AzureOpenAIEmbeddingSkill Class

  • Reference

Allows you to generate a vector embedding for a given text input using the Azure OpenAI resource.

All required parameters must be populated in order to send to server.

Inheritance
azure.search.documents.indexes._generated.models._models_py3.SearchIndexerSkill
AzureOpenAIEmbeddingSkill
azure.search.documents.indexes._generated.models._models_py3.AzureOpenAIVectorizerParameters
AzureOpenAIEmbeddingSkill

Constructor

AzureOpenAIEmbeddingSkill(*, inputs: List[_models.InputFieldMappingEntry], outputs: List[_models.OutputFieldMappingEntry], resource_url: str | None = None, deployment_name: str | None = None, api_key: str | None = None, auth_identity: _models.SearchIndexerDataIdentity | None = None, model_name: str | _models.AzureOpenAIModelName | None = None, name: str | None = None, description: str | None = None, context: str | None = None, dimensions: int | None = None, **kwargs: Any)

Keyword-Only Parameters

Name Description
resource_url
str

The resource URI of the Azure OpenAI resource.
deployment_name
str

ID of the Azure OpenAI model deployment on the designated resource.
api_key
str

API key of the designated Azure OpenAI resource.
auth_identity
SearchIndexerDataIdentity

The user-assigned managed identity used for outbound connections.
model_name
str or AzureOpenAIModelName

The name of the embedding model that is deployed at the provided deploymentId path. Known values are: "text-embedding-ada-002", "text-embedding-3-large", and "text-embedding-3-small".
name
str

The name of the skill which uniquely identifies it within the skillset. A skill with no name defined will be given a default name of its 1-based index in the skills array, prefixed with the character '#'.
description
str

The description of the skill which describes the inputs, outputs, and usage of the skill.
context
str

Represents the level at which operations take place, such as the document root or document content (for example, /document or /document/content). The default is /document.
inputs
list[InputFieldMappingEntry]

Inputs of the skills could be a column in the source data set, or the output of an upstream skill. Required.
outputs
list[OutputFieldMappingEntry]

The output of a skill is either a field in a search index, or a value that can be consumed as an input by another skill. Required.
dimensions
int

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

Variables

Name Description
resource_url
str

The resource URI of the Azure OpenAI resource.
deployment_name
str

ID of the Azure OpenAI model deployment on the designated resource.
api_key
str

API key of the designated Azure OpenAI resource.
auth_identity
SearchIndexerDataIdentity

The user-assigned managed identity used for outbound connections.
model_name
str or AzureOpenAIModelName

The name of the embedding model that is deployed at the provided deploymentId path. Known values are: "text-embedding-ada-002", "text-embedding-3-large", and "text-embedding-3-small".
odata_type
str

A URI fragment specifying the type of skill. Required.
name
str

The name of the skill which uniquely identifies it within the skillset. A skill with no name defined will be given a default name of its 1-based index in the skills array, prefixed with the character '#'.
description
str

The description of the skill which describes the inputs, outputs, and usage of the skill.
context
str

Represents the level at which operations take place, such as the document root or document content (for example, /document or /document/content). The default is /document.
inputs
list[InputFieldMappingEntry]

Inputs of the skills could be a column in the source data set, or the output of an upstream skill. Required.
outputs
list[OutputFieldMappingEntry]

The output of a skill is either a field in a search index, or a value that can be consumed as an input by another skill. Required.
dimensions
int

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

Methods

as_dict

Return a dict that can be serialized using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains 'type' with the msrest type and 'key' with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

  • attribute_transformer

  • full_restapi_key_transformer

  • last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.
deserialize

Parse a str using the RestAPI syntax and return a model.
enable_additional_properties_sending
from_dict

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)
is_xml_model
serialize

Return the JSON that would be sent to server from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

as_dict

Return a dict that can be serialized using json.dump.

Advanced usage might optionally use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains 'type' with the msrest type and 'key' with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

  • attribute_transformer

  • full_restapi_key_transformer

  • last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

as_dict(keep_readonly: bool = True, key_transformer: ~typing.Callable[[str, ~typing.Dict[str, ~typing.Any], ~typing.Any], ~typing.Any] = <function attribute_transformer>, **kwargs: ~typing.Any) -> MutableMapping[str, Any]

Parameters

Name Description
key_transformer
<xref:function>

A key transformer function.
keep_readonly
Default value: True

Returns

Type Description
dict

A dict JSON compatible object

deserialize

Parse a str using the RestAPI syntax and return a model.

deserialize(data: Any, content_type: str | None = None) -> ModelType

Parameters

Name Description
data
Required
str

A str using RestAPI structure. JSON by default.
content_type
str

JSON by default, set application/xml if XML.

Default value: None

Returns

Type Description

An instance of this model

Exceptions

Type Description
DeserializationError if something went wrong

enable_additional_properties_sending 

enable_additional_properties_sending() -> None

from_dict

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

from_dict(data: Any, key_extractors: Callable[[str, Dict[str, Any], Any], Any] | None = None, content_type: str | None = None) -> ModelType

Parameters

Name Description
data
Required
dict

A dict using RestAPI structure
content_type
str

JSON by default, set application/xml if XML.

Default value: None
key_extractors
Default value: None

Returns

Type Description

An instance of this model

Exceptions

Type Description
DeserializationError if something went wrong

is_xml_model 

is_xml_model() -> bool

serialize

Return the JSON that would be sent to server from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

serialize(keep_readonly: bool = False, **kwargs: Any) -> MutableMapping[str, Any]

Parameters

Name Description
keep_readonly
bool

If you want to serialize the readonly attributes

Default value: False

Returns

Type Description
dict

A dict JSON compatible object