VertexAIEmbeddings

class langchain_community.embeddings.vertexai.VertexAIEmbeddings

Bases: _VertexAICommon, Embeddings

Deprecated since version 0.0.12: Use :class:`~langchain_google_vertexai.VertexAIEmbeddings` instead. It will be removed in None==1.0.

Google Cloud VertexAI embedding models.

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: str | None = 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: List[str] | None = 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]) – 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]

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

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 (Literal['RETRIEVAL_QUERY', 'RETRIEVAL_DOCUMENT', 'SEMANTIC_SIMILARITY', 'CLASSIFICATION', 'CLUSTERING'] | None) –

  [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]]

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]

Embed a text.

Parameters:

text (str) – The text to embed.

Returns:

Embedding for the text.

Return type:

List[float]

classmethod validate_environment(values: Dict) → Dict

Validates that the python package exists in environment.

Parameters:

values (Dict)

Return type:

Dict

property is_codey_model: bool

task_executor: ClassVar[Executor | None] = FieldInfo(annotation=NoneType, required=False, default=None, exclude=True)