JsonpMappingException: Error deserializing co.elastic.clients.elasticsearch.ml.TextEmbeddingInferenceOptions

[yulinxp]

Java API client version

8.16.1

Java version

17

Elasticsearch Version

8.16.1

Problem description

I'd like to get all trained models

        GetTrainedModelsResponse response = esClient.ml().getTrainedModels(g -> g
                .modelId("_all")
                .size(100) 
        );

Here is the error:

co.elastic.clients.transport.TransportException: node: https://xxx.com/, status: 200, [es/ml.get_trained_models] Failed to decode response
	at co.elastic.clients.transport.ElasticsearchTransportBase.decodeTransportResponse(ElasticsearchTransportBase.java:409)
	at co.elastic.clients.transport.ElasticsearchTransportBase.getApiResponse(ElasticsearchTransportBase.java:367)
	at co.elastic.clients.transport.ElasticsearchTransportBase.performRequest(ElasticsearchTransportBase.java:148)
	at co.elastic.clients.elasticsearch.ml.ElasticsearchMlClient.getTrainedModels(ElasticsearchMlClient.java:1761)
	at co.elastic.clients.elasticsearch.ml.ElasticsearchMlClient.getTrainedModels(ElasticsearchMlClient.java:1778)
	at com.dsg.oss.ds.batch.vectorgeneration.service.ElasticService.getProductCount(ElasticService.java:99)
	at com.dsg.oss.ds.batch.vectorgeneration.service.ElasticService.lambda$getProductCountWithRetries$18(ElasticService.java:90)
	at com.dsg.oss.ds.batch.vectorgeneration.util.RetryWrapper.call(RetryWrapper.java:29)
	at com.dsg.oss.ds.batch.vectorgeneration.service.ElasticService.getProductCountWithRetries(ElasticService.java:91)
	at com.dsg.oss.ds.batch.vectorgeneration.service.VectorGenerationService.generate(VectorGenerationService.java:51)
	at com.dsg.oss.ds.batch.vectorgeneration.CustomApplicationRunner.run(CustomApplicationRunner.java:29)
	at org.springframework.boot.SpringApplication.callRunner(SpringApplication.java:760)
	at org.springframework.boot.SpringApplication.callRunners(SpringApplication.java:750)
	at org.springframework.boot.SpringApplication.run(SpringApplication.java:317)
	at org.springframework.boot.SpringApplication.run(SpringApplication.java:1304)
	at org.springframework.boot.SpringApplication.run(SpringApplication.java:1293)
	at com.dsg.oss.ds.batch.vectorgeneration.VectorGenerationApplication.main(VectorGenerationApplication.java:10)
Caused by: co.elastic.clients.json.JsonpMappingException: Error deserializing co.elastic.clients.elasticsearch.ml.TextEmbeddingInferenceOptions: co.elastic.clients.util.MissingRequiredPropertyException: Missing required property 'Builder.<variant kind>' (JSON path: trained_model_configs[2].inference_config.text_embedding.tokenization) (line no=1, column no=2824, offset=-1)
	at co.elastic.clients.json.JsonpMappingException.from0(JsonpMappingException.java:134)
	at co.elastic.clients.json.JsonpMappingException.from(JsonpMappingException.java:121)
	at co.elastic.clients.json.ObjectDeserializer.deserializeInner(ObjectDeserializer.java:236)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:166)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:157)
	at co.elastic.clients.json.JsonpDeserializer.deserialize(JsonpDeserializer.java:77)
	at co.elastic.clients.json.ObjectBuilderDeserializer.deserialize(ObjectBuilderDeserializer.java:79)
	at co.elastic.clients.json.DelegatingDeserializer$SameType.deserialize(DelegatingDeserializer.java:43)
	at co.elastic.clients.json.ObjectDeserializer$FieldObjectDeserializer.deserialize(ObjectDeserializer.java:78)
	at co.elastic.clients.json.ObjectDeserializer.deserializeInner(ObjectDeserializer.java:214)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:166)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:157)
	at co.elastic.clients.json.JsonpDeserializer.deserialize(JsonpDeserializer.java:77)
	at co.elastic.clients.json.BuildFunctionDeserializer.deserialize(BuildFunctionDeserializer.java:47)
	at co.elastic.clients.json.DelegatingDeserializer$SameType.deserialize(DelegatingDeserializer.java:43)
	at co.elastic.clients.json.ObjectDeserializer$FieldObjectDeserializer.deserialize(ObjectDeserializer.java:78)
	at co.elastic.clients.json.ObjectDeserializer.deserializeInner(ObjectDeserializer.java:214)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:166)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:157)
	at co.elastic.clients.json.ObjectBuilderDeserializer.deserialize(ObjectBuilderDeserializer.java:85)
	at co.elastic.clients.json.DelegatingDeserializer$SameType.deserialize(DelegatingDeserializer.java:48)
	at co.elastic.clients.json.JsonpDeserializerBase$ArrayDeserializer.deserialize(JsonpDeserializerBase.java:318)
	at co.elastic.clients.json.JsonpDeserializerBase$ArrayDeserializer.deserialize(JsonpDeserializerBase.java:280)
	at co.elastic.clients.json.JsonpDeserializer.deserialize(JsonpDeserializer.java:77)
	at co.elastic.clients.json.ObjectDeserializer$FieldObjectDeserializer.deserialize(ObjectDeserializer.java:78)
	at co.elastic.clients.json.ObjectDeserializer.deserializeInner(ObjectDeserializer.java:214)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:166)
	at co.elastic.clients.json.ObjectDeserializer.deserialize(ObjectDeserializer.java:157)
	at co.elastic.clients.json.JsonpDeserializer.deserialize(JsonpDeserializer.java:77)
	at co.elastic.clients.json.ObjectBuilderDeserializer.deserialize(ObjectBuilderDeserializer.java:79)
	at co.elastic.clients.json.DelegatingDeserializer$SameType.deserialize(DelegatingDeserializer.java:43)
	at co.elastic.clients.transport.ElasticsearchTransportBase.decodeTransportResponse(ElasticsearchTransportBase.java:404)
	... 16 common frames omitted
Caused by: co.elastic.clients.util.MissingRequiredPropertyException: Missing required property 'Builder.<variant kind>'
	at co.elastic.clients.util.ApiTypeHelper.requireNonNull(ApiTypeHelper.java:76)
	at co.elastic.clients.elasticsearch.ml.TokenizationConfig.<init>(TokenizationConfig.java:119)
	at co.elastic.clients.elasticsearch.ml.TokenizationConfig.<init>(TokenizationConfig.java:66)
	at co.elastic.clients.elasticsearch.ml.TokenizationConfig$Builder.build(TokenizationConfig.java:244)
	at co.elastic.clients.json.BuildFunctionDeserializer.deserialize(BuildFunctionDeserializer.java:48)
	at co.elastic.clients.json.DelegatingDeserializer$SameType.deserialize(DelegatingDeserializer.java:43)
	at co.elastic.clients.json.ObjectDeserializer$FieldObjectDeserializer.deserialize(ObjectDeserializer.java:78)
	at co.elastic.clients.json.ObjectDeserializer.deserializeInner(ObjectDeserializer.java:214)
	... 45 common frames omitted

[l-trotta]

Hello and thank you for reporting this! I tried reproducing it using the same version of the client, but I couldn't, and I suspect it's because of some configuration in one of the trained models that we're wrongly mapping and I currently don't have locally. Could you please execute the same query in kibana:

GET _ml/trained_models/_all

and paste the result? It would speed up a lot of the debugging :D thank you!

[yulinxp]

I have modified this.

      "classification_labels": [
        "Cookie",
        "Candy"
      ]

{
  "count": 17,
  "trained_model_configs": [
    {
      "model_id": ".elser_model_1",
      "model_type": "pytorch",
      "model_package": {
        "packaged_model_id": "elser_model_1",
        "model_repository": "https://ml-models.elastic.co",
        "minimum_version": "8.8.0",
        "size": 438123276,
        "sha256": "95f645a3ab8dc66a33de7892391a41ef4fc609a74d21d7b3f7fdd973d58dfe06",
        "metadata": {},
        "tags": [],
        "vocabulary_file": "elser_model_1.vocab.json"
      },
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1692293022496,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Elastic Learned Sparse EncodeR v1 (Tech Preview)",
      "tags": [
        "elastic"
      ],
      "metadata": {},
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_expansion": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          }
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": ".elser_model_2",
      "model_type": "pytorch",
      "model_package": {
        "packaged_model_id": "elser_model_2",
        "model_repository": "https://ml-models.elastic.co",
        "minimum_version": "11.0.0",
        "size": 438123914,
        "sha256": "2e0450a1c598221a919917cbb05d8672aed6c613c028008fedcd696462c81af0",
        "metadata": {},
        "tags": [],
        "vocabulary_file": "elser_model_2.vocab.json"
      },
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1707771109798,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Elastic Learned Sparse EncodeR v2",
      "tags": [
        "elastic"
      ],
      "metadata": {},
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_expansion": {
          "vocabulary": {
            "index": ".ml-inference-native-000002"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          }
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000002"
        }
      }
    },
    {
      "model_id": ".multilingual-e5-small_linux-x86_64",
      "model_type": "pytorch",
      "model_package": {
        "packaged_model_id": "multilingual-e5-small_linux-x86_64",
        "model_repository": "https://ml-models.elastic.co",
        "minimum_version": "12.0.0",
        "size": 411956170,
        "sha256": "1382e5ec74600a1c35919ed1c29d7f8cd03dae5e347abb515d2798277483291b",
        "metadata": {},
        "tags": [],
        "vocabulary_file": "multilingual-e5-small_linux-x86_64.vocab.json",
        "platform_architecture": "linux-x86_64"
      },
      "platform_architecture": "linux-x86_64",
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1733758532610,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "E5 small multilingual optimized for linux-x86_64",
      "tags": [],
      "metadata": {},
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_embedding": {
          "vocabulary": {
            "index": ".ml-inference-native-000002"
          },
          "tokenization": {
            "xlm_roberta": {
              "do_lower_case": false,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "embedding_size": 384
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000002"
        }
      },
      "prefix_strings": {
        "ingest": "passage: ",
        "search": "query: "
      }
    },
    {
      "model_id": "bert-base-uncased_multi_label",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1704744699933,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model models/model_bert-base-uncased_multi_label for task type 'text_classification'",
      "tags": [],
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_classification": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "classification_labels": [
            "Cookie",
            "Candy"
          ],          
          "num_top_classes": 0
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": "bert-base-uncased_single_label",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1704746127799,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model models/model_bert-base-uncased_single_label for task type 'text_classification'",
      "tags": [],
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_classification": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "classification_labels": [
            "Cookie",
            "Candy"
          ], 
          "num_top_classes": 0
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": "ltr-test__5",
      "model_type": "tree_ensemble",
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1735577495233,
      "model_size_bytes": 2509560,
      "estimated_operations": 432,
      "license_level": "platinum",
      "tags": [],
      "input": {
        "field_names": []
      },
      "inference_config": {
        "learning_to_rank": {
          "num_top_feature_importance_values": 0,
          "feature_extractors": [
            {
              "query_extractor": {
                "feature_name": "or_match__name.name-search",
                "query": {
                  "match": {
                    "name.name-search": {
                      "query": "{{query}}"
                    }
                  }
                }
              }
            }
          ]
        }
      }
    },
    {
      "model_id": "distillbert-base-uncased_multi_label",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1704746663969,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model models/model_distillbert-base-uncased_multi_label for task type 'text_classification'",
      "tags": [],
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_classification": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "classification_labels": [
            "Cookie",
            "Candy"
          ], 
          "num_top_classes": 0
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": "distillbert-base-uncased_single_label",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1704746989389,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model models/model_distillbert-base-uncased_single_label for task type 'text_classification'",
      "tags": [],
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_classification": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "classification_labels": [
            "Cookie",
            "Candy"
          ], 
          "num_top_classes": 0
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": "elastic__distilbert-base-uncased-finetuned-conll03-english",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "8.9.1",
      "create_time": 1693516690317,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model elastic/distilbert-base-uncased-finetuned-conll03-english for task type 'ner'",
      "tags": [],
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "ner": {
          "vocabulary": {
            "index": ".ml-inference-native-000001"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "classification_labels": [
            "Cookie",
            "Candy"
          ]
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000001"
        }
      }
    },
    {
      "model_id": "lang_ident_model_1",
      "model_type": "lang_ident",
      "created_by": "_xpack",
      "version": "7.6.0",
      "create_time": 1575548914594,
      "model_size_bytes": 1053992,
      "estimated_operations": 39629,
      "license_level": "basic",
      "description": "Model used for identifying language from arbitrary input text.",
      "tags": [
        "lang_ident",
        "prepackaged"
      ],
      "input": {
        "field_names": [
          "text"
        ]
      },
      "inference_config": {
        "classification": {
          "num_top_classes": 0,
          "top_classes_results_field": "top_classes",
          "results_field": "predicted_value",
          "num_top_feature_importance_values": 0,
          "prediction_field_type": "string"
        }
      }
    },
    {
      "model_id": "ltr-model-xgboost",
      "model_type": "tree_ensemble",
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1716911667904,
      "model_size_bytes": 705144,
      "estimated_operations": 210,
      "license_level": "platinum",
      "tags": [],
      "input": {
        "field_names": [
          "title_bm25",
          "actors_bm25",
          "title_all_terms_bm25",
          "actors_all_terms_bm25",
          "popularity"
        ]
      },
      "inference_config": {
        "learning_to_rank": {
          "num_top_feature_importance_values": 0,
          "feature_extractors": [
            {
              "query_extractor": {
                "feature_name": "title_bm25",
                "query": {
                  "match": {
                    "title": "{{query}}"
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "actors_bm25",
                "query": {
                  "match": {
                    "actors": "{{query}}"
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "title_all_terms_bm25",
                "query": {
                  "match": {
                    "title": {
                      "query": "{{query}}",
                      "minimum_should_match": "100%"
                    }
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "actors_all_terms_bm25",
                "query": {
                  "match": {
                    "actors": {
                      "query": "{{query}}",
                      "minimum_should_match": "100%"
                    }
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "popularity",
                "query": {
                  "script_score": {
                    "query": {
                      "exists": {
                        "field": "popularity"
                      }
                    },
                    "script": {
                      "source": "return doc['popularity'].value;"
                    }
                  }
                }
              }
            }
          ]
        }
      }
    },
    {
      "model_id": "ltr-model-xgboost-movies",
      "model_type": "tree_ensemble",
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1709672105665,
      "model_size_bytes": 730920,
      "estimated_operations": 210,
      "license_level": "platinum",
      "tags": [],
      "input": {
        "field_names": [
          "title_bm25",
          "actors_bm25",
          "title_all_terms_bm25",
          "actors_all_terms_bm25",
          "popularity"
        ]
      },
      "inference_config": {
        "learning_to_rank": {
          "num_top_feature_importance_values": 0,
          "feature_extractors": [
            {
              "query_extractor": {
                "feature_name": "title_bm25",
                "query": {
                  "match": {
                    "title": "{{query}}"
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "actors_bm25",
                "query": {
                  "match": {
                    "actors": "{{query}}"
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "title_all_terms_bm25",
                "query": {
                  "match": {
                    "title": {
                      "query": "{{query}}",
                      "minimum_should_match": "100%"
                    }
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "actors_all_terms_bm25",
                "query": {
                  "match": {
                    "actors": {
                      "query": "{{query}}",
                      "minimum_should_match": "100%"
                    }
                  }
                }
              }
            },
            {
              "query_extractor": {
                "feature_name": "popularity",
                "query": {
                  "script_score": {
                    "query": {
                      "exists": {
                        "field": "popularity"
                      }
                    },
                    "script": {
                      "source": "return doc['popularity'].value;"
                    }
                  }
                }
              }
            }
          ]
        }
      }
    },
    {
      "model_id": "sentence-transformers__msmarco-minilm-l-12-v3",
      "model_type": "pytorch",
      "created_by": "api_user",
      "version": "12.0.0",
      "create_time": 1707754684673,
      "model_size_bytes": 0,
      "estimated_operations": 0,
      "license_level": "platinum",
      "description": "Model sentence-transformers/msmarco-MiniLM-L-12-v3 for task type 'text_embedding'",
      "tags": [],
      "metadata": {
        "model_aliases": [
          "ltr-test"
        ]
      },
      "input": {
        "field_names": [
          "text_field"
        ]
      },
      "inference_config": {
        "text_embedding": {
          "vocabulary": {
            "index": ".ml-inference-native-000002"
          },
          "tokenization": {
            "bert": {
              "do_lower_case": true,
              "with_special_tokens": true,
              "max_sequence_length": 512,
              "truncate": "first",
              "span": -1
            }
          },
          "embedding_size": 384
        }
      },
      "location": {
        "index": {
          "name": ".ml-inference-native-000002"
        }
      }
    }
  ]
}

[l-trotta]

@yulinxp thankj you so much for the info, turns out we were missing some fields from the common specification we use to generate the clients, I'll push a fix there and update the java client code.

[l-trotta]

fixed in elastic/elasticsearch-specification#4095, will be part of the next patch/release

[yulinxp]

Will it be fixed in 8.16 version? Can you please provide the release version? Thanks!

[l-trotta]

Unfortunately we weren't fast enough to have this fix ready for 8.16.6, which should be the last patch coming out for 8.16. So it will surely be in 8.17.5 and following versions, not sure about 8.16, I'll see if we can release one last patch.

[l-trotta]

hey @yulinxp good news, 8.16.7 should be available tomorrow with the fix!