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* (Doc+) Inference Pipeline ignores Mapping Analyzers From internal Dev feedback (will cross-link after), this updates that inference processors within ingest pipelines run before mapping analyzers effectively ignoring them. So if users want analyzers to take effect, they would need to select the analyzer's ingest pipeline process equivalent and run it higher in flow than the inference processor. --------- Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
633 lines
18 KiB
Text
633 lines
18 KiB
Text
[role="xpack"]
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[[inference-processor]]
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=== {infer-cap} processor
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++++
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<titleabbrev>{infer-cap}</titleabbrev>
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++++
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Uses a pre-trained {dfanalytics} model or a model deployed for natural
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language processing tasks to infer against the data that is being
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ingested in the pipeline.
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[[inference-options]]
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.{infer-cap} Options
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[options="header"]
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|======
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| Name | Required | Default | Description
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| `model_id` . | yes | - | (String) The ID or alias for the trained model, or the ID of the deployment.
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| `input_output` | no | - | (List) Input fields for {infer} and output (destination) fields for the {infer} results. This option is incompatible with the `target_field` and `field_map` options.
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| `target_field` | no | `ml.inference.<processor_tag>` | (String) Field added to incoming documents to contain results objects.
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| `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.
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| `inference_config` | no | The default settings defined in the model | (Object) Contains the inference type and its options.
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| `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.
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include::common-options.asciidoc[]
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|======
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[IMPORTANT]
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==================================================
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* You cannot use the `input_output` field with the `target_field` and
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`field_map` fields. For NLP models, use the `input_output` option. For
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{dfanalytics} models, use the `target_field` and `field_map` option.
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* Each {infer} input field must be single strings, not arrays of strings.
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* The `input_field` is processed as is and ignores any <<mapping,index mapping>>'s <<analysis,analyzers>> at time of {infer} run.
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==================================================
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[discrete]
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[[inference-input-output-example]]
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==== Configuring input and output fields
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Select the `content` field for inference and write the result to
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`content_embedding`.
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IMPORTANT: If the specified `output_field` already exists in the ingest document, it won't be overwritten.
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The {infer} results will be appended to the existing fields within `output_field`, which could lead to duplicate fields and potential errors.
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To avoid this, use an unique `output_field` field name that does not clash with any existing fields.
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[source,js]
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--------------------------------------------------
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{
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"inference": {
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"model_id": "model_deployment_for_inference",
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"input_output": [
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{
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"input_field": "content",
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"output_field": "content_embedding"
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}
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]
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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==== Configuring multiple inputs
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The `content` and `title` fields will be read from the incoming document and
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sent to the model for the inference. The inference output is written to
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`content_embedding` and `title_embedding` respectively.
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[source,js]
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--------------------------------------------------
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{
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"inference": {
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"model_id": "model_deployment_for_inference",
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"input_output": [
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{
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"input_field": "content",
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"output_field": "content_embedding"
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},
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{
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"input_field": "title",
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"output_field": "title_embedding"
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}
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]
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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Selecting the input fields with `input_output` is incompatible with
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the `target_field` and `field_map` options.
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{dfanalytics-cap} models must use the `target_field` to specify the root
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location results are written to and optionally a `field_map` to map field names
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in the input document to the model input fields.
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[source,js]
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--------------------------------------------------
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{
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"inference": {
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"model_id": "model_deployment_for_inference",
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"target_field": "FlightDelayMin_prediction_infer",
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"field_map": {
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"your_field": "my_field"
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},
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"inference_config": { "regression": {} }
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}
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}
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--------------------------------------------------
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// NOTCONSOLE
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[discrete]
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[[inference-processor-classification-opt]]
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==== {classification-cap} configuration options
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Classification configuration for inference.
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`num_top_classes`::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-classes]
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`num_top_feature_importance_values`::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-feature-importance-values]
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`top_classes_results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-top-classes-results-field]
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`prediction_field_type`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-prediction-field-type]
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[discrete]
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[[inference-processor-fill-mask-opt]]
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==== Fill mask configuration options
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`num_top_classes`::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-classes]
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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.Properties of bert
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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+
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.Properties of roberta
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`mpnet`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
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+
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.Properties of mpnet
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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=====
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[discrete]
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[[inference-processor-ner-opt]]
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==== NER configuration options
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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.Properties of bert
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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+
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.Properties of roberta
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`mpnet`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
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+
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.Properties of mpnet
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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=====
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[discrete]
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[[inference-processor-regression-opt]]
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==== {regression-cap} configuration options
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Regression configuration for inference.
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`num_top_feature_importance_values`::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-regression-num-top-feature-importance-values]
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[discrete]
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[[inference-processor-text-classification-opt]]
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==== Text classification configuration options
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`classification_labels`::
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(Optional, string) An array of classification labels.
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`num_top_classes`::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-top-classes]
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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.Properties of bert
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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+
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.Properties of roberta
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`mpnet`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
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+
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.Properties of mpnet
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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=====
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[discrete]
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[[inference-processor-text-embedding-opt]]
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==== Text embedding configuration options
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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.Properties of bert
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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+
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.Properties of roberta
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`mpnet`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
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+
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.Properties of mpnet
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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=====
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[discrete]
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[[inference-processor-text-expansion-opt]]
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==== Text expansion configuration options
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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|
.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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|
.Properties of bert
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`roberta`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
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+
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.Properties of roberta
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[%collapsible%open]
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=======
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`span`::::
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(Optional, integer)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-span]
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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`mpnet`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
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+
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.Properties of mpnet
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
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=======
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=====
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[discrete]
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[[inference-processor-zero-shot-opt]]
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==== Zero shot classification configuration options
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`labels`::
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(Optional, array)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-zero-shot-classification-labels]
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`multi_label`::
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(Optional, boolean)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-zero-shot-classification-multi-label]
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`results_field`::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-results-field-processor]
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`tokenization`::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization]
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+
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.Properties of tokenization
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[%collapsible%open]
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=====
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`bert`::::
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(Optional, object)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-bert]
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+
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.Properties of bert
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[%collapsible%open]
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=======
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`truncate`::::
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(Optional, string)
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include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
|
|
=======
|
|
|
|
`roberta`::::
|
|
(Optional, object)
|
|
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-roberta]
|
|
+
|
|
.Properties of roberta
|
|
[%collapsible%open]
|
|
=======
|
|
|
|
`truncate`::::
|
|
(Optional, string)
|
|
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
|
|
=======
|
|
|
|
`mpnet`::::
|
|
(Optional, object)
|
|
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-mpnet]
|
|
+
|
|
.Properties of mpnet
|
|
[%collapsible%open]
|
|
=======
|
|
|
|
`truncate`::::
|
|
(Optional, string)
|
|
include::{es-ref-dir}/ml/ml-shared.asciidoc[tag=inference-config-nlp-tokenization-truncate]
|
|
=======
|
|
=====
|
|
|
|
[discrete]
|
|
[[inference-processor-config-example]]
|
|
==== {infer-cap} processor examples
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
"inference":{
|
|
"model_id": "my_model_id",
|
|
"field_map": {
|
|
"original_fieldname": "expected_fieldname"
|
|
},
|
|
"inference_config": {
|
|
"regression": {
|
|
"results_field": "my_regression"
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// NOTCONSOLE
|
|
|
|
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.
|
|
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
"inference":{
|
|
"model_id":"my_model_id"
|
|
"inference_config": {
|
|
"classification": {
|
|
"num_top_classes": 2,
|
|
"results_field": "prediction",
|
|
"top_classes_results_field": "probabilities"
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// NOTCONSOLE
|
|
|
|
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 {nlp} trained models, refer to
|
|
{ml-docs}/ml-nlp-inference.html[Add NLP inference to ingest pipelines].
|
|
|
|
[discrete]
|
|
[[inference-processor-feature-importance]]
|
|
==== {feat-imp-cap} object mapping
|
|
|
|
To get the full benefit of aggregating and searching for
|
|
{ml-docs}/ml-feature-importance.html[{feat-imp}], update your index mapping of
|
|
the {feat-imp} result field as you can see below:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
"ml.inference.feature_importance": {
|
|
"type": "nested",
|
|
"dynamic": true,
|
|
"properties": {
|
|
"feature_name": {
|
|
"type": "keyword"
|
|
},
|
|
"importance": {
|
|
"type": "double"
|
|
}
|
|
}
|
|
}
|
|
--------------------------------------------------
|
|
// NOTCONSOLE
|
|
|
|
The mapping field name for {feat-imp} (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:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
{
|
|
"tag": "foo",
|
|
...
|
|
}
|
|
--------------------------------------------------
|
|
// NOTCONSOLE
|
|
|
|
|
|
Then, the {feat-imp} value is written to the
|
|
`ml.inference.foo.feature_importance` field.
|
|
|
|
You can also specify the target field as follows:
|
|
|
|
[source,js]
|
|
--------------------------------------------------
|
|
{
|
|
"tag": "foo",
|
|
"target_field": "my_field"
|
|
}
|
|
--------------------------------------------------
|
|
// NOTCONSOLE
|
|
|
|
In this case, {feat-imp} is exposed in the
|
|
`my_field.foo.feature_importance` field.
|