langchain_community.embeddings.anyscale.AnyscaleEmbeddings

class langchain_community.embeddings.anyscale.AnyscaleEmbeddings[source]

Bases: OpenAIEmbeddings

Anyscale 嵌入式 API。

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param allowed_special: Union[Literal['all'], Set[str]] = {}
param anyscale_api_base: str = 'https://api.endpoints.anyscale.com/v1'

API请求的基础URL路径。

param anyscale_api_key: SecretStr = None

任何规模的端点 API 密钥。

Constraints

  • type = string

  • writeOnly = True

  • format = password

param chunk_size: int = 1000

每个批次中嵌入的最大文本数量

param default_headers: Union[Mapping[str, str], None] = None
param default_query: Union[Mapping[str, object], None] = None
param deployment: Optional[str] = 'text-embedding-ada-002'
param disallowed_special: Union[Literal['all'], Set[str], Sequence[str]] = 'all'
param embedding_ctx_length: int = 500

一次嵌入的最大令牌数。

param headers: Any = None
param http_client: Union[Any, None] = None

可选的 httpx.Client。

param max_retries: int = 2

生成时最大的重试次数。

param model: str = 'thenlper/gte-large'

要使用的模型名称。

param model_kwargs: Dict[str, Any] [Optional]

保存任何在`create`调用中有效但未明确指定的模型参数。

param openai_api_base: Optional[str] = None (alias 'base_url')

API请求的基本URL路径，如果不使用代理或服务模拟器，请留空。

param openai_api_key: Optional[str] = None (alias 'api_key')

如果未提供，将自动从环境变量`OPENAI_API_KEY`中推断。

param openai_api_type: Optional[str] = None
param openai_api_version: Optional[str] = None (alias 'api_version')

如果未提供，将自动从环境变量`OPENAI_API_VERSION`中推断。

param openai_organization: Optional[str] = None (alias 'organization')

如果未提供，将自环境变量`OPENAI_ORG_ID`自动推断。

param openai_proxy: Optional[str] = None
param request_timeout: Optional[Union[float, Tuple[float, float], Any]] = None (alias 'timeout')

请求到OpenAI完成API的超时时间。可以是浮点数、httpx.Timeout或None。

param retry_max_seconds: int = 20

重试之间等待的最大秒数

param retry_min_seconds: int = 4

重试之间等待的最短秒数

param show_progress_bar: bool = False

在嵌入时是否显示进度条。

param skip_empty: bool = False

是否在嵌入时跳过空字符串或引发错误。 默认情况下不跳过。

param tiktoken_enabled: bool = False

将此设置为False，用于嵌入API的非OpenAI实现。

param tiktoken_model_name: Optional[str] = None

在使用这个类时，传递给tiktoken的模型名称。 Tiktoken用于计算文档中令牌的数量，以限制它们在某个特定限制之下。默认情况下，当设置为None时，这将与嵌入模型名称相同。然而，在一些情况下，您可能希望使用这个嵌入类与tiktoken不支持的模型名称一起使用。这可能包括使用Azure嵌入或使用许多提供类似OpenAI API但具有不同模型的模型提供商之一。在这些情况下，为了避免在调用tiktoken时出错，您可以在这里指定要使用的模型名称。

async aembed_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]]

调用OpenAI的嵌入端点异步进行嵌入搜索文档。

参数：

texts：要嵌入的文本列表。 chunk_size：嵌入的块大小。如果为None，则将使用类别指定的块大小。

返回：

每个文本的嵌入列表。

Parameters

  • texts (List[str]) –

  • chunk_size (Optional[int]) –

Return type

List[List[float]]

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

调用OpenAI的嵌入端点异步地为嵌入查询文本。

参数：

text：要嵌入的文本。

返回：

文本的嵌入。

Parameters

text (str) –

Return type

List[float]

classmethod 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_documents(texts: List[str], chunk_size: Optional[int] = 0) → List[List[float]]

调用OpenAI的嵌入端点以获取嵌入搜索文档。

参数：

texts：要嵌入的文本列表。 chunk_size：嵌入的块大小。如果为None，将使用类指定的块大小。

返回：

每个文本的嵌入列表。

Parameters

  • texts (List[str]) –

  • chunk_size (Optional[int]) –

Return type

List[List[float]]

embed_query(text: str) → List[float]

调用OpenAI的嵌入端点来嵌入查询文本。

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 lc_secrets: Dict[str, str]

Examples using AnyscaleEmbeddings