150 行代码，复刻「草莓」，青春版支持联网

from openai import OpenAIfrom dataclasses import dataclass, fieldfrom typing import List, Optionalfrom IPython.display import display, Markdownfrom google.colab import userdata
# Set your OpenAI API key securelyclient = OpenAI(    api_key=userdata.get('Key_Zhipu'),    base_url="https://open.bigmodel.cn/api/paas/v4/") model = "glm-4-flash"
# Define data models@dataclassclass ThoughtStep:    step_answer: str    is_completed: bool    hint: str
@dataclassclass ReasoningProcess:    initial_problem: str    steps: List[ThoughtStep] = field(default_factory=list)    final_answer: Optional[str] = None
def solve_problem(problem: str, max_attempts: int = 10) -> ReasoningProcess:    """    Solve a problem using multi-step reasoning, planning, and intelligent thinking.    """    reasoning_process = ReasoningProcess(initial_problem=problem)    attempts = 0    is_completed = False
    # Step 1: Analyze the problem and plan    analysis_prompt = f"""You are an AI assistant that excels at solving complex STEM problems using multi-step reasoning.When given a problem, first analyze it, think about possible solution methods, and plan the subsequent steps to solve it.
Problem:{problem}
Web Search:{watt(problem)}
Provide your analysis and step-by-step plan in plain text."""
    display(Markdown("**大聪明正在思考...**"))    messages = [{"role": "user", "content": analysis_prompt}]    response = client.chat.completions.create(        model=model,        messages=messages    ).choices[0].message.content.strip()
    # Display AI's initial analysis    display(Markdown(f"### AI Initial Analysis:\n{response}\n"))
    hint = response    analysis_step = ThoughtStep(step_answer="", is_completed=False, hint=hint)    reasoning_process.steps.append(analysis_step)
    messages = [{"role": "system", "content": "You are an AI assistant continuing the problem-solving process."},                {"role": "user", "content": "Giving a thought about this problem: " + problem},                {"role": "assistant", "content": hint},                {"role": "user", "content": f"Solve it with this thought, and give the final answer"}]
    # Continue with the plan and attempt to solve the problem    while not is_completed and attempts < max_attempts:        attempts += 1        # Phase 1: Generate the step answer based on the thought        response = client.chat.completions.create(            model=model,            messages=messages        ).choices[0].message.content.strip()
        # Extract step answer        step_answer = response.strip()        display(Markdown(f"### Step Answer (Attempt {attempts}):\n{step_answer}\n"))
        # Phase 2: Validate the step answer using XML format        validation_prompt = f"""You are an AI validator. Check if the following step answer solves the problem correctly:
Problem:{problem}
Step Answer:{step_answer}
Respond in XML format as follows:<response>    <is_correct>Is this answer 100% correct? Return true or false</is_correct>    <hint>If the answer is incorrect, provide a new thought or hint.</hint></response>"""
        display(Markdown(f"**AI is validating step answer (Attempt {attempts})...**"))        messages_validation = [{"role": "user", "content": validation_prompt}]        response = client.chat.completions.create(            model=model,            messages=messages_validation        ).choices[0].message.content.strip()
        # Parse the XML response        try:            is_correct = 'true' in response.lower()            hint_start = response.find('<hint>') + len('<hint>')            hint_end = response.find('</hint>')            hint = response[hint_start:hint_end].strip() if hint_start != -1 and hint_end != -1 else "No hint provided"        except:            is_correct = False            hint = "Error parsing validation response."
        # Update reasoning process        step = ThoughtStep(step_answer=step_answer, is_completed=is_correct, hint=hint)        reasoning_process.steps.append(step)
        messages += [{"role": "assistant", "content": step_answer}]
        if is_correct:            break  # Exit loop if the step answer is correct
        messages += [{"role": "user", "content": "Not correct, try with this: " + hint}]
    # Final answer step    messages += [{"role": "user", "content": f"Based on your reasoning, provide the final answer to the problem and return it in the same language as the following: {reasoning_process.initial_problem}"}]    response = client.chat.completions.create(        model=model,        messages=messages    ).choices[0].message.content.strip()
    # Extract the final answer    reasoning_process.final_answer = response
    # Display the final answer    display(Markdown(f"## Final Answer:\n{response}"))
    return reasoning_process
def display_reasoning_process(process: ReasoningProcess) -> None:    """    Display the reasoning process details.    """    display(Markdown(f"## Problem:\n{process.initial_problem}\n"))    for idx, step in enumerate(process.steps, 1):        display(Markdown(f"### Step {idx}:\n**Hint**: {step.hint}\n**Is Completed**: {step.is_completed}\n"))    if process.final_answer:        display(Markdown(f"## Final Answer:\n{process.final_answer}"))    else:        display(Markdown("## Final Answer: Not determined yet."))
# Example usageif __name__ == "__main__":    problem_text = """9.8 和 9.11 谁大"""
    # Solve the problem    reasoning = solve_problem(problem_text)