Source code for langchain.chains.router.embedding_router
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
from pydantic import Extra
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.router.base import RouterChain
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
[docs]class EmbeddingRouterChain(RouterChain):
    """Chain that uses embeddings to route between options."""
    vectorstore: VectorStore
    routing_keys: List[str] = ["query"]
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
    @property
    def input_keys(self) -> List[str]:
        """Will be whatever keys the LLM chain prompt expects.
        :meta private:
        """
        return self.routing_keys
    def _call(
        self,
        inputs: Dict[str, Any],
        run_manager: Optional[CallbackManagerForChainRun] = None,
    ) -> Dict[str, Any]:
        _input = ", ".join([inputs[k] for k in self.routing_keys])
        results = self.vectorstore.similarity_search(_input, k=1)
        return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
[docs]    @classmethod
    def from_names_and_descriptions(
        cls,
        names_and_descriptions: Sequence[Tuple[str, Sequence[str]]],
        vectorstore_cls: Type[VectorStore],
        embeddings: Embeddings,
        **kwargs: Any,
    ) -> EmbeddingRouterChain:
        """Convenience constructor."""
        documents = []