langchain_community.embeddings.vertexai.VertexAIEmbeddings¶

class langchain_community.embeddings.vertexai.VertexAIEmbeddings[source]¶

Bases: _VertexAICommon, Embeddings

[Deprecated] Google Cloud VertexAI embedding models.

Notes

Deprecated since version langchain-community==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.

Parameters

texts (List[str]) –

Return type

List[List[float]]

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

Asynchronous Embed query text.

Parameters

text (str) –

Return type

List[float]

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

Parameters

  • _fields_set (Optional[SetStr]) –

  • values (Any) –

Return type

Model

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 (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to include in new model

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – fields to exclude from new model, as with values this takes precedence over include

  • update (Optional[DictStrAny]) – 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 (bool) – set to True to make a deep copy of the model

  • self (Model) –

Returns

new model instance

Return type

Model

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.

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

Return type

DictStrAny

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]][source]¶

Embed a list of strings.

Parameters

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

  • batch_size (int) – [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 (Optional[Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING']]) –

    [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.

Return type

List[List[float]]

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

Embed a list of documents.

Parameters

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

  • batch_size (int) – [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.

Return type

List[List[float]]

embed_query(text: str) → List[float][source]¶

Embed a text.

Parameters

text (str) – The text to embed.

Returns

Embedding for the text.

Return type

List[float]

classmethod from_orm(obj: Any) → Model¶
Parameters

obj (Any) –

Return type

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().

Parameters

  • include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) –

  • by_alias (bool) –

  • skip_defaults (Optional[bool]) –

  • exclude_unset (bool) –

  • exclude_defaults (bool) –

  • exclude_none (bool) –

  • encoder (Optional[Callable[[Any], Any]]) –

  • models_as_dict (bool) –

  • dumps_kwargs (Any) –

Return type

unicode

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

  • path (Union[str, Path]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

classmethod parse_obj(obj: Any) → Model¶
Parameters

obj (Any) –

Return type

Model

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

  • b (Union[str, bytes]) –

  • content_type (unicode) –

  • encoding (unicode) –

  • proto (Protocol) –

  • allow_pickle (bool) –

Return type

Model

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

  • by_alias (bool) –

  • ref_template (unicode) –

Return type

DictStrAny

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

  • by_alias (bool) –

  • ref_template (unicode) –

  • dumps_kwargs (Any) –

Return type

unicode

classmethod update_forward_refs(**localns: Any) → None¶

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

Parameters

localns (Any) –

Return type

None

classmethod validate(value: Any) → Model¶
Parameters

value (Any) –

Return type

Model

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

Examples using VertexAIEmbeddings¶