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


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"]}

    @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 = []
        for name, descriptions in names_and_descriptions:
            for description in descriptions:
                documents.append(
                    Document(page_content=description, metadata={"name": name})
                )
        vectorstore = vectorstore_cls.from_documents(documents, embeddings)
        return cls(vectorstore=vectorstore, **kwargs)
