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Elastic Docs ›Elasticsearch Guide [master] ›Ingest pipelines ›Ingest processor reference

Inference processor

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Inference processor

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Uses a pre-trained data frame analytics model or a model deployed for natural language processing tasks to infer against the data that is being ingested in the pipeline.

Table 27. Inference Options

Name Required Default Description

model_id .

yes

-

(String) An inference ID, a model deployment ID, a trained model ID or an alias.

input_output

no

-

(List) Input fields for inference and output (destination) fields for the inference results. This option is incompatible with the target_field and field_map options.

target_field

no

ml.inference.<processor_tag>

(String) Field added to incoming documents to contain results objects.

field_map

no

If defined the model’s default field map

(Object) Maps the document field names to the known field names of the model. This mapping takes precedence over any default mappings provided in the model configuration.

inference_config

no

The default settings defined in the model

(Object) Contains the inference type and its options.

ignore_missing

no

false

(Boolean) If true and any of the input fields defined in input_ouput are missing then those missing fields are quietly ignored, otherwise a missing field causes a failure. Only applies when using input_output configurations to explicitly list the input fields.

description

no

-

Description of the processor. Useful for describing the purpose of the processor or its configuration.

if

no

-

Conditionally execute the processor. See Conditionally run a processor.

ignore_failure

no

false

Ignore failures for the processor. See Handling pipeline failures.

on_failure

no

-

Handle failures for the processor. See Handling pipeline failures.

tag

no

-

Identifier for the processor. Useful for debugging and metrics.
  • You cannot use the input_output field with the target_field and field_map fields. For NLP models, use the input_output option. For data frame analytics models, use the target_field and field_map option.
  • Each inference input field must be single strings, not arrays of strings.
  • The input_field is processed as is and ignores any index mapping's analyzers at time of inference run.

Configuring input and output fields

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Select the content field for inference and write the result to content_embedding.

If the specified output_field already exists in the ingest document, it won’t be overwritten. The inference results will be appended to the existing fields within output_field, which could lead to duplicate fields and potential errors. To avoid this, use an unique output_field field name that does not clash with any existing fields.

{
  "inference": {
    "model_id": "model_deployment_for_inference",
    "input_output": [
        {
            "input_field": "content",
            "output_field": "content_embedding"
        }
    ]
  }
}

Configuring multiple inputs

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The content and title fields will be read from the incoming document and sent to the model for the inference. The inference output is written to content_embedding and title_embedding respectively.

{
  "inference": {
    "model_id": "model_deployment_for_inference",
    "input_output": [
        {
            "input_field": "content",
            "output_field": "content_embedding"
        },
        {
            "input_field": "title",
            "output_field": "title_embedding"
        }
    ]
  }
}

Selecting the input fields with input_output is incompatible with the target_field and field_map options.

Data frame analytics models must use the target_field to specify the root location results are written to and optionally a field_map to map field names in the input document to the model input fields.

{
  "inference": {
    "model_id": "model_deployment_for_inference",
    "target_field": "FlightDelayMin_prediction_infer",
    "field_map": {
      "your_field": "my_field"
    },
    "inference_config": { "regression": {} }
  }
}

Classification configuration options

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Classification configuration for inference.

num_top_classes
(Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
num_top_feature_importance_values
(Optional, integer) Specifies the maximum number of feature importance values per document. Defaults to 0 which means no feature importance calculation occurs.
results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
top_classes_results_field
(Optional, string) Specifies the field to which the top classes are written. Defaults to top_classes.
prediction_field_type
(Optional, string) Specifies the type of the predicted field to write. Valid values are: string, number, boolean. When boolean is provided 1.0 is transformed to true and 0.0 to false.

Fill mask configuration options

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num_top_classes
(Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

NER configuration options

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results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Regression configuration options

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Regression configuration for inference.

results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
num_top_feature_importance_values
(Optional, integer) Specifies the maximum number of feature importance values per document. By default, it is zero and no feature importance calculation occurs.

Text classification configuration options

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classification_labels
(Optional, string) An array of classification labels.
num_top_classes
(Optional, integer) Specifies the number of top class predictions to return. Defaults to 0.
results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Text embedding configuration options

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results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Text expansion configuration options

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results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
span

(Optional, integer) When truncate is none, you can partition longer text sequences for inference. The value indicates how many tokens overlap between each subsequence.

The default value is -1, indicating no windowing or spanning occurs.

When your typical input is just slightly larger than max_sequence_length, it may be best to simply truncate; there will be very little information in the second subsequence.

truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Text similarity configuration options

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text_similarity

(Object, optional) Text similarity takes an input sequence and compares it with another input sequence. This is commonly referred to as cross-encoding. This task is useful for ranking document text when comparing it to another provided text input.

Properties of text_similarity inference
span_score_combination_function

(Optional, string) Identifies how to combine the resulting similarity score when a provided text passage is longer than max_sequence_length and must be automatically separated for multiple calls. This only is applicable when truncate is none and span is a non-negative number. The default value is max. Available options are:

  • max: The maximum score from all the spans is returned.
  • mean: The mean score over all the spans is returned.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.

Refer to Properties of tokenizaton to review the properties of the tokenization object.

Zero shot classification configuration options

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labels
(Optional, array) The labels to classify. Can be set at creation for default labels, and then updated during inference.
multi_label
(Optional, boolean) Indicates if more than one true label is possible given the input. This is useful when labeling text that could pertain to more than one of the input labels. Defaults to false.
results_field
(Optional, string) The field that is added to incoming documents to contain the inference prediction. Defaults to the results_field value of the data frame analytics job that was used to train the model, which defaults to <dependent_variable>_prediction.
tokenization

(Optional, object) Indicates the tokenization to perform and the desired settings. The default tokenization configuration is bert. Valid tokenization values are

  • bert: Use for BERT-style models
  • deberta_v2: Use for DeBERTa v2 and v3-style models
  • mpnet: Use for MPNet-style models
  • roberta: Use for RoBERTa-style and BART-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. xlm_roberta: Use for XLMRoBERTa-style models
  • [preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features. bert_ja: Use for BERT-style models trained for the Japanese language.
Properties of tokenization
bert

(Optional, object) BERT-style tokenization is to be performed with the enclosed settings.

Properties of bert
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

deberta_v2

(Optional, object) DeBERTa-style tokenization is to be performed with the enclosed settings.

Properties of deberta_v2
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • balanced: One or both of the first and second sequences may be truncated so as to balance the tokens included from both sequences.
  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.
roberta

(Optional, object) RoBERTa-style tokenization is to be performed with the enclosed settings.

Properties of roberta
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

mpnet

(Optional, object) MPNet-style tokenization is to be performed with the enclosed settings.

Properties of mpnet
truncate

(Optional, string) Indicates how tokens are truncated when they exceed max_sequence_length. The default value is first.

  • none: No truncation occurs; the inference request receives an error.
  • first: Only the first sequence is truncated.
  • second: Only the second sequence is truncated. If there is just one sequence, that sequence is truncated.

For zero_shot_classification, the hypothesis sequence is always the second sequence. Therefore, do not use second in this case.

Inference processor examples

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"inference":{
  "model_id": "my_model_id",
  "field_map": {
    "original_fieldname": "expected_fieldname"
  },
  "inference_config": {
    "regression": {
      "results_field": "my_regression"
    }
  }
}

This configuration specifies a regression inference and the results are written to the my_regression field contained in the target_field results object. The field_map configuration maps the field original_fieldname from the source document to the field expected by the model.

"inference":{
  "model_id":"my_model_id"
  "inference_config": {
    "classification": {
      "num_top_classes": 2,
      "results_field": "prediction",
      "top_classes_results_field": "probabilities"
    }
  }
}

This configuration specifies a classification inference. The number of categories for which the predicted probabilities are reported is 2 (num_top_classes). The result is written to the prediction field and the top classes to the probabilities field. Both fields are contained in the target_field results object.

For an example that uses natural language processing trained models, refer to Add NLP inference to ingest pipelines.

Feature importance object mapping

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To get the full benefit of aggregating and searching for feature importance, update your index mapping of the feature importance result field as you can see below:

"ml.inference.feature_importance": {
  "type": "nested",
  "dynamic": true,
  "properties": {
    "feature_name": {
      "type": "keyword"
    },
    "importance": {
      "type": "double"
    }
  }
}

The mapping field name for feature importance (in the example above, it is ml.inference.feature_importance) is compounded as follows:

<ml.inference.target_field>.<inference.tag>.feature_importance

  • <ml.inference.target_field>: defaults to ml.inference.
  • <inference.tag>: if is not provided in the processor definition, then it is not part of the field path.

For example, if you provide a tag foo in the definition as you can see below:

{
  "tag": "foo",
  ...
}

Then, the feature importance value is written to the ml.inference.foo.feature_importance field.

You can also specify the target field as follows:

{
  "tag": "foo",
  "target_field": "my_field"
}

In this case, feature importance is exposed in the my_field.foo.feature_importance field.

Inference processor examples

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The following example uses an inference endpoint in an inference processor named query_helper_pipeline to perform a chat completion task. The processor generates an Elasticsearch query from natural language input using a prompt designed for a completion task type. Refer to this list for the inference service you use and check the corresponding examples of setting up an endpoint with the chat completion task type.

resp = client.ingest.put_pipeline(
    id="query_helper_pipeline",
    processors=[
        {
            "script": {
                "source": "ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content"
            }
        },
        {
            "inference": {
                "model_id": "openai_chat_completions",
                "input_output": {
                    "input_field": "prompt",
                    "output_field": "query"
                }
            }
        },
        {
            "remove": {
                "field": "prompt"
            }
        }
    ],
)
print(resp)
const response = await client.ingest.putPipeline({
  id: "query_helper_pipeline",
  processors: [
    {
      script: {
        source:
          "ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content",
      },
    },
    {
      inference: {
        model_id: "openai_chat_completions",
        input_output: {
          input_field: "prompt",
          output_field: "query",
        },
      },
    },
    {
      remove: {
        field: "prompt",
      },
    },
  ],
});
console.log(response);
PUT _ingest/pipeline/query_helper_pipeline
{
  "processors": [
    {
      "script": {
        "source": "ctx.prompt = 'Please generate an elasticsearch search query on index `articles_index` for the following natural language query. Dates are in the field `@timestamp`, document types are in the field `type` (options are `news`, `publication`), categories in the field `category` and can be multiple (options are `medicine`, `pharmaceuticals`, `technology`), and document names are in the field `title` which should use a fuzzy match. Ignore fields which cannot be determined from the natural language query context: ' + ctx.content" 
      }
    },
    {
      "inference": {
        "model_id": "openai_chat_completions", 
        "input_output": {
          "input_field": "prompt",
          "output_field": "query"
        }
      }
    },
    {
      "remove": {
        "field": "prompt"
      }
    }
  ]
}

The prompt field contains the prompt used for the completion task, created with Painless. + ctx.content appends the natural language input to the prompt.

The ID of the pre-configured inference endpoint, which utilizes the openai service with the completion task type.

The following API request will simulate running a document through the ingest pipeline created previously:

resp = client.ingest.simulate(
    id="query_helper_pipeline",
    docs=[
        {
            "_source": {
                "content": "artificial intelligence in medicine articles published in the last 12 months"
            }
        }
    ],
)
print(resp)
const response = await client.ingest.simulate({
  id: "query_helper_pipeline",
  docs: [
    {
      _source: {
        content:
          "artificial intelligence in medicine articles published in the last 12 months",
      },
    },
  ],
});
console.log(response);
POST _ingest/pipeline/query_helper_pipeline/_simulate
{
  "docs": [
    {
      "_source": {
        "content": "artificial intelligence in medicine articles published in the last 12 months" 
      }
    }
  ]
}

The natural language query used to generate an Elasticsearch query within the prompt created by the inference processor.

Further readings

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