langchain_community.embeddings.vertexai.VertexAIEmbeddings

class langchain_community.embeddings.vertexai.VertexAIEmbeddings

Bases: _VertexAICommon, Embeddings

Google Cloud VertexAI embedding models.

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

Raises ValidationError if the input data cannot be parsed to form a valid model.

param credentials: Any = None

The default custom credentials (google.auth.credentials.Credentials) to use

param location: str = 'us-central1'

The default location to use when making API calls.

param max_output_tokens: int = 128

Token limit determines the maximum amount of text output from one prompt.

param max_retries: int = 6

The maximum number of retries to make when generating.

param model_name: str = 'textembedding-gecko'

Underlying model name.

param n: int = 1

How many completions to generate for each prompt.

param project: Optional[str] = None

The default GCP project to use when making Vertex API calls.

param request_parallelism: int = 5

The amount of parallelism allowed for requests issued to VertexAI models.

param stop: Optional[List[str]] = None

Optional list of stop words to use when generating.

param streaming: bool = False

Whether to stream the results or not.

param temperature: float = 0.0

Sampling temperature, it controls the degree of randomness in token selection.

param top_k: int = 40

How the model selects tokens for output, the next token is selected from

param top_p: float = 0.95

Tokens are selected from most probable to least until the sum of their

async aembed_documents(texts: List[str]) → List[List[float]]

Asynchronous Embed search docs.

async aembed_query(text: str) → List[float]

Asynchronous Embed query text.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed_documents(texts: List[str], batch_size: int = 5) → List[List[float]]

Embed a list of strings. Vertex AI currently sets a max batch size of 5 strings.

Parameters

• texts – List[str] The list of strings to embed.

• batch_size – [int] The batch size of embeddings to send to the model

Returns

List of embeddings, one for each text.

embed_query(text: str) → List[float]

Embed a text.

Parameters

text – The text to embed.

Returns

Embedding for the text.

classmethod from_orm(obj: Any) → Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod parse_obj(obj: Any) → Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model

property is_codey_model: bool

task_executor: ClassVar[Optional[Executor]] = FieldInfo(exclude=True, extra={})

Examples using VertexAIEmbeddings

• Google Vertex AI PaLM