思维树

class TreeOfThoughts:
    def __init__(self, llm, search_algorithm="bfs", max_depth=5):
        self.llm = llm
        self.search_algorithm = search_algorithm
        self.max_depth = max_depth
        self.memory = []  # 存储搜索历史
        
    def generate_thoughts(self, state, k=4):
        """生成k个候选思维"""
        prompt = f"""
        当前状态：{state}
        请生成{k}个不同的下一步思维：
        """
        thoughts = self.llm.generate_multiple(prompt, n=k)
        return thoughts
    
    def evaluate_thought(self, thought):
        """评估思维的价值"""
        prompt = f"""
        评估以下思维的价值（0-10分）：
        {thought}
        考虑：正确性、进展、可行性
        """
        score = self.llm.evaluate(prompt)
        return score
    
    def search(self, problem):
        """执行树搜索"""
        if self.search_algorithm == "bfs":
            return self.breadth_first_search(problem)
        elif self.search_algorithm == "dfs":
            return self.depth_first_search(problem)
        elif self.search_algorithm == "beam":
            return self.beam_search(problem)

def solve_24_game(numbers):
    """使用ToT解决24点游戏"""
    tot = TreeOfThoughts(llm, search_algorithm="beam")
    
    initial_state = {
        "numbers": numbers,
        "operations": [],
        "target": 24
    }
    
    # 定义思维生成规则
    def generate_step(state):
        thoughts = []
        nums = state["numbers"]
        
        # 生成所有可能的运算组合
        for i, j in combinations(range(len(nums)), 2):
            for op in ['+', '-', '*', '/']:
                thought = f"{nums[i]} {op} {nums[j]}"
                thoughts.append(thought)
        
        return thoughts
    
    # 执行搜索
    solution = tot.search_with_custom_generator(
        initial_state, 
        generate_step,
        is_goal=lambda s: len(s["numbers"]) == 1 and s["numbers"][0] == 24
    )
    
    return solution

def creative_writing_tot(prompt, constraints):
    """使用ToT进行创意写作"""
    
    class StoryNode:
        def __init__(self, text, coherence_score, creativity_score):
            self.text = text
            self.coherence = coherence_score
            self.creativity = creativity_score
            self.children = []
    
    def generate_story_branches(node, num_branches=3):
        """为故事节点生成多个可能的延续"""
        branches = []
        for _ in range(num_branches):
            continuation = llm.generate(
                f"继续这个故事：{node.text}\n"
                f"约束：{constraints}"
            )
            # 评估连贯性和创意性
            scores = evaluate_story_quality(continuation)
            branches.append(StoryNode(
                continuation, 
                scores['coherence'],
                scores['creativity']
            ))
        return branches
    
    # 构建故事树并选择最佳路径
    root = StoryNode(prompt, 1.0, 1.0)
    best_story = beam_search_story(root, beam_width=3, depth=5)
    
    return best_story

class TreeOfUncertainThoughts(TreeOfThoughts):
    """集成不确定性量化的ToT"""
    
    def evaluate_with_uncertainty(self, thought):
        """评估思维并量化不确定性"""
        # 多次采样获得分布
        scores = []
        for _ in range(5):
            score = self.llm.evaluate(thought, temperature=0.7)
            scores.append(score)
        
        mean_score = np.mean(scores)
        uncertainty = np.std(scores)
        
        return {
            'score': mean_score,
            'uncertainty': uncertainty,
            'confidence': 1 / (1 + uncertainty)
        }
    
    def risk_aware_search(self, problem, risk_tolerance=0.5):
        """考虑风险的搜索策略"""
        # 在高风险决策点要求更高确定性
        # 在低风险探索阶段允许更多不确定性
        pass