Source code for langchain_experimental.tot.thought_generation
"""
We provide two strategies for generating thoughts in the Tree of Thoughts (ToT)
framework to avoid repetition:
These strategies ensure that the language model generates diverse and
non-repeating thoughts, which are crucial for problem-solving tasks that require
exploration.
"""
from abc import abstractmethod
from typing import Any, Dict, List, Tuple
from langchain.chains.llm import LLMChain
from langchain.prompts.base import BasePromptTemplate
from langchain_experimental.pydantic_v1 import Field
from langchain_experimental.tot.prompts import COT_PROMPT, PROPOSE_PROMPT
[docs]class BaseThoughtGenerationStrategy(LLMChain):
    """
    Base class for a thought generation strategy.
    """
    c: int = 3
    """The number of children thoughts to propose at each step."""
[docs]    @abstractmethod
    def next_thought(
        self,
        problem_description: str,
        thoughts_path: Tuple[str, ...] = (),
        **kwargs: Any
    ) -> str:
        """
        Generate the next thought given the problem description and the thoughts
        generated so far.
        """
[docs]class SampleCoTStrategy(BaseThoughtGenerationStrategy):
    """
    Sample thoughts from a Chain-of-Thought (CoT) prompt.
    This strategy works better when the thought space is rich, such as when each
    thought is a paragraph. Independent and identically distributed samples
    lead to diversity, which helps to avoid repetition.
    """
    prompt: BasePromptTemplate = COT_PROMPT
[docs]    def next_thought(
        self,
        problem_description: str,
        thoughts_path: Tuple[str, ...] = (),
        **kwargs: Any
    ) -> str: