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# 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_core.prompts.base import BasePromptTemplate

    from langchain_experimental.pydantic_v1 import Field
    from langchain_experimental.tot.prompts import get_cot_prompt, get_propose_prompt

    class BaseThoughtGenerationStrategy(LLMChain):
        """
        Base class for a thought generation strategy.
        """

        c: int = 3
        """The number of children thoughts to propose at each step."""

        @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.
            """

    class SampleCoTStrategy(BaseThoughtGenerationStrategy):
        """
        Sample strategy 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 = Field(default_factory=get_cot_prompt)

        def next_thought(
            self,
            problem_description: str,
            thoughts_path: Tuple[str, ...] = (),
            **kwargs: Any,
        ) -> str:
            response_text = self.predict_and_parse(
                problem_description=problem_description, thoughts=thoughts_path, **kwargs
            )
            return response_text if isinstance(response_text, str) else ""

    class ProposePromptStrategy(BaseThoughtGenerationStrategy):
        """
        Strategy that is sequentially using a "propose prompt".

        This strategy works better when the thought space is more constrained, such
        as when each thought is just a word or a line. Proposing different thoughts
        in the same prompt completion helps to avoid duplication.
        """

        prompt: BasePromptTemplate = Field(default_factory=get_propose_prompt)
        tot_memory: Dict[Tuple[str, ...], List[str]] = Field(default_factory=dict)

        def next_thought(
            self,
            problem_description: str,
            thoughts_path: Tuple[str, ...] = (),
            **kwargs: Any,
        ) -> str:
            if thoughts_path not in self.tot_memory or not self.tot_memory[thoughts_path]:
                new_thoughts = self.predict_and_parse(
                    problem_description=problem_description,
                    thoughts=thoughts_path,
                    n=self.c,
                    **kwargs,
                )
                if not new_thoughts:
                    return ""
                if isinstance(new_thoughts, list):
                    self.tot_memory[thoughts_path] = new_thoughts[::-1]
                else:
                    return ""
            return self.tot_memory[thoughts_path].pop()