WatsonxEmbeddings#
- class langchain_ibm.embeddings.WatsonxEmbeddings[source]#
Bases:
BaseModel,EmbeddingsIBM watsonx.ai embedding models.
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 apikey: SecretStr | None [Optional]#
API key to the Watson Machine Learning or CPD instance.
- param instance_id: SecretStr | None [Optional]#
Instance_id of the CPD instance.
- param model_id: str [Required]#
Type of model to use.
- param params: dict | None = None#
Model parameters to use during request generation.
- param password: SecretStr | None [Optional]#
Password to the CPD instance.
- param project_id: str | None = None#
ID of the Watson Studio project.
- param space_id: str | None = None#
ID of the Watson Studio space.
- param token: SecretStr | None [Optional]#
Token to the CPD instance.
- param url: SecretStr [Optional]#
URL to the Watson Machine Learning or CPD instance.
- param username: SecretStr | None [Optional]#
Username to the CPD instance.
- param verify: str | bool | None = None#
You can pass one of following as verify: * the path to a CA_BUNDLE file * the path of directory with certificates of trusted CAs * True - default path to truststore will be taken * False - no verification will be made
- param version: SecretStr | None = None#
Version of the CPD instance.
- async aembed_documents(texts: list[str]) list[list[float]]#
Asynchronous Embed search docs.
- Parameters:
texts (list[str]) – List of text to embed.
- Returns:
List of embeddings.
- Return type:
list[list[float]]
- async aembed_query(text: str) list[float]#
Asynchronous Embed query text.
- Parameters:
text (str) – Text to embed.
- Returns:
Embedding.
- Return type:
list[float]