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48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""Interface for embedding models."""
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import asyncio
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from abc import ABC, abstractmethod
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from typing import List
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class RerankEmbeddings(ABC):
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"""Interface for rerank models."""
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@abstractmethod
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def predict(self, query: str, candidates: List[str]) -> List[float]:
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"""Predict the scores of the candidates."""
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async def apredict(self, query: str, candidates: List[str]) -> List[float]:
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"""Asynchronously predict the scores of the candidates."""
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return await asyncio.get_running_loop().run_in_executor(
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None, self.predict, query, candidates
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)
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class Embeddings(ABC):
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"""Interface for embedding models.
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Refer to `Langchain Embeddings <https://github.com/langchain-ai/langchain/tree/
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master/libs/langchain/langchain/embeddings>`_.
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"""
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@abstractmethod
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed search docs."""
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@abstractmethod
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def embed_query(self, text: str) -> List[float]:
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"""Embed query text."""
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async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Asynchronous Embed search docs."""
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return await asyncio.get_running_loop().run_in_executor(
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None, self.embed_documents, texts
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)
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async def aembed_query(self, text: str) -> List[float]:
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"""Asynchronous Embed query text."""
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return await asyncio.get_running_loop().run_in_executor(
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None, self.embed_query, text
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)
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