description: >- Configuration for an ensemble classifier training job. This job type is used to train an ensemble of XGBoost classifier models using bagging. Each model is trained on a bootstrap sample of the training data. The ensemble uses majority voting for predictions. Attributes: job_type (JobType): Must be 'ensemble-classifier-training'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset. output_model (ModelName): The output model. ensemble_config (EnsembleClassifierConfig): Configuration for the ensemble including size and classifier settings. EnsembleClassifierInferenceConfig: properties: job_type: type: string const: ensemble-classifier-inference title: Job Type default: ensemble-classifier-inference input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: EnsembleClassifierInferenceConfig description: >- Configuration for an ensemble classifier inference job. This job type is used to infer the classification of an ensemble model on a dataset. This job accepts a model trained using the EnsembleClassifierTrainingConfig and applies it to a dataset. The model will be applied to the columns specified by the input_feature_column_names attribute on the input dataset and output the results to the output dataset. The output dataset will have a new column called 'output_column_name' which will contain the classification for each record. Attributes: job_type (JobType): Must be 'ensemble-classifier-inference'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. HyperparameterSearchCategoryConfig-Output: properties: job_type: type: string const: hyperparameter-search-category title: Job Type default: hyperparameter-search-category input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model output_dataset: type: string title: Output Dataset search_config: $ref: '#/components/schemas/HyperparameterSearchConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - output_dataset - search_config title: HyperparameterSearchCategoryConfig description: >- Configuration for a hyperparameter search job for tabular classifiers. This job type is used to find optimal hyperparameters for an XGBoost classifier using grid search. It accepts a single input dataset which is automatically split into training and test sets (typically 80/20). The model will be trained using the feature columns specified by the input_feature_column_names attribute on the input dataset to predict the label column specified by the output_target_column_name attribute. Grid search is performed using cross-validation on the training set, and the best model (based on the optimization metric) is evaluated on the test set. The job outputs the best model and an evaluation dataset containing one row per trial with metrics and hyperparameters. Attributes: job_type (JobType): Must be 'hyperparameter-search-category'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset that will be split into training and test sets internally. output_model (ModelName): The output model trained with the best hyperparameters. output_dataset (DatasetName): The output dataset containing one row per trial with hyperparameters, CV scores, and test metrics. search_config (HyperparameterSearchConfig): Configuration for the hyperparameter search including search space and optimization settings. HyperparameterSearchRegressorConfig-Output: properties: job_type: type: string const: hyperparameter-search-regressor title: Job Type default: hyperparameter-search-regressor input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model output_dataset: type: string title: Output Dataset search_config: $ref: '#/components/schemas/HyperparameterSearchRegressorSearchConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - output_dataset - search_config title: HyperparameterSearchRegressorConfig description: >- Configuration for a hyperparameter search job for tabular regressors. This job type is used to find optimal hyperparameters for an XGBoost regressor using grid search. It accepts a single input dataset which is automatically split into training and test sets (typically 80/20). The model will be trained using the feature columns specified by the input_feature_column_names attribute on the input dataset to predict the target column specified by the output_target_column_name attribute. Grid search is performed using cross-validation on the training set, and the best model (based on the optimization metric) is evaluated on the test set. The job outputs the best model and an evaluation dataset containing one row per trial with metrics and hyperparameters. Attributes: job_type (JobType): Must be 'hyperparameter-search-regressor'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset that will be split into training and test sets internally. output_model (ModelName): The output model trained with the best hyperparameters. output_dataset (DatasetName): The output dataset containing one row per trial with hyperparameters, CV scores, and test metrics. search_config (HyperparameterSearchRegressorSearchConfig): Configuration for the hyperparameter search including search space and optimization settings. IntegrationConfig-Output: properties: job_type: type: string const: integration title: Job Type default: integration integration_config: oneOf: - $ref: '#/components/schemas/SalesforceIntegrationParams' - $ref: '#/components/schemas/GoogleCloudStorageIntegrationParams' - $ref: '#/components/schemas/AzureBlobStorageIntegrationParams' - $ref: '#/components/schemas/AwsS3IntegrationParams' - $ref: '#/components/schemas/SqlServerIntegrationParams' - $ref: '#/components/schemas/SharepointIntegrationParams' - $ref: '#/components/schemas/BigqueryIntegrationParams' - $ref: '#/components/schemas/FabricWarehouseIntegrationParams' - $ref: '#/components/schemas/SnowflakeIntegrationParams' - $ref: '#/components/schemas/TaboolaIntegrationParams' - $ref: '#/components/schemas/OutbrainIntegrationParams' - $ref: '#/components/schemas/D365BusinessCentralIntegrationParams' title: Integration Config discriminator: propertyName: source mapping: aws-s3: '#/components/schemas/AwsS3IntegrationParams' azure-blob-storage: '#/components/schemas/AzureBlobStorageIntegrationParams' bigquery: '#/components/schemas/BigqueryIntegrationParams' dynamics-365-business-central: '#/components/schemas/D365BusinessCentralIntegrationParams' fabric-warehouse: '#/components/schemas/FabricWarehouseIntegrationParams' google-cloud-storage: '#/components/schemas/GoogleCloudStorageIntegrationParams' outbrain: '#/components/schemas/OutbrainIntegrationParams' salesforce: '#/components/schemas/SalesforceIntegrationParams' sharepoint: '#/components/schemas/SharepointIntegrationParams' snowflake: '#/components/schemas/SnowflakeIntegrationParams' sql-server: '#/components/schemas/SqlServerIntegrationParams' taboola: '#/components/schemas/TaboolaIntegrationParams' credential_secret: type: string title: Credential Secret output_dataset: type: string title: Output Dataset type: object required: - integration_config - credential_secret - output_dataset title: IntegrationConfig IntegrationExportConfig-Output: properties: job_type: type: string const: integration-export title: Job Type default: integration-export integration_config: oneOf: - $ref: '#/components/schemas/GoogleCloudStorageIntegrationParams' - $ref: '#/components/schemas/AzureBlobStorageIntegrationParams' - $ref: '#/components/schemas/AwsS3IntegrationParams' - $ref: '#/components/schemas/SqlServerIntegrationExportParams' - $ref: '#/components/schemas/SnowflakeIntegrationExportParams' title: Integration Config discriminator: propertyName: source mapping: aws-s3: '#/components/schemas/AwsS3IntegrationParams' azure-blob-storage: '#/components/schemas/AzureBlobStorageIntegrationParams' google-cloud-storage: '#/components/schemas/GoogleCloudStorageIntegrationParams' snowflake: '#/components/schemas/SnowflakeIntegrationExportParams' sql-server: '#/components/schemas/SqlServerIntegrationExportParams' credential_secret: type: string title: Credential Secret input_dataset: type: string title: Input Dataset type: object required: - integration_config - credential_secret - input_dataset title: IntegrationExportConfig TabularRegressorShapConfig: properties: job_type: type: string const: tabular-regressor-shap title: Job Type default: tabular-regressor-shap input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: TabularRegressorShapConfig description: >- Configuration for a tabular regressor OR tabular inference SHAP job. This job type is used to calculate the SHAP values for a tabular regressor or tabular inference model on a dataset. The SHAP values are calculated for each record in the dataset and added as new columns to the output dataset. The input_feature_column_names and output_target_column_name must match the input_feature_column_names and output_target_column_name of the input model. Typically this job is used to explain the predictions of a model on a dataset. Attributes: job_type (JobType): Must be 'tabular-regressor-shap'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. BatchAutomationConfig: properties: job_type: type: string const: batch-automation title: Job Type default: batch-automation input_column_names: items: type: string type: array title: Input Column Names output_column_name: type: string title: Output Column Name input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset automation: $ref: '#/components/schemas/AutomationConfig' type: object required: - input_column_names - output_column_name - input_dataset - output_dataset - automation title: BatchAutomationConfig LinearRegressorTrainingConfig: properties: job_type: type: string const: linear-regressor-training title: Job Type default: linear-regressor-training input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model regressor_config: $ref: '#/components/schemas/LinearRegressorConfig' type: object required: - input_feature_column_names - output_target_column_name - input_dataset - output_model - regressor_config title: LinearRegressorTrainingConfig description: >- Configuration for a linear regressor training job. This job type is used to train a linear regressor model on a dataset. The model will be trained using the feature columns specified by the input_feature_column_names attribute on the input dataset to predict the target column specified by the output_target_column_name attribute on the input dataset. It uses the scikit-learn linear regressor with settings customisable by the regressor_config attribute. It predicts a quantitative value. Attributes: job_type (JobType): Must be 'linear-regressor-training'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_dataset (DatasetName): The input dataset. output_model (ModelName): The output model. LinearRegressorInferenceConfig: properties: job_type: type: string const: linear-regressor-inference title: Job Type default: linear-regressor-inference input_feature_column_names: items: type: string type: array title: Input Feature Column Names output_target_column_name: type: string title: Output Target Column Name input_model: type: string title: Input Model input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - input_feature_column_names - output_target_column_name - input_model - input_dataset - output_dataset title: LinearRegressorInferenceConfig description: >- Configuration for a linear regressor inference job. This job type is used to infer the regression of a model on a dataset. This job accepts a model trained using the LinearRegressorTrainingConfig and applies it to a dataset. The model will be applied to the columns specified by the input_feature_column_names attribute on the input dataset and output the results to the output dataset. The output dataset will have a new column called 'output_column_name' which will contain the regression for each record. Attributes: job_type (JobType): Must be 'linear-regressor-inference'. input_feature_column_names (list[str]): The names of the columns to use as the feature input. output_target_column_name (str): The name of the column to use as the target output. input_model (ModelName): The input model. input_dataset (DatasetName): The input dataset. output_dataset (DatasetName): The output dataset. ClusteringInferenceConfig: properties: job_type: type: string const: clustering-inference title: Job Type default: clustering-inference input_dataset: type: string title: Input Dataset input_model: type: string title: Input Model output_dataset: type: string title: Output Dataset type: object required: - input_dataset - input_model - output_dataset title: ClusteringInferenceConfig description: >- This job type is used to apply a previously trained clustering model to a dataset. The clustering model assigns a cluster label to each input row. The resulting dataset, including the predicted cluster labels, is written to the output_dataset. ClusteringTrainingConfig: properties: job_type: type: string const: clustering-training title: Job Type default: clustering-training input_column_names: items: type: string type: array title: Input Column Names input_dataset: type: string title: Input Dataset output_model: type: string title: Output Model clustering_params: $ref: '#/components/schemas/KMeansParams' type: object required: - input_column_names - input_dataset - output_model - clustering_params title: ClusteringTrainingConfig description: >- This job type is used to train an unsupervised clustering model on a dataset. The model is trained using the columns specified by the input_column_names attribute from the input_dataset. No target or label column is required. The default training process will apply 1. median + missing indicator for null numeric values and 2. one hot encoding including the missing value to non numeric columns OptimisationConfig: properties: job_type: type: string const: optimisation title: Job Type default: optimisation script: type: string title: Script input_dataset: type: string title: Input Dataset output_dataset: type: string title: Output Dataset type: object required: - script - input_dataset - output_dataset title: OptimisationConfig description: >- This job type is used to run combinatorial optimisation using Or-Tools. 'script' field must have a function `def run():` which will contain your optimisation code and return a pandas dataframe as the output. This this function will be run in an isolated python environment that only allows standard packages, 'pandas' and 'ortools' packages so don't use any others. The input_dataset name can be directly used as a python variable in the script and is a pandas DataFrame. The `run()` function must return a pandas dataframe which will be saved as the output_dataset. Example script: import pandas as pd from ortools.sat.python import cp_model def run(): # replace the input_dataset_name df = model = cp_model.CpModel() n = len(df) # Decision variables: calls per account (0, 1, or 2) calls = { i: model.NewIntVar(0, 2, f"calls_{i}") for i in range(n) } # Constraint: total calls ≤ 50 max_total_calls = 50 model.Add(sum(calls[i] for i in range(n)) <= max_total_calls) # Objective: maximize total net value objective_terms = [] for i in range(n): expected_value = int(df.loc[i, "expected_value"]) per_call_cost = int(df.loc[i, "per_call_cost"]) # expected_value − cost × calls objective_terms.append( expected_value - per_call_cost * calls[i] ) model.Maximize(sum(objective_terms)) # Solve solver = cp_model.CpSolver() solver.parameters.max_time_in_seconds = 10 status = solver.Solve(model) if status not in (cp_model.OPTIMAL, cp_model.FEASIBLE): raise RuntimeError("No solution found") # Build result DataFrame result = df.copy() result["allocated_calls"] = [ solver.Value(calls[i]) for i in range(n) ] result["net_value"] = ( result["expected_value"] - result["per_call_cost"] * result["allocated_calls"] ) return result DataTransformQueryType: type: string enum: - sql - python - pandas title: DataTransformQueryType BulkPromptBaseModelType: type: string enum: - gemini_2_5_flash title: BulkPromptBaseModelType XGBoostRegressorConfig: properties: tree_method: type: string title: Tree Method default: auto learning_rate: type: number title: Learning Rate default: 0.3 gamma: type: number title: Gamma default: 0 max_depth: type: integer title: Max Depth default: 6 min_child_weight: type: number title: Min Child Weight default: 1 max_delta_step: type: number title: Max Delta Step default: 0 subsample: type: number title: Subsample default: 1 sampling_method: type: string title: Sampling Method default: uniform colsample_bytree: type: number title: Colsample Bytree default: 1 colsample_bylevel: type: number title: Colsample Bylevel default: 1 colsample_bynode: type: number title: Colsample Bynode default: 1 reg_lambda: type: number title: Reg Lambda default: 1 reg_alpha: type: number title: Reg Alpha default: 0 grow_policy: type: string title: Grow Policy default: depthwise max_bin: type: integer title: Max Bin default: 256 num_parallel_tree: type: integer title: Num Parallel Tree default: 1 num_boost_round: type: integer title: Num Boost Round default: 10 early_stopping_rounds: anyOf: - type: integer - type: 'null' title: Early Stopping Rounds log_steps: type: integer title: Log Steps default: 1000 type: object title: XGBoostRegressorConfig EnsembleRegressorConfig: properties: regressor_configs: items: $ref: '#/components/schemas/XGBoostRegressorConfig' type: array title: Regressor Configs type: object required: - regressor_configs title: EnsembleRegressorConfig XGBoostClassifierConfig: properties: tree_method: type: string title: Tree Method default: auto learning_rate: type: number title: Learning Rate default: 0.3 gamma: type: number title: Gamma default: 0 max_depth: type: integer title: Max Depth default: 6 min_child_weight: type: number title: Min Child Weight default: 1 max_delta_step: type: number title: Max Delta Step default: 0 subsample: type: number title: Subsample default: 1 sampling_method: type: string title: Sampling Method default: uniform colsample_bytree: type: number title: Colsample Bytree default: 1 colsample_bylevel: type: number title: Colsample Bylevel default: 1 colsample_bynode: type: number title: Colsample Bynode default: 1 reg_lambda: type: number title: Reg Lambda default: 1 reg_alpha: type: number title: Reg Alpha default: 0 grow_policy: type: string title: Grow Policy default: depthwise max_bin: type: integer title: Max Bin default: 256 num_parallel_tree: type: integer title: Num Parallel Tree default: 1 num_boost_round: type: integer title: Num Boost Round default: 10 early_stopping_rounds: anyOf: - type: integer - type: 'null' title: Early Stopping Rounds log_steps: type: integer title: Log Steps default: 1000 type: object title: XGBoostClassifierConfig EnsembleClassifierConfig: properties: classifier_configs: items: $ref: '#/components/schemas/XGBoostClassifierConfig' type: array title: Classifier Configs type: object required: - classifier_configs title: EnsembleClassifierConfig description: >- Configuration for an ensemble classifier. This configuration defines the XGBoost classifier configurations to use for each model in the ensemble. The ensemble size is determined by the number of configurations provided. Attributes: classifier_configs (list[TabularClassifierConfig]): List of XGBoost classifier configurations to use for each model. Each model in the ensemble will use the corresponding config (cycling through if needed). HyperparameterSearchConfig: properties: search_space: additionalProperties: items: {} type: array propertyNames: $ref: '#/components/schemas/SearchableHyperparameter' type: object title: Search Space default: {} optimization_metric: type: string title: Optimization Metric default: f1 metric_direction: type: string title: Metric Direction default: maximize cv_folds: type: integer title: Cv Folds default: 5 fixed_params: additionalProperties: true type: object title: Fixed Params default: {} type: object title: HyperparameterSearchConfig description: >- Configuration for hyperparameter search for classifiers. This configuration defines the search space, optimization metric, and other settings for finding optimal hyperparameters using grid search on an XGBoost classifier. Attributes: search_space (Dict[SearchableHyperparameter, list]): Dictionary mapping hyperparameters to lists of discrete values to search. Example: {SearchableHyperparameter.learning_rate: [0.01, 0.1, 0.3], SearchableHyperparameter.max_depth: [3, 5, 7]}. optimization_metric (str): The metric to optimize. Options: "precision", "recall", "f1", "accuracy", "auc", "mlogloss". Note: "auc" is only supported for binary classification (2 classes). Default: "f1". metric_direction (str): Whether to maximize or minimize the metric. Options: "maximize" or "minimize". Default: "maximize". cv_folds (int): Number of cross-validation folds to use on the training set. Default: 5. fixed_params (dict): Fixed hyperparameters that are not searched, applied to all trials. These should match XGBoostClassifierConfig structure. HyperparameterSearchRegressorSearchConfig: properties: search_space: additionalProperties: items: {} type: array propertyNames: $ref: '#/components/schemas/SearchableHyperparameter' type: object title: Search Space default: {} optimization_metric: type: string title: Optimization Metric default: rmse metric_direction: type: string title: Metric Direction default: minimize cv_folds: type: integer title: Cv Folds default: 5 fixed_params: additionalProperties: true type: object title: Fixed Params default: {} type: object title: HyperparameterSearchRegressorSearchConfig description: >- Configuration for hyperparameter search for regressors. This configuration defines the search space, optimization metric, and other settings for finding optimal hyperparameters using grid search on an XGBoost regressor. Attributes: search_space (Dict[SearchableHyperparameter, list]): Dictionary mapping hyperparameters to lists of discrete values to search. Example: {SearchableHyperparameter.learning_rate: [0.01, 0.1, 0.3], SearchableHyperparameter.max_depth: [3, 5, 7]}. optimization_metric (str): The metric to optimize. Options: "rmse", "mae", "r2". Default: "rmse". metric_direction (str): Whether to maximize or minimize the metric. For RMSE and MAE, use "minimize". For R2, use "maximize". Default: "minimize". cv_folds (int): Number of cross-validation folds to use on the training set. Default: 5. fixed_params (dict): Fixed hyperparameters that are not searched, applied to all trials. These should match XGBoostRegressorConfig structure. SalesforceIntegrationParams: properties: source: type: string const: salesforce title: Source default: salesforce domain: type: string title: Domain query_type: $ref: '#/components/schemas/SalesforceQueryType' query: type: string title: Query type: object required: - domain - query_type - query title: SalesforceIntegrationParams GoogleCloudStorageIntegrationParams: properties: source: type: string const: google-cloud-storage title: Source default: google-cloud-storage path: type: string title: Path filetype: $ref: '#/components/schemas/IntegrationFileType' type: object required: - path - filetype title: GoogleCloudStorageIntegrationParams AzureBlobStorageIntegrationParams: properties: source: type: string const: azure-blob-storage title: Source default: azure-blob-storage path: type: string title: Path filetype: $ref: '#/components/schemas/IntegrationFileType' type: object required: - path - filetype title: AzureBlobStorageIntegrationParams AwsS3IntegrationParams: properties: source: type: string const: aws-s3 title: Source default: aws-s3 path: type: string title: Path filetype: $ref: '#/components/schemas/IntegrationFileType' type: object required: - path - filetype title: AwsS3IntegrationParams SqlServerIntegrationParams: properties: source: type: string const: sql-server title: Source default: sql-server server: type: string title: Server database: type: string title: Database query: type: string title: Query table_name: anyOf: - type: string - type: 'null' title: Table Name type: object required: - server - database - query title: SqlServerIntegrationParams SharepointIntegrationParams: properties: source: type: string const: sharepoint title: Source default: sharepoint tenant_id: type: string title: Tenant Id site_name: type: string title: Site Name file_path: type: string title: File Path default: '' type: object required: - tenant_id - site_name title: SharepointIntegrationParams BigqueryIntegrationParams: properties: source: type: string const: bigquery title: Source default: bigquery query: type: string title: Query bq_location: type: string title: Bq Location default: '' type: object required: - query title: BigqueryIntegrationParams FabricWarehouseIntegrationParams: properties: source: type: string const: fabric-warehouse title: Source default: fabric-warehouse sql_endpoint: type: string title: Sql Endpoint warehouse_name: type: string title: Warehouse Name query: type: string title: Query type: object required: - sql_endpoint - warehouse_name - query title: FabricWarehouseIntegrationParams SnowflakeIntegrationParams: properties: source: type: string const: snowflake title: Source default: snowflake account_identifier: type: string title: Account Identifier database: type: string title: Database query: type: string title: Query database_schema: type: string title: Database Schema default: public warehouse: anyOf: - type: string - type: 'null' title: Warehouse role: anyOf: - type: string - type: 'null' title: Role type: object required: - account_identifier - database - query title: SnowflakeIntegrationParams TaboolaIntegrationParams: properties: source: type: string const: taboola title: Source default: taboola level: type: string enum: - campaign - item title: Level account_id: type: string title: Account Id start_date: anyOf: - type: string - type: 'null' title: Start Date end_date: anyOf: - type: string - type: 'null' title: End Date last_n_days: anyOf: - type: integer - type: 'null' title: Last N Days default: 30 type: object required: - level - account_id title: TaboolaIntegrationParams OutbrainIntegrationParams: properties: source: type: string const: outbrain title: Source default: outbrain marketer_id: type: string title: Marketer Id include_archived: type: boolean title: Include Archived default: true start_date: anyOf: - type: string - type: 'null' title: Start Date end_date: anyOf: - type: string - type: 'null' title: End Date last_n_days: anyOf: - type: integer - type: 'null' title: Last N Days type: object required: - marketer_id title: OutbrainIntegrationParams D365BusinessCentralIntegrationParams: properties: source: type: string const: dynamics-365-business-central title: Source default: dynamics-365-business-central tenant: type: string title: Tenant environment: type: string title: Environment entity: type: string title: Entity is_company_child: type: boolean title: Is Company Child query_params: anyOf: - type: string - type: 'null' title: Query Params publisher: anyOf: - type: string - type: 'null' title: Publisher group: anyOf: - type: string - type: 'null' title: Group type: object required: - tenant - environment - entity - is_company_child title: D365BusinessCentralIntegrationParams SqlServerIntegrationExportParams: properties: source: type: string const: sql-server title: Source default: sql-server server: type: string title: Server database: type: string title: Database table_name: type: string title: Table Name database_schema: type: string title: Database Schema default: dbo type: object required: - server - database - table_name title: SqlServerIntegrationExportParams SnowflakeIntegrationExportParams: properties: source: type: string const: snowflake title: Source default: snowflake account_identifier: type: string title: Account Identifier database: type: string title: Database table_name: type: string title: Table Name database_schema: type: string title: Database Schema default: public warehouse: anyOf: - type: string - type: 'null' title: Warehouse role: anyOf: - type: string - type: 'null' title: Role type: object required: - account_identifier - database - table_name title: SnowflakeIntegrationExportParams AutomationConfig: properties: id: type: string format: uuid title: Id type: object required: - id title: AutomationConfig LinearRegressorConfig: properties: fit_intercept: type: boolean title: Fit Intercept default: true type: object title: LinearRegressorConfig KMeansParams: properties: algorithm: type: string const: K_MEANS title: Algorithm default: K_MEANS n_clusters: type: integer title: N Clusters max_iter: type: integer title: Max Iter default: 300 tol: type: number title: Tol default: 0.001 type: object required: - n_clusters title: KMeansParams SalesforceQueryType: type: string enum: - soql title: SalesforceQueryType IntegrationFileType: type: string enum: - csv - parquet title: IntegrationFileType securitySchemes: HTTPBearer: type: http scheme: bearer ````