AzureAIEmbeddingsModel#

class langchain_azure_ai.embeddings.inference.AzureAIEmbeddingsModel[source]#

Bases: ModelInferenceService, Embeddings

Azure AI model inference for embeddings.

Examples

If your endpoint supports multiple models, indicate the parameter model_name:

Troubleshooting:: To diagnostic issues with the model, you can enable debug logging:

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param api_version: str | None = None#: The API version to use with Azure. If None, the default version is used.

param client_kwargs: Dict[str, Any] = {}#: Additional keyword arguments to pass to the client.

param credential: str | AzureKeyCredential | TokenCredential | None = None#: The API key or credential to use to connect to the service. If using a project endpoint, this must be of type TokenCredential since only Microsoft EntraID is supported.

param dimensions: int | None = None#: The number of dimensions in the embeddings to generate. If None, the model’s default is used.

param embed_batch_size: int = 1024#: The batch size for embedding requests. The default is 1024.

param endpoint: str | None = None#: The endpoint of the specific service to connect to. If you are connecting to a model, use the URL of the model deployment.

param model_kwargs: Dict[str, Any] = {}#: Additional kwargs model parameters.

param model_name: str | None = None (alias 'model')#: The name of the model to use for inference, if the endpoint is running more than one model. If not, this parameter is ignored.

param project_endpoint: str | None = None#: The project endpoint associated with the AI project. If this is specified, then the endpoint parameter becomes optional and credential has to be of type TokenCredential.

param service: Literal['inference'] = 'inference'#: The type of service to connect to. For Inference Services, use ‘inference’.

classmethod validate_environment( values: Dict, ) → Any#

Validate that required values are present in the environment.

async aembed_documents( texts: list[str], ) → list[list[float]][source]#

Asynchronous Embed search docs.

async aembed_query( text: str, ) → list[float][source]#

Asynchronous Embed query text.

embed_documents( texts: list[str], ) → list[list[float]][source]#

Embed search docs.

embed_query(text: str) → list[float][source]#

Embed query text.