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from typing import Any, List, Optional
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra
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
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()
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()
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