EmbeddingsVectorizer#

class langchain_redis.cache.EmbeddingsVectorizer(embeddings: Embeddings)[source]#

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.

Attributes

type

Methods

__init__(embeddings)

Create a new model by parsing and validating input data from keyword arguments.

aembed(text[, dtype])

aembed_many(texts[, dtype])

batchify(seq, size[, preprocess])

check_dims(value)

Ensures the dims are a positive integer.

construct([_fields_set])

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.

copy(*[, include, exclude, update, deep])

Duplicate a model, optionally choose which fields to include, exclude and change.

dict(*[, include, exclude, by_alias, ...])

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed(text[, dtype])

embed_many(texts[, dtype])

encode(texts, dtype, **kwargs)

from_orm(obj)

json(*[, include, exclude, by_alias, ...])

Generate a JSON representation of the model, include and exclude arguments as per dict().

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

update_forward_refs(**localns)

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

validate(value)

Parameters:

embeddings (Embeddings)

__init__(embeddings: Embeddings)[source]#

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.

Parameters:

embeddings (Embeddings)

async aembed(text: str, dtype: str | VectorDataType = 'float32', **kwargs: Any) → List[float][source]#
Parameters:

  • text (str)

  • dtype (str | VectorDataType)

  • kwargs (Any)

Return type:

List[float]

async aembed_many(texts: List[str], dtype: str | VectorDataType = 'float32', **kwargs: Any) → List[List[float]][source]#
Parameters:

  • texts (List[str])

  • dtype (str | VectorDataType)

  • kwargs (Any)

Return type:

List[List[float]]

batchify(seq: list, size: int, preprocess: Callable | None = None)#
Parameters:

  • seq (list)

  • size (int)

  • preprocess (Callable | None)

classmethod check_dims(value)#

Ensures the dims are a positive integer.

classmethod construct(_fields_set: SetStr | None = 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 (SetStr | None)

  • values (Any)

Return type:

Model

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

  • include (AbstractSetIntStr | MappingIntStrAny | None) – fields to include in new model

  • exclude (AbstractSetIntStr | MappingIntStrAny | None) – fields to exclude from new model, as with values this takes precedence over include

  • update (DictStrAny | None) – 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: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = 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 (AbstractSetIntStr | MappingIntStrAny | None)

  • exclude (AbstractSetIntStr | MappingIntStrAny | None)

  • by_alias (bool)

  • skip_defaults (bool | None)

  • exclude_unset (bool)

  • exclude_defaults (bool)

  • exclude_none (bool)

Return type:

DictStrAny

embed(text: str, dtype: str | VectorDataType = 'float32', **kwargs: Any) → List[float][source]#
Parameters:

  • text (str)

  • dtype (str | VectorDataType)

  • kwargs (Any)

Return type:

List[float]

embed_many(texts: List[str], dtype: str | VectorDataType = 'float32', **kwargs: Any) → List[List[float]][source]#
Parameters:

  • texts (List[str])

  • dtype (str | VectorDataType)

  • kwargs (Any)

Return type:

List[List[float]]

encode(texts: str | List[str], dtype: str | VectorDataType, **kwargs: Any) → ndarray[source]#
Parameters:

  • texts (str | List[str])

  • dtype (str | VectorDataType)

  • kwargs (Any)

Return type:

ndarray

classmethod from_orm(obj: Any) → Model#
Parameters:

obj (Any)

Return type:

Model

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → str#

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 (AbstractSetIntStr | MappingIntStrAny | None)

  • exclude (AbstractSetIntStr | MappingIntStrAny | None)

  • by_alias (bool)

  • skip_defaults (bool | None)

  • exclude_unset (bool)

  • exclude_defaults (bool)

  • exclude_none (bool)

  • encoder (Callable[[Any], Any] | None)

  • models_as_dict (bool)

  • dumps_kwargs (Any)

Return type:

str

classmethod parse_file(path: str | Path, *, content_type: str = None, encoding: str = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#
Parameters:

  • path (str | Path)

  • content_type (str)

  • encoding (str)

  • proto (Protocol)

  • allow_pickle (bool)

Return type:

Model

classmethod parse_obj(obj: Any) → Model#
Parameters:

obj (Any)

Return type:

Model

classmethod parse_raw(b: str | bytes, *, content_type: str = None, encoding: str = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model#
Parameters:

  • b (str | bytes)

  • content_type (str)

  • encoding (str)

  • proto (Protocol)

  • allow_pickle (bool)

Return type:

Model

classmethod schema(by_alias: bool = True, ref_template: str = '#/definitions/{model}') → DictStrAny#
Parameters:

  • by_alias (bool)

  • ref_template (str)

Return type:

DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/definitions/{model}', **dumps_kwargs: Any) → str#
Parameters:

  • by_alias (bool)

  • ref_template (str)

  • dumps_kwargs (Any)

Return type:

str

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