Source code for langchain_community.embeddings.text2vec

"""Wrapper around text2vec embedding models."""

from typing import Any, List, Optional

from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel

[docs]class Text2vecEmbeddings(Embeddings, BaseModel): """text2vec embedding models. Install text2vec first, run 'pip install -U text2vec'. The gitbub repository for text2vec is : Example: .. code-block:: python from langchain_community.embeddings.text2vec import Text2vecEmbeddings embedding = Text2vecEmbeddings() embedding.embed_documents([ "This is a CoSENT(Cosine Sentence) model.", "It maps sentences to a 768 dimensional dense vector space.", ]) embedding.embed_query( "It can be used for text matching or semantic search." ) """ model_name_or_path: Optional[str] = None encoder_type: Any = "MEAN" max_seq_length: int = 256 device: Optional[str] = None model: Any = None def __init__( self, *, model: Any = None, model_name_or_path: Optional[str] = None, **kwargs: Any, ): try: from text2vec import SentenceModel except ImportError as e: raise ImportError( "Unable to import text2vec, please install with " "`pip install -U text2vec`." ) from e model_kwargs = {} if model_name_or_path is not None: model_kwargs["model_name_or_path"] = model_name_or_path model = model or SentenceModel(**model_kwargs, **kwargs) super().__init__(model=model, model_name_or_path=model_name_or_path, **kwargs)
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed documents using the text2vec embeddings model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return self.model.encode(texts)
[docs] def embed_query(self, text: str) -> List[float]: """Embed a query using the text2vec embeddings model. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.model.encode(text)