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


[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 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)
[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): """ 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)
[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()