Source code for langchain_community.embeddings.modelscope_hub

from typing import Any, List, Optional

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

[docs]class ModelScopeEmbeddings(BaseModel, Embeddings): """ModelScopeHub embedding models. To use, you should have the ``modelscope`` python package installed. Example: .. code-block:: python from langchain_community.embeddings import ModelScopeEmbeddings model_id = "damo/nlp_corom_sentence-embedding_english-base" embed = ModelScopeEmbeddings(model_id=model_id, model_revision="v1.0.0") """ embed: Any model_id: str = "damo/nlp_corom_sentence-embedding_english-base" """Model name to use.""" model_revision: Optional[str] = None def __init__(self, **kwargs: Any): """Initialize the modelscope""" super().__init__(**kwargs) try: from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks except ImportError as e: raise ImportError( "Could not import some python packages." "Please install it with `pip install modelscope`." ) from e self.embed = pipeline( Tasks.sentence_embedding, model=self.model_id, model_revision=self.model_revision, ) class Config: """Configuration for this pydantic object.""" extra = Extra.forbid
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Compute doc embeddings using a modelscope embedding model. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ texts = list(map(lambda x: x.replace("\n", " "), texts)) inputs = {"source_sentence": texts} embeddings = self.embed(input=inputs)["text_embedding"] return embeddings.tolist()
[docs] def embed_query(self, text: str) -> List[float]: """Compute query embeddings using a modelscope embedding model. Args: text: The text to embed. Returns: Embeddings for the text. """ text = text.replace("\n", " ") inputs = {"source_sentence": [text]} embedding = self.embed(input=inputs)["text_embedding"][0] return embedding.tolist()