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langchain[patch]: Add async methods to EmbeddingRouterChain (#19603)
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@@ -2,7 +2,10 @@ from __future__ import annotations
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from typing import Any, Dict, List, Optional, Sequence, Tuple, Type
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from langchain_core.callbacks import CallbackManagerForChainRun
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from langchain_core.callbacks import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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)
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import Extra
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@@ -40,6 +43,15 @@ class EmbeddingRouterChain(RouterChain):
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results = self.vectorstore.similarity_search(_input, k=1)
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return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
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async def _acall(
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self,
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inputs: Dict[str, Any],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, Any]:
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_input = ", ".join([inputs[k] for k in self.routing_keys])
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results = await self.vectorstore.asimilarity_search(_input, k=1)
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return {"next_inputs": inputs, "destination": results[0].metadata["name"]}
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@classmethod
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def from_names_and_descriptions(
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cls,
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@@ -57,3 +69,21 @@ class EmbeddingRouterChain(RouterChain):
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)
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vectorstore = vectorstore_cls.from_documents(documents, embeddings)
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return cls(vectorstore=vectorstore, **kwargs)
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@classmethod
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async def afrom_names_and_descriptions(
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cls,
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names_and_descriptions: Sequence[Tuple[str, Sequence[str]]],
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vectorstore_cls: Type[VectorStore],
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embeddings: Embeddings,
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**kwargs: Any,
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) -> EmbeddingRouterChain:
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"""Convenience constructor."""
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documents = []
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for name, descriptions in names_and_descriptions:
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for description in descriptions:
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documents.append(
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Document(page_content=description, metadata={"name": name})
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)
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vectorstore = await vectorstore_cls.afrom_documents(documents, embeddings)
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return cls(vectorstore=vectorstore, **kwargs)
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