`langchain_community.embeddings.aleph_alpha`.AlephAlphaSymmetricSemanticEmbedding¶

class langchain_community.embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding[source]¶

Bases: AlephAlphaAsymmetricSemanticEmbedding

The 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]][source]¶

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][source]¶

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

langchain_community.embeddings.aleph_alpha.AlephAlphaSymmetricSemanticEmbedding¶

Examples using AlephAlphaSymmetricSemanticEmbedding¶

`langchain_community.embeddings.aleph_alpha`.AlephAlphaSymmetricSemanticEmbedding¶