1  # Copyright (c) 2024-2026 Tencent Zhuque Lab. All rights reserved.
 2  #
 3  # Licensed under the Apache License, Version 2.0 (the "License");
 4  # you may not use this file except in compliance with the License.
 5  # You may obtain a copy of the License at
 6  #
 7  #     http://www.apache.org/licenses/LICENSE-2.0
 8  #
 9  # Unless required by applicable law or agreed to in writing, software
10  # distributed under the License is distributed on an "AS IS" BASIS,
11  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12  # See the License for the specific language governing permissions and
13  # limitations under the License.
14  #
15  # Requirement: Any integration or derivative work must explicitly attribute
16  # Tencent Zhuque Lab (https://github.com/Tencent/AI-Infra-Guard) in its
17  # documentation or user interface, as detailed in the NOTICE file.
18  
19  
20  from tools.registry import register_tool
21  from utils.tool_context import ToolContext
22  
23  
24  @register_tool(sandbox_execution=False)
25  def think(thought: str, context: ToolContext = None):
26      """
27      Deep Thinking Tool.
28      Use this tool when you are stuck, facing a complex problem, or need to plan a multi-step task.
29      It will pause the current execution and use a specialized reasoning model to analyze the situation.
30  
31      Args:
32          thought: The specific problem, question, or situation you need to think about.
33                   Be detailed about what you know and what you are unsure about.
34          context: Tool context (automatically injected).
35  
36      Returns:
37          A structured analysis containing reasoning, plan, and next steps.
38      """
39      try:
40          if not thought or not thought.strip():
41              return {"message": "Thought cannot be empty"}
42  
43          # 如果有context，使用思考模型深度分析
44          #         system_prompt = """你是一个专业的思考助手，擅长深度分析和逻辑推理。
45          # 你的任务是对用户提出的问题进行深入思考，提供：
46          # 1. 问题分析
47          # 2. 当前信息和背景整合
48          # 3. 可能的解决方案
49          # 4. 潜在风险和注意事项
50          # 5. 推荐的行动步骤
51          #
52          # 请用简洁、结构化的方式回答。"""
53          #
54          #         # 使用专门的思考模型（如果配置了），否则使用默认LLM
55          #         thinking_result = context.call_llm(
56          #             prompt=f"请对以下内容进行深度思考和分析：\n\n{thought}",
57          #             purpose="thinking",
58          #             system_prompt=system_prompt,
59          #             use_history=True  # 思考时需要历史记录
60          #         )
61  
62          return {
63              "success": True,
64              "thought": thought,
65              # "thinking_result": thinking_result,
66          }
67  
68      except (ValueError, TypeError) as e:
69          return {"success": False, "message": f"Failed to record thought: {e!s}"}
70      except Exception as e:
71          return {"success": False, "message": f"Error during thinking: {str(e)}"}