mirror of
https://github.com/hwchase17/langchain
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f94e24dfd7
Best to review one commit at a time, since two of the commits are 100% autogenerated changes from running `ruff format`: - Install and use `ruff format` instead of black for code formatting. - Output of `ruff format .` in the `langchain` package. - Use `ruff format` in experimental package. - Format changes in experimental package by `ruff format`. - Manual formatting fixes to make `ruff .` pass.
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
"""
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We provide two strategies for generating thoughts in the Tree of Thoughts (ToT)
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framework to avoid repetition:
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These strategies ensure that the language model generates diverse and
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non-repeating thoughts, which are crucial for problem-solving tasks that require
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exploration.
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"""
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from abc import abstractmethod
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from typing import Any, Dict, List, Tuple
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from langchain.chains.llm import LLMChain
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from langchain.prompts.base import BasePromptTemplate
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from langchain_experimental.pydantic_v1 import Field
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from langchain_experimental.tot.prompts import COT_PROMPT, PROPOSE_PROMPT
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class BaseThoughtGenerationStrategy(LLMChain):
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"""
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Base class for a thought generation strategy.
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"""
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c: int = 3
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"""The number of children thoughts to propose at each step."""
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@abstractmethod
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def next_thought(
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self,
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problem_description: str,
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thoughts_path: Tuple[str, ...] = (),
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**kwargs: Any,
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) -> str:
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"""
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Generate the next thought given the problem description and the thoughts
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generated so far.
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"""
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class SampleCoTStrategy(BaseThoughtGenerationStrategy):
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"""
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Sample thoughts from a Chain-of-Thought (CoT) prompt.
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This strategy works better when the thought space is rich, such as when each
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thought is a paragraph. Independent and identically distributed samples
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lead to diversity, which helps to avoid repetition.
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"""
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prompt: BasePromptTemplate = COT_PROMPT
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def next_thought(
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self,
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problem_description: str,
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thoughts_path: Tuple[str, ...] = (),
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**kwargs: Any,
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) -> str:
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response_text = self.predict_and_parse(
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problem_description=problem_description, thoughts=thoughts_path, **kwargs
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)
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return response_text if isinstance(response_text, str) else ""
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class ProposePromptStrategy(BaseThoughtGenerationStrategy):
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"""
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Propose thoughts sequentially using a "propose prompt".
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This strategy works better when the thought space is more constrained, such
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as when each thought is just a word or a line. Proposing different thoughts
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in the same prompt completion helps to avoid duplication.
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"""
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prompt: BasePromptTemplate = PROPOSE_PROMPT
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tot_memory: Dict[Tuple[str, ...], List[str]] = Field(default_factory=dict)
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def next_thought(
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self,
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problem_description: str,
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thoughts_path: Tuple[str, ...] = (),
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**kwargs: Any,
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) -> str:
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if thoughts_path not in self.tot_memory or not self.tot_memory[thoughts_path]:
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new_thoughts = self.predict_and_parse(
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problem_description=problem_description,
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thoughts=thoughts_path,
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n=self.c,
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**kwargs,
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)
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if not new_thoughts:
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return ""
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if isinstance(new_thoughts, list):
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self.tot_memory[thoughts_path] = new_thoughts[::-1]
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else:
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return ""
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return self.tot_memory[thoughts_path].pop()
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