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# `langchain_google_vertexai.embeddings`.VertexAIEmbeddings

class langchain_google_vertexai.embeddings.VertexAIEmbeddings

  Bases: `_VertexAICommon`, `Embeddings`

  Google Cloud VertexAI embedding models.

  Initialize the sentence_transformer.

  param api_endpoint: Optional[str] = None

    The desired API endpoint, e.g., us-central1-aiplatform.googleapis.com

  param api_transport: Optional[str] = None

    The desired API transport method, can be either ‘grpc’ or ‘rest’

  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: Optional[int] = None (alias 'max_tokens')

    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 = None (alias 'model')

    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 safety_settings: Optional[Dict[HarmCategory, HarmBlockThreshold]] = None

    The default safety settings to use for all generations.

    For example:

    > from langchain_google_vertexai import HarmBlockThreshold, HarmCategory

    > safety_settings = {

    >   HarmCategory.HARM_CATEGORY_UNSPECIFIED: HarmBlockThreshold.BLOCK_NONE, HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_ONLY_HIGH, HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE, HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,

    > }

  param stop: Optional[List[str]] = None (alias 'stop_sequences')

    Optional list of stop words to use when generating.

  param streaming: bool = False

    Whether to stream the results or not.

  param temperature: Optional[float] = None

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

  param top_k: Optional[int] = None

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

  param top_p: Optional[float] = None

    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]]

    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]]

    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_image(image_path: str, contextual_text: Optional[str] = None) → List[float]

    Embed an image.

    Parameters

      - image_path (str) – Path to image (local, Google Cloud Storage or web) to generate

      - for. (embeddings) –

      - contextual_text (Optional[str]) – Text to generate embeddings for.

    Returns

      Embedding for the image.

    Return type

      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 from_orm(obj: Any) → Model
    Parameters

      obj (Any) –

    Return type

      Model

  get_num_tokens(text: str) → int

    Get the number of tokens present in the text.

    Useful for checking if an input will fit in a model’s context window.

    Parameters

      text (str) – The string input to tokenize.

    Returns

      The integer number of tokens in the text.

    Return type

      int

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

# Examples using VertexAIEmbeddings

- # Automatically restart kernel after installs so that your environment can access the new packages

- @markdown Please specify a source for demo purpose.

- Google

- TODO : Set values as per your requirements

- google_vertex_ai_palm.md