`langchain_community.embeddings.huggingface`.HuggingFaceInstructEmbeddings¶

class langchain_community.embeddings.huggingface.HuggingFaceInstructEmbeddings[source]¶

Bases: BaseModel, Embeddings

Wrapper around sentence_transformers embedding models.

To use, you should have the sentence_transformers and InstructorEmbedding python packages installed.

Example

from langchain_community.embeddings import HuggingFaceInstructEmbeddings

model_name = "hkunlp/instructor-large"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': True}
hf = HuggingFaceInstructEmbeddings(
    model_name=model_name,
    model_kwargs=model_kwargs,
    encode_kwargs=encode_kwargs
)

Initialize the sentence_transformer.

param cache_folder: Optional[str] = None¶: Path to store models. Can be also set by SENTENCE_TRANSFORMERS_HOME environment variable.

param embed_instruction: str = 'Represent the document for retrieval: '¶: Instruction to use for embedding documents.

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: str = 'hkunlp/instructor-large'¶: Model name to use.

param query_instruction: str = 'Represent the question for retrieving supporting documents: '¶: Instruction to use for embedding query.

param show_progress: bool = False¶: Whether to show a progress bar.

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 instruct 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 instruct model.

Parameters: text (str) – The text to embed.
Returns: Embeddings for the text.
Return type: List[float]

Examples using HuggingFaceInstructEmbeddings¶

langchain_community.embeddings.huggingface.HuggingFaceInstructEmbeddings¶

Examples using HuggingFaceInstructEmbeddings¶

`langchain_community.embeddings.huggingface`.HuggingFaceInstructEmbeddings¶