langchain_community.embeddings.openvino.OpenVINOEmbeddings¶

class langchain_community.embeddings.openvino.OpenVINOEmbeddings[source]¶

Bases: BaseModel, Embeddings

OpenVINO embedding models.

Example

from langchain_community.embeddings import OpenVINOEmbeddings

model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'CPU'}
encode_kwargs = {'normalize_embeddings': True}
ov = OpenVINOEmbeddings(
    model_name_or_path=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

Initialize the sentence_transformer.

param encode_kwargs: Dict[str, Any] [Optional]¶

Keyword arguments to pass when calling the encode method of the model.

param model_kwargs: Dict[str, Any] [Optional]¶

Keyword arguments to pass to the model.

param model_name_or_path: str [Required]¶

HuggingFace model id.

param ov_model: Any = None¶

OpenVINO model object.

param show_progress: bool = False¶

Whether to show a progress bar.

param tokenizer: Any = None¶

Tokenizer for embedding model.

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]¶

Compute doc embeddings using a HuggingFace transformer model.

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]¶

Compute query embeddings using a HuggingFace transformer model.

Parameters

text (str) – The text to embed.

Returns

Embeddings for the text.

Return type

List[float]

encode(sentences: Any, batch_size: int = 4, show_progress_bar: bool = False, convert_to_numpy: bool = True, convert_to_tensor: bool = False, mean_pooling: bool = False, normalize_embeddings: bool = True) → Any[source]¶

Computes sentence embeddings.

Parameters

  • sentences (Any) – the sentences to embed.

  • batch_size (int) – the batch size used for the computation.

  • show_progress_bar (bool) – Whether to output a progress bar.

  • convert_to_numpy (bool) – Whether the output should be a list of numpy vectors.

  • convert_to_tensor (bool) – Whether the output should be one large tensor.

  • mean_pooling (bool) – Whether to pool returned vectors.

  • normalize_embeddings (bool) – Whether to normalize returned vectors.

Returns

By default, a 2d numpy array with shape [num_inputs, output_dimension].

Return type

Any

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

save_model(model_path: str) → bool[source]¶
Parameters

model_path (str) –

Return type

bool

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 OpenVINOEmbeddings¶