Source code for langchain_qdrant.sparse_embeddings
from abc import ABC, abstractmethod
from typing import List
from langchain_core.runnables.config import run_in_executor
from pydantic import BaseModel, Field
[docs]
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"
)
[docs]
class SparseEmbeddings(ABC):
"""An interface for sparse embedding models to use with Qdrant."""
[docs]
@abstractmethod
def embed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Embed search docs."""
[docs]
@abstractmethod
def embed_query(self, text: str) -> SparseVector:
"""Embed query text."""
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async def aembed_documents(self, texts: List[str]) -> List[SparseVector]:
"""Asynchronous Embed search docs."""
return await run_in_executor(None, self.embed_documents, texts)
[docs]
async def aembed_query(self, text: str) -> SparseVector:
"""Asynchronous Embed query text."""
return await run_in_executor(None, self.embed_query, text)