Source code for langchain_experimental.tot.prompts

import json
from textwrap import dedent
from typing import List

from langchain.prompts import PromptTemplate
from langchain.schema import BaseOutputParser

from langchain_experimental.tot.thought import ThoughtValidity


[docs]def get_cot_prompt() -> PromptTemplate: return PromptTemplate( template_format="jinja2", input_variables=["problem_description", "thoughts"], template=dedent( """ You are an intelligent agent that is generating one thought at a time in a tree of thoughts setting. PROBLEM {{problem_description}} {% if thoughts %} THOUGHTS {% for thought in thoughts %} {{ thought }} {% endfor %} {% endif %} Let's think step by step. """ ).strip(), )
[docs]class JSONListOutputParser(BaseOutputParser): """Class to parse the output of a PROPOSE_PROMPT response.""" @property def _type(self) -> str: return "json_list"
[docs] def parse(self, text: str) -> List[str]: """Parse the output of an LLM call.""" json_string = text.split("```json")[1].strip().strip("```").strip() try: return json.loads(json_string) except json.JSONDecodeError: return []
[docs]def get_propose_prompt() -> PromptTemplate: return PromptTemplate( template_format="jinja2", input_variables=["problem_description", "thoughts", "n"], output_parser=JSONListOutputParser(), template=dedent( """ You are an intelligent agent that is generating thoughts in a tree of thoughts setting. The output should be a markdown code snippet formatted as a JSON list of strings, including the leading and trailing "```json" and "```": ```json [ "<thought-1>", "<thought-2>", "<thought-3>" ] ``` PROBLEM {{ problem_description }} {% if thoughts %} VALID THOUGHTS {% for thought in thoughts %} {{ thought }} {% endfor %} Possible next {{ n }} valid thoughts based on the last valid thought: {% else %} Possible next {{ n }} valid thoughts based on the PROBLEM: {%- endif -%} """ ).strip(), )
[docs]class CheckerOutputParser(BaseOutputParser):
[docs] def parse(self, text: str) -> ThoughtValidity: """Parse the output of the language model.""" text = text.upper() if "INVALID" in text: return ThoughtValidity.INVALID elif "INTERMEDIATE" in text: return ThoughtValidity.VALID_INTERMEDIATE elif "VALID" in text: return ThoughtValidity.VALID_FINAL else: return ThoughtValidity.INVALID
@property def _type(self) -> str: return "tot_llm_checker_output"
CHECKER_PROMPT = PromptTemplate( input_variables=["problem_description", "thoughts"], template=dedent( """ You are an intelligent agent, validating thoughts of another intelligent agent. PROBLEM {problem_description} THOUGHTS {thoughts} Evaluate the thoughts and respond with one word. - Respond VALID if the last thought is a valid final solution to the problem. - Respond INVALID if the last thought is invalid. - Respond INTERMEDIATE if the last thought is valid but not the final solution to the problem. This chain of thoughts is""" ).strip(), output_parser=CheckerOutputParser(), )