Source code for langchain_experimental.plan_and_execute.planners.chat_planner

import re

from langchain.chains import LLMChain
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import SystemMessage
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate

from langchain_experimental.plan_and_execute.planners.base import LLMPlanner
from langchain_experimental.plan_and_execute.schema import (

    "Let's first understand the problem and devise a plan to solve the problem."
    " Please output the plan starting with the header 'Plan:' "
    "and then followed by a numbered list of steps. "
    "Please make the plan the minimum number of steps required "
    "to accurately complete the task. If the task is a question, "
    "the final step should almost always be 'Given the above steps taken, "
    "please respond to the users original question'. "
    "At the end of your plan, say '<END_OF_PLAN>'"

[docs]class PlanningOutputParser(PlanOutputParser): """Planning output parser."""
[docs] def parse(self, text: str) -> Plan: steps = [Step(value=v) for v in re.split("\n\s*\d+\. ", text)[1:]] return Plan(steps=steps)
[docs]def load_chat_planner( llm: BaseLanguageModel, system_prompt: str = SYSTEM_PROMPT ) -> LLMPlanner: """ Load a chat planner. Args: llm: Language model. system_prompt: System prompt. Returns: LLMPlanner """ prompt_template = ChatPromptTemplate.from_messages( [ SystemMessage(content=system_prompt), HumanMessagePromptTemplate.from_template("{input}"), ] ) llm_chain = LLMChain(llm=llm, prompt=prompt_template) return LLMPlanner( llm_chain=llm_chain, output_parser=PlanningOutputParser(), stop=["<END_OF_PLAN>"], )