Added framework to support embedding onnx model

ads/model/__init__.py

@@ -1,29 +1,26 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2021, 2023 Oracle and/or its affiliates.
+# Copyright (c) 2021, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
-from ads.model.generic_model import GenericModel, ModelState
 from ads.model.datascience_model import DataScienceModel
-from ads.model.model_properties import ModelProperties
+from ads.model.deployment.model_deployer import ModelDeployer
+from ads.model.deployment.model_deployment import ModelDeployment
+from ads.model.deployment.model_deployment_properties import ModelDeploymentProperties
 from ads.model.framework.automl_model import AutoMLModel
+from ads.model.framework.embedding_onnx_model import EmbeddingONNXModel
+from ads.model.framework.huggingface_model import HuggingFacePipelineModel
 from ads.model.framework.lightgbm_model import LightGBMModel
 from ads.model.framework.pytorch_model import PyTorchModel
 from ads.model.framework.sklearn_model import SklearnModel
+from ads.model.framework.spark_model import SparkPipelineModel
 from ads.model.framework.tensorflow_model import TensorFlowModel
 from ads.model.framework.xgboost_model import XGBoostModel
-from ads.model.framework.spark_model import SparkPipelineModel
-from ads.model.framework.huggingface_model import HuggingFacePipelineModel
-
-from ads.model.deployment.model_deployer import ModelDeployer
-from ads.model.deployment.model_deployment import ModelDeployment
-from ads.model.deployment.model_deployment_properties import ModelDeploymentProperties
-
+from ads.model.generic_model import GenericModel, ModelState
+from ads.model.model_properties import ModelProperties
+from ads.model.model_version_set import ModelVersionSet, experiment
 from ads.model.serde.common import SERDE
 from ads.model.serde.model_input import ModelInputSerializer
-
-from ads.model.model_version_set import ModelVersionSet, experiment
 from ads.model.service.oci_datascience_model_version_set import (
     ModelVersionSetNotExists,
     ModelVersionSetNotSaved,
@@ -42,6 +39,7 @@
     "XGBoostModel",
     "SparkPipelineModel",
     "HuggingFacePipelineModel",
+    "EmbeddingONNXModel",
     "ModelDeployer",
     "ModelDeployment",
     "ModelDeploymentProperties",

ads/model/artifact.py

@@ -1,28 +1,28 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
-# Copyright (c) 2022, 2023 Oracle and/or its affiliates.
+# Copyright (c) 2022, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import fnmatch
 import importlib
 import os
-import sys
 import shutil
+import sys
 import tempfile
 import uuid
-import fsspec
+from datetime import datetime
 from typing import Dict, Optional, Tuple
+
+import fsspec
+from jinja2 import Environment, PackageLoader
+
+from ads import __version__
 from ads.common import auth as authutil
 from ads.common import logger, utils
 from ads.common.object_storage_details import ObjectStorageDetails
 from ads.config import CONDA_BUCKET_NAME, CONDA_BUCKET_NS
 from ads.model.runtime.env_info import EnvInfo, InferenceEnvInfo, TrainingEnvInfo
 from ads.model.runtime.runtime_info import RuntimeInfo
-from jinja2 import Environment, PackageLoader
-import warnings
-from ads import __version__
-from datetime import datetime
 
 MODEL_ARTIFACT_VERSION = "3.0"
 REQUIRED_ARTIFACT_FILES = ("runtime.yaml", "score.py")
@@ -378,6 +378,45 @@ def prepare_score_py(
         ) as f:
             f.write(scorefn_template.render(context))
 
+    def prepare_schema(self, schema_name: str):
+        """Copies schema to artifact directory.
+
+        Parameters
+        ----------
+        schema_name: str
+            The schema name
+
+        Returns
+        -------
+        None
+
+        Raises
+        ------
+        FileExistsError
+            If `schema_name` doesn't exist.
+        """
+        uri_src = os.path.join(
+            os.path.abspath(os.path.join(os.path.dirname(__file__), "..")),
+            "templates",
+            "schemas",
+            f"{schema_name}",
+        )
+
+        if not os.path.exists(uri_src):
+            raise FileExistsError(
+                f"{schema_name} does not exists. "
+                "Ensure the schema name is valid or specify a different one."
+            )
+
+        uri_dst = os.path.join(self.artifact_dir, os.path.basename(uri_src))
+
+        utils.copy_file(
+            uri_src=uri_src,
+            uri_dst=uri_dst,
+            force_overwrite=True,
+            auth=self.auth,
+        )
+
     def reload(self):
         """Syncs the `score.py` to reload the model and predict function.
 

ads/model/extractor/embedding_onnx_extractor.py

@@ -0,0 +1,80 @@
+#!/usr/bin/env python
+
+# Copyright (c) 2025 Oracle and/or its affiliates.
+# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
+
+from ads.common.decorator.runtime_dependency import (
+    OptionalDependency,
+    runtime_dependency,
+)
+from ads.model.extractor.model_info_extractor import ModelInfoExtractor
+from ads.model.model_metadata import Framework
+
+
+class EmbeddingONNXExtractor(ModelInfoExtractor):
+    """Class that extract model metadata from EmbeddingONNXModel models.
+
+    Attributes
+    ----------
+    model: object
+        The model to extract metadata from.
+
+    Methods
+    -------
+    framework(self) -> str
+        Returns the framework of the model.
+    algorithm(self) -> object
+        Returns the algorithm of the model.
+    version(self) -> str
+        Returns the version of framework of the model.
+    hyperparameter(self) -> dict
+        Returns the hyperparameter of the model.
+    """
+
+    def __init__(self, model=None):
+        self.model = model
+
+    @property
+    def framework(self):
+        """Extracts the framework of the model.
+
+        Returns
+        ----------
+        str:
+           The framework of the model.
+        """
+        return Framework.EMBEDDING_ONNX
+
+    @property
+    def algorithm(self):
+        """Extracts the algorithm of the model.
+
+        Returns
+        ----------
+        object:
+           The algorithm of the model.
+        """
+        return "Embedding_ONNX"
+
+    @property
+    @runtime_dependency(module="onnxruntime", install_from=OptionalDependency.ONNX)
+    def version(self):
+        """Extracts the framework version of the model.
+
+        Returns
+        ----------
+        str:
+           The framework version of the model.
+        """
+        return onnxruntime.__version__
+
+    @property
+    def hyperparameter(self):
+        """Extracts the hyperparameters of the model.
+
+        Returns
+        ----------
+        dict:
+           The hyperparameters of the model.
+        """
+        return None