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