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 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={})¶