langchain/libs/experimental/langchain_experimental/plan_and_execute/schema.py
Nuno Campos c0d67420e5
Use a submodule for pydantic v1 compat (#9371)
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2023-08-17 16:35:49 +01:00

64 lines
1.4 KiB
Python

from abc import abstractmethod
from typing import List, Tuple
from langchain.schema import BaseOutputParser
from langchain_experimental.pydantic_v1 import BaseModel, Field
class Step(BaseModel):
"""Step."""
value: str
"""The value."""
class Plan(BaseModel):
"""Plan."""
steps: List[Step]
"""The steps."""
class StepResponse(BaseModel):
"""Step response."""
response: str
"""The response."""
class BaseStepContainer(BaseModel):
"""Base step container."""
@abstractmethod
def add_step(self, step: Step, step_response: StepResponse) -> None:
"""Add step and step response to the container."""
@abstractmethod
def get_final_response(self) -> str:
"""Return the final response based on steps taken."""
class ListStepContainer(BaseStepContainer):
"""List step container."""
steps: List[Tuple[Step, StepResponse]] = Field(default_factory=list)
"""The steps."""
def add_step(self, step: Step, step_response: StepResponse) -> None:
self.steps.append((step, step_response))
def get_steps(self) -> List[Tuple[Step, StepResponse]]:
return self.steps
def get_final_response(self) -> str:
return self.steps[-1][1].response
class PlanOutputParser(BaseOutputParser):
"""Plan output parser."""
@abstractmethod
def parse(self, text: str) -> Plan:
"""Parse into a plan."""