langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding

class langchain_community.embeddings.aleph_alpha.AlephAlphaAsymmetricSemanticEmbedding[source]

Bases: BaseModel, Embeddings

Aleph Alpha’s asymmetric semantic embedding.

AA provides you with an endpoint to embed a document and a query. The models were optimized to make the embeddings of documents and the query for a document as similar as possible. To learn more, check out: https://docs.aleph-alpha.com/docs/tasks/semantic_embed/

Example

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 aleph_alpha_api_key: Optional[str] = None

API key for Aleph Alpha API.

param compress_to_size: Optional[int] = None

Should the returned embeddings come back as an original 5120-dim vector, or should it be compressed to 128-dim.

param contextual_control_threshold: Optional[int] = None

Attention control parameters only apply to those tokens that have explicitly been set in the request.

param control_log_additive: bool = True

Apply controls on prompt items by adding the log(control_factor) to attention scores.

param host: str = 'https://api.aleph-alpha.com'

The hostname of the API host. The default one is “https://api.aleph-alpha.com”)

param hosting: Optional[str] = None

Determines in which datacenters the request may be processed. You can either set the parameter to “aleph-alpha” or omit it (defaulting to None). Not setting this value, or setting it to None, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximal availability. Setting it to “aleph-alpha” allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.

param model: str = 'luminous-base'

Model name to use.

param nice: bool = False

Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.

param normalize: Optional[bool] = None

Should returned embeddings be normalized

param request_timeout_seconds: int = 305

Client timeout that will be set for HTTP requests in the requests library’s API calls. Server will close all requests after 300 seconds with an internal server error.

param total_retries: int = 8

The number of retries made in case requests fail with certain retryable status codes. If the last retry fails a corresponding exception is raised. Note, that between retries an exponential backoff is applied, starting with 0.5 s after the first retry and doubling for each retry made. So with the default setting of 8 retries a total wait time of 63.5 s is added between the retries.

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]

Call out to Aleph Alpha’s asymmetric Document endpoint.

Parameters

texts – The list of texts to embed.

Returns

List of embeddings, one for each text.

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

Call out to Aleph Alpha’s asymmetric, query embedding endpoint :param text: The text to embed.

Returns

Embeddings for the text.

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 AlephAlphaAsymmetricSemanticEmbedding