langchain_community.embeddings.embaas.EmbaasEmbeddings¶

class langchain_community.embeddings.embaas.EmbaasEmbeddings[source]¶

Bases: BaseModel, Embeddings

Embaas’s embedding service.

To use, you should have the environment variable EMBAAS_API_KEY set with your API key, or pass it as a named parameter to the constructor.

Example

# initialize with default model and instruction
from langchain_community.embeddings import EmbaasEmbeddings
emb = EmbaasEmbeddings()

# initialize with custom model and instruction
from langchain_community.embeddings import EmbaasEmbeddings
emb_model = "instructor-large"
emb_inst = "Represent the Wikipedia document for retrieval"
emb = EmbaasEmbeddings(
    model=emb_model,
    instruction=emb_inst
)

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 api_url: str = 'https://api.embaas.io/v1/embeddings/'¶

The URL for the embaas embeddings API.

param embaas_api_key: Optional[SecretStr] = None¶

max number of retries for requests

Constraints
  • type = string

  • writeOnly = True

  • format = password

param instruction: Optional[str] = None¶

Instruction used for domain-specific embeddings.

param max_retries: Optional[int] = 3¶

request timeout in seconds

param model: str = 'e5-large-v2'¶

The model used for embeddings.

param timeout: Optional[int] = 30¶
async aembed_documents(texts: List[str]) List[List[float]]¶

Asynchronous Embed search docs.

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

Asynchronous Embed query text.

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

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 – fields to include in new model

  • exclude – fields to exclude from new model, as with values this takes precedence over include

  • update – 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 – set to True to make a deep copy of the model

Returns

new model instance

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.

embed_documents(texts: List[str]) List[List[float]][source]¶

Get embeddings for a list of texts.

Parameters

texts – The list of texts to get embeddings for.

Returns

List of embeddings, one for each text.

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

Get embeddings for a single text.

Parameters

text – The text to get embeddings for.

Returns

List of embeddings.

classmethod from_orm(obj: Any) 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().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model¶
classmethod parse_obj(obj: Any) Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) unicode¶
classmethod update_forward_refs(**localns: Any) None¶

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) Model¶

Examples using EmbaasEmbeddings¶