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"""
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()
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