Source code for langchain_experimental.plan_and_execute.planners.base
from abc import abstractmethod
from typing import Any, List, Optional
from langchain.chains.llm import LLMChain
from langchain_core.callbacks.manager import Callbacks
from langchain_experimental.plan_and_execute.schema import Plan, PlanOutputParser
from langchain_experimental.pydantic_v1 import BaseModel
[docs]class BasePlanner(BaseModel):
"""Base planner."""
[docs] @abstractmethod
def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
"""Given input, decide what to do."""
[docs] @abstractmethod
async def aplan(
self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
) -> Plan:
"""Given input, asynchronously decide what to do."""
[docs]class LLMPlanner(BasePlanner):
"""LLM planner."""
llm_chain: LLMChain
"""The LLM chain to use."""
output_parser: PlanOutputParser
"""The output parser to use."""
stop: Optional[List] = None
"""The stop list to use."""
[docs] def plan(self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any) -> Plan:
"""Given input, decide what to do."""
llm_response = self.llm_chain.run(**inputs, stop=self.stop, callbacks=callbacks)
return self.output_parser.parse(llm_response)
[docs] async def aplan(
self, inputs: dict, callbacks: Callbacks = None, **kwargs: Any
) -> Plan:
"""Given input, asynchronously decide what to do."""
llm_response = await self.llm_chain.arun(
**inputs, stop=self.stop, callbacks=callbacks
)
return self.output_parser.parse(llm_response)