langchain_community.embeddings.localai.LocalAIEmbeddings

class langchain_community.embeddings.localai.LocalAIEmbeddings

Bases: BaseModel, Embeddings

本地AI嵌入模型。

由于LocalAI和OpenAI在API之间具有1:1的兼容性，因此这个类使用``openai`` Python包的``openai.Embedding``作为其客户端。 因此，您应该已经安装了``openai`` Python包，并通过将环境变量``OPENAI_API_KEY``设置为一个随机字符串来解除它的影响。 您还需要指定``OPENAI_API_BASE``以指向您的LocalAI服务端点。

示例:

from langchain_community.embeddings import LocalAIEmbeddings
openai = LocalAIEmbeddings(
    openai_api_key="random-string",
    openai_api_base="http://localhost:8080"
)

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 chunk_size: int = 1000

每个批次中嵌入的最大文本数量

param deployment: str = 'text-embedding-ada-002'

param disallowed_special: Union[Literal['all'], Set[str], Sequence[str]] = 'all'

param embedding_ctx_length: int = 8191

一次嵌入的最大令牌数。

param headers: Any = None

param max_retries: int = 6

生成时最大的重试次数。

param model: str = 'text-embedding-ada-002'

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

param openai_api_key: Optional[str] = None

param openai_api_version: Optional[str] = None

param openai_organization: Optional[str] = None

param openai_proxy: Optional[str] = None

param request_timeout: Optional[Union[float, Tuple[float, float]]] = None

本地AI请求的超时时间（秒）。

param show_progress_bar: bool = False

在嵌入时是否显示进度条。

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

调用LocalAI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]]

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

调用LocalAI的嵌入端点异步进行嵌入查询文本。

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LocalAI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，则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LocalAI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

Examples using LocalAIEmbeddings

• if you are behind an explicit proxy, you can use the OPENAI_PROXY environment variable to pass through