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# `langchain_community.embeddings.aleph_alpha`.AlephAlphaSymmetricSemanticEmbedding

class langchain_community.embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding

  Bases: `AlephAlphaAsymmetricSemanticEmbedding`

  Symmetric version of the Aleph Alpha’s semantic embeddings.

  The main difference is that here, both the documents and queries are embedded with a SemanticRepresentation.Symmetric .. rubric:: Example

      from aleph_alpha import AlephAlphaSymmetricSemanticEmbedding

      embeddings = AlephAlphaAsymmetricSemanticEmbedding(
          normalize=True, compress_to_size=128
      )
      text = "This is a test text"

      doc_result = embeddings.embed_documents([text])
      query_result = embeddings.embed_query(text)

  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.

    Parameters

      texts (List[str]) –

    Return type

      List[List[float]]

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

    Asynchronous Embed query text.

    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]) → List[List[float]]

    Call out to Aleph Alpha’s Document endpoint.

    Parameters

      texts (List[str]) – The list of texts to embed.

    Returns

      List of embeddings, one for each text.

    Return type

      List[List[float]]

  embed_query(text: str) → List[float]

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

    Returns

      Embeddings for the text.

    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 AlephAlphaSymmetricSemanticEmbedding

- Aleph Alpha

- aleph_alpha.md