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]) – List of text to embed.
- Returns
List of embeddings.
- Return type
List[List[float]]
- async aembed_query(text: str) List[float] ¶
Asynchronous Embed query text.
- Parameters
text (str) – Text to embed.
- Returns
Embedding.
- Return type
List[float]
- 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