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73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
"""Wrapper around ModelScopeHub embedding models."""
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from typing import Any, List
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from pydantic import BaseModel, Extra
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from langchain.embeddings.base import Embeddings
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class ModelScopeEmbeddings(BaseModel, Embeddings):
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"""Wrapper around modelscope_hub embedding models.
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To use, you should have the ``modelscope`` python package installed.
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Example:
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.. code-block:: python
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from langchain.embeddings import ModelScopeEmbeddings
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model_id = "damo/nlp_corom_sentence-embedding_english-base"
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embed = ModelScopeEmbeddings(model_id=model_id)
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"""
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embed: Any
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model_id: str = "damo/nlp_corom_sentence-embedding_english-base"
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"""Model name to use."""
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def __init__(self, **kwargs: Any):
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"""Initialize the modelscope"""
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super().__init__(**kwargs)
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try:
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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self.embed = pipeline(Tasks.sentence_embedding, model=self.model_id)
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except ImportError as e:
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raise ImportError(
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"Could not import some python packages."
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"Please install it with `pip install modelscope`."
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) from e
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Compute doc embeddings using a modelscope embedding model.
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Args:
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texts: The list of texts to embed.
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Returns:
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List of embeddings, one for each text.
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"""
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texts = list(map(lambda x: x.replace("\n", " "), texts))
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inputs = {"source_sentence": texts}
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embeddings = self.embed(input=inputs)["text_embedding"]
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return embeddings.tolist()
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def embed_query(self, text: str) -> List[float]:
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"""Compute query embeddings using a modelscope embedding model.
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Args:
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text: The text to embed.
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Returns:
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Embeddings for the text.
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"""
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text = text.replace("\n", " ")
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inputs = {"source_sentence": [text]}
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embedding = self.embed(input=inputs)["text_embedding"][0]
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return embedding.tolist()
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