mirror of
https://github.com/hwchase17/langchain
synced 2024-10-29 17:07:25 +00:00
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
from abc import abstractmethod
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from typing import Any, List, Optional
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from langchain.callbacks.manager import Callbacks
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from langchain.chains.llm import LLMChain
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from pydantic_v1 import BaseModel
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from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
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class BasePlanner(BaseModel):
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"""Base planner."""
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@abstractmethod
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def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
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"""Given input, decide what to do."""
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@abstractmethod
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async def aplan(
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self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
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) -> Plan:
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"""Given input, asynchronously decide what to do."""
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class LLMPlanner(BasePlanner):
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"""LLM planner."""
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llm_chain: LLMChain
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"""The LLM chain to use."""
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output_parser: PlanOutputParser
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"""The output parser to use."""
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stop: Optional[List] = None
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"""The stop list to use."""
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def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
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"""Given input, decide what to do."""
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llm_response = self.llm_chain.run(**inputs, stop=self.stop, callbacks=callbacks)
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return self.output_parser.parse(llm_response)
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async def aplan(
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self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
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) -> Plan:
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"""Given input, asynchronously decide what to do."""
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llm_response = await self.llm_chain.arun(
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**inputs, stop=self.stop, callbacks=callbacks
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)
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return self.output_parser.parse(llm_response)
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