langchain_experimental.tot.thought_generation

"""
我们为思维树（ToT）框架提供了两种生成思维的策略，以避免重复：

这些策略确保语言模型生成多样且不重复的思维，这对于需要探索的解决问题任务至关重要。
"""
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


[docs]class BaseThoughtGenerationStrategy(LLMChain):
    """
    用于思维生成策略的基类。
    """

    c: int = 3
    """每一步提议的子思维数量。"""

[docs]    @abstractmethod
    def next_thought(
        self,
        problem_description: str,
        thoughts_path: Tuple[str, ...] = (),
        **kwargs: Any,
    ) -> str:
        """根据问题描述和迄今为止生成的思考，生成下一个思考。
"""


[docs]class SampleCoTStrategy(BaseThoughtGenerationStrategy):
    """来自“Chain-of-Thought（CoT）”提示的示例策略。

当思维空间丰富时，例如每个思维都是一个段落时，这种策略效果更好。独立且同分布的样本导致多样性，有助于避免重复。"""

    prompt: BasePromptTemplate = Field(default_factory=get_cot_prompt)

[docs]    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 ""


[docs]class ProposePromptStrategy(BaseThoughtGenerationStrategy):
    """顺序使用“提议提示”的策略。

当思维空间更受限制时，例如每个思维只是一个词或一行时，这种策略效果更好。在同一个提示完成中提出不同的思维有助于避免重复。"""

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

[docs]    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()
Source code for langchain_experimental.tot.thought_generation
