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34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from abc import ABC, abstractmethod
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from langchain_core.runnables.config import run_in_executor
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from pydantic import BaseModel, Field
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class SparseVector(BaseModel, extra="forbid"):
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"""Sparse vector structure."""
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indices: list[int] = Field(..., description="indices must be unique")
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values: list[float] = Field(
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..., description="values and indices must be the same length"
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)
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class SparseEmbeddings(ABC):
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"""An interface for sparse embedding models to use with Qdrant."""
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@abstractmethod
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def embed_documents(self, texts: list[str]) -> list[SparseVector]:
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"""Embed search docs."""
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@abstractmethod
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def embed_query(self, text: str) -> SparseVector:
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"""Embed query text."""
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async def aembed_documents(self, texts: list[str]) -> list[SparseVector]:
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"""Asynchronous Embed search docs."""
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return await run_in_executor(None, self.embed_documents, texts)
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async def aembed_query(self, text: str) -> SparseVector:
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"""Asynchronous Embed query text."""
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return await run_in_executor(None, self.embed_query, text)
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