langchain_community.embeddings.vertexai.VertexAIEmbeddings

class langchain_community.embeddings.vertexai.VertexAIEmbeddings

Bases: _VertexAICommon, Embeddings

[Deprecated] Google Cloud VertexAI embedding models.[Deprecated] Google Cloud VertexAI embedding models.

Notes

Deprecated since version 0.0.12.

Initialize the sentence_transformer.

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 [Required]

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 show_progress_bar: bool = False

Whether to show a tqdm progress bar. Must have tqdm installed.

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(texts: List[str], batch_size: int = 0, embeddings_task_type: Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING']] = None) → List[List[float]]

Embed a list of strings.

Parameters

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

• batch_size – [int] The batch size of embeddings to send to the model. If zero, then the largest batch size will be detected dynamically at the first request, starting from 250, down to 5.

• embeddings_task_type –

  [str] optional embeddings task type, one of the following

  RETRIEVAL_QUERY - Text is a query

  in a search/retrieval setting.

  RETRIEVAL_DOCUMENT - Text is a document

  in a search/retrieval setting.

  SEMANTIC_SIMILARITY - Embeddings will be used

  for Semantic Textual Similarity (STS).

  CLASSIFICATION - Embeddings will be used for classification. CLUSTERING - Embeddings will be used for clustering.

Returns

List of embeddings, one for each text.

embed_documents(texts: List[str], batch_size: int = 0) → List[List[float]]

Embed a list of documents.

Parameters

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

• batch_size – [int] The batch size of embeddings to send to the model. If zero, then the largest batch size will be detected dynamically at the first request, starting from 250, down to 5.

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