Add failure handling for set processors in ML inference pipelines (#1…

…44654)

## Summary

Also, add a `remove` processor and `text_classification` and
`text_embedding` types.

### Checklist

Delete any items that are not applicable to this PR.

- [x] [Unit or functional
tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)
were updated or added to match the most common scenarios

(cherry picked from commit 8e81a7d)

x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.test.ts

@@ -6,6 +6,7 @@
  */
 
 import {
+  IngestRemoveProcessor,
   IngestSetProcessor,
   MlTrainedModelConfig,
   MlTrainedModelStats,
@@ -19,11 +20,12 @@ import {
   BUILT_IN_MODEL_TAG as LOCAL_BUILT_IN_MODEL_TAG,
   generateMlInferencePipelineBody,
   getMlModelTypesForModelConfig,
+  getRemoveProcessorForInferenceType,
   getSetProcessorForInferenceType,
-  SUPPORTED_PYTORCH_TASKS as LOCAL_SUPPORTED_PYTORCH_TASKS,
   parseMlInferenceParametersFromPipeline,
   parseModelStateFromStats,
   parseModelStateReasonFromStats,
+  SUPPORTED_PYTORCH_TASKS as LOCAL_SUPPORTED_PYTORCH_TASKS,
 } from '.';
 
 const mockModel: MlTrainedModelConfig = {
@@ -69,6 +71,38 @@ describe('getMlModelTypesForModelConfig lib function', () => {
   });
 });
 
+describe('getRemoveProcessorForInferenceType lib function', () => {
+  const destinationField = 'dest';
+
+  it('should return expected value for TEXT_CLASSIFICATION', () => {
+    const inferenceType = SUPPORTED_PYTORCH_TASKS.TEXT_CLASSIFICATION;
+
+    const expected: IngestRemoveProcessor = {
+      field: destinationField,
+      ignore_missing: true,
+    };
+
+    expect(getRemoveProcessorForInferenceType(destinationField, inferenceType)).toEqual(expected);
+  });
+
+  it('should return expected value for TEXT_EMBEDDING', () => {
+    const inferenceType = SUPPORTED_PYTORCH_TASKS.TEXT_EMBEDDING;
+
+    const expected: IngestRemoveProcessor = {
+      field: destinationField,
+      ignore_missing: true,
+    };
+
+    expect(getRemoveProcessorForInferenceType(destinationField, inferenceType)).toEqual(expected);
+  });
+
+  it('should return undefined for unknown inferenceType', () => {
+    const inferenceType = 'wrongInferenceType';
+
+    expect(getRemoveProcessorForInferenceType(destinationField, inferenceType)).toBeUndefined();
+  });
+});
+
 describe('getSetProcessorForInferenceType lib function', () => {
   const destinationField = 'dest';
 
@@ -84,7 +118,7 @@ describe('getSetProcessorForInferenceType lib function', () => {
       description:
         "Copy the predicted_value to 'dest' if the prediction_probability is greater than 0.5",
       field: destinationField,
-      if: 'ctx.ml.inference.dest.prediction_probability > 0.5',
+      if: "ctx?.ml?.inference != null && ctx.ml.inference['dest'] != null && ctx.ml.inference['dest'].prediction_probability > 0.5",
       value: undefined,
     };
 
@@ -98,6 +132,7 @@ describe('getSetProcessorForInferenceType lib function', () => {
       copy_from: 'ml.inference.dest.predicted_value',
       description: "Copy the predicted_value to 'dest'",
       field: destinationField,
+      if: "ctx?.ml?.inference != null && ctx.ml.inference['dest'] != null",
       value: undefined,
     };
 
@@ -191,13 +226,19 @@ describe('generateMlInferencePipelineBody lib function', () => {
       expect.objectContaining({
         description: expect.any(String),
         processors: expect.arrayContaining([
+          expect.objectContaining({
+            remove: {
+              field: 'my-destination-field',
+              ignore_missing: true,
+            },
+          }),
           expect.objectContaining({
             set: {
               copy_from: 'ml.inference.my-destination-field.predicted_value',
               description:
                 "Copy the predicted_value to 'my-destination-field' if the prediction_probability is greater than 0.5",
               field: 'my-destination-field',
-              if: 'ctx.ml.inference.my-destination-field.prediction_probability > 0.5',
+              if: "ctx?.ml?.inference != null && ctx.ml.inference['my-destination-field'] != null && ctx.ml.inference['my-destination-field'].prediction_probability > 0.5",
             },
           }),
         ]),

x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts

@@ -7,6 +7,7 @@
 
 import {
   IngestPipeline,
+  IngestRemoveProcessor,
   IngestSetProcessor,
   MlTrainedModelConfig,
   MlTrainedModelStats,
@@ -58,6 +59,7 @@ export const generateMlInferencePipelineBody = ({
     model.input?.field_names?.length > 0 ? model.input.field_names[0] : 'MODEL_INPUT_FIELD';
 
   const inferenceType = Object.keys(model.inference_config)[0];
+  const remove = getRemoveProcessorForInferenceType(destinationField, inferenceType);
   const set = getSetProcessorForInferenceType(destinationField, inferenceType);
 
   return {
@@ -69,6 +71,7 @@ export const generateMlInferencePipelineBody = ({
           ignore_missing: true,
         },
       },
+      ...(remove ? [{ remove }] : []),
       {
         inference: {
           field_map: {
@@ -123,21 +126,37 @@ export const getSetProcessorForInferenceType = (
       copy_from: `${prefixedDestinationField}.predicted_value`,
       description: `Copy the predicted_value to '${destinationField}' if the prediction_probability is greater than 0.5`,
       field: destinationField,
-      if: `ctx.${prefixedDestinationField}.prediction_probability > 0.5`,
+      if: `ctx?.ml?.inference != null && ctx.ml.inference['${destinationField}'] != null && ctx.ml.inference['${destinationField}'].prediction_probability > 0.5`,
       value: undefined,
     };
   } else if (inferenceType === SUPPORTED_PYTORCH_TASKS.TEXT_EMBEDDING) {
     set = {
       copy_from: `${prefixedDestinationField}.predicted_value`,
       description: `Copy the predicted_value to '${destinationField}'`,
       field: destinationField,
+      if: `ctx?.ml?.inference != null && ctx.ml.inference['${destinationField}'] != null`,
       value: undefined,
     };
   }
 
   return set;
 };
 
+export const getRemoveProcessorForInferenceType = (
+  destinationField: string,
+  inferenceType: string
+): IngestRemoveProcessor | undefined => {
+  if (
+    inferenceType === SUPPORTED_PYTORCH_TASKS.TEXT_CLASSIFICATION ||
+    inferenceType === SUPPORTED_PYTORCH_TASKS.TEXT_EMBEDDING
+  ) {
+    return {
+      field: destinationField,
+      ignore_missing: true,
+    };
+  }
+};
+
 /**
  * Parses model types list from the given configuration of a trained machine learning model
  * @param trainedModel configuration for a trained machine learning model