langchain Architecture Docs
Home / File/ sparse_embeddings.py — langchain Source File

sparse_embeddings.py — langchain Source File

Architecture documentation for sparse_embeddings.py, a python file in the langchain codebase. 3 imports, 0 dependents.

File python CoreAbstractions MessageSchema 3 imports 2 classes

Entity Profile

Dependency Diagram

graph LR
  aa6d3c0b_08ba_9711_9c2f_a65edbd1b33d["sparse_embeddings.py"]
  cccbe73e_4644_7211_4d55_e8fb133a8014["abc"]
  aa6d3c0b_08ba_9711_9c2f_a65edbd1b33d --> cccbe73e_4644_7211_4d55_e8fb133a8014
  2971f9da_6393_a3e3_610e_ace3d35ee978["langchain_core.runnables.config"]
  aa6d3c0b_08ba_9711_9c2f_a65edbd1b33d --> 2971f9da_6393_a3e3_610e_ace3d35ee978
  6e58aaea_f08e_c099_3cc7_f9567bfb1ae7["pydantic"]
  aa6d3c0b_08ba_9711_9c2f_a65edbd1b33d --> 6e58aaea_f08e_c099_3cc7_f9567bfb1ae7
  style aa6d3c0b_08ba_9711_9c2f_a65edbd1b33d fill:#6366f1,stroke:#818cf8,color:#fff

Relationship Graph

Source Code

from abc import ABC, abstractmethod

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)

Domain

Subdomains

Classes

Dependencies

  • abc
  • langchain_core.runnables.config
  • pydantic

Source

Frequently Asked Questions

What does sparse_embeddings.py do?
sparse_embeddings.py is a source file in the langchain codebase, written in python. It belongs to the CoreAbstractions domain, MessageSchema subdomain.
What does sparse_embeddings.py depend on?
sparse_embeddings.py imports 3 module(s): abc, langchain_core.runnables.config, pydantic.
Where is sparse_embeddings.py in the architecture?
sparse_embeddings.py is located at libs/partners/qdrant/langchain_qdrant/sparse_embeddings.py (domain: CoreAbstractions, subdomain: MessageSchema, directory: libs/partners/qdrant/langchain_qdrant).

Analyze Your Own Codebase

Get architecture documentation, dependency graphs, and domain analysis for your codebase in minutes.

Try Supermodel Free