langchain_community.embeddings.vertexai.VertexAIEmbeddings

class langchain_community.embeddings.vertexai.VertexAIEmbeddings[source]

Bases: _VertexAICommon, Embeddings

[Deprecated] 谷歌云VertexAI嵌入模型。

Notes

Deprecated since version 0.0.12.

初始化sentence_transformer。

param credentials: Any = None

要使用的默认自定义凭据（google.auth.credentials.Credentials）

param location: str = 'us-central1'

用于进行API调用时使用的默认位置。

param max_output_tokens: int = 128

Token limit确定了一个提示输出的文本的最大数量。

param max_retries: int = 6

生成时最大重试次数。

param model_name: str [Required]

基础模型名称。

param n: int = 1

每个提示生成多少个完成。

param project: Optional[str] = None

在进行Vertex API调用时要使用的默认GCP项目。

param request_parallelism: int = 5

允许发送给VertexAI模型的请求的并行度。

param show_progress_bar: bool = False

是否显示tqdm进度条。必须安装`tqdm`。

param stop: Optional[List[str]] = None

生成时使用的可选停用词列表。

param streaming: bool = False

是否要流式传输结果。

param temperature: float = 0.0

采样温度，它控制了在标记选择中的随机程度。

param top_k: int = 40

模型如何选择输出的标记，下一个标记是从

param top_p: float = 0.95

令牌按从最有可能到最不可能的顺序选择，直到它们的总和达到

async aembed_documents(texts: List[str]) → List[List[float]]

Asynchronous 嵌入搜索文档。

Parameters

texts (List[str]) –

Return type

List[List[float]]

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

Asynchronous 嵌入查询文本。

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]

嵌入字符串列表。

参数：

texts：List[str] 要嵌入的字符串列表。 batch_size：[int] 发送到模型的嵌入批大小。

如果为零，则将在第一次请求时动态检测最大批大小，从250开始，逐渐减少到5。

embeddings_task_type：[str] 可选的嵌入任务类型，
以下为其中一种：

RETRIEVAL_QUERY - 文本用作搜索/检索设置中的查询。 RETRIEVAL_DOCUMENT - 文本用作搜索/检索设置中的文档。 SEMANTIC_SIMILARITY - 嵌入将用于语义文本相似度（STS）。 CLASSIFICATION - 嵌入将用于分类。 CLUSTERING - 嵌入将用于聚类。

返回：

每个文本的嵌入列表。

Parameters

  • texts (List[str]) –

  • batch_size (int) –

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

Return type

List[List[float]]

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

嵌入文档列表。

参数：

texts: List[str] 要嵌入的文本列表。 batch_size: [int] 发送到模型的嵌入的批处理大小。

如果为零，则将在第一次请求时动态检测到最大的批处理大小，从250开始，逐渐减少到5。

返回：

每个文本的嵌入列表。

Parameters

  • texts (List[str]) –

  • batch_size (int) –

Return type

List[List[float]]

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

嵌入文本。

参数:

text: 要嵌入的文本。

返回:

文本的嵌入。

Parameters

text (str) –

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