|
|
|
|
|
MY_REACT_PROMPT = """你是一个具备推理和行动能力的AI助手。你可以通过思考分析问题，然后调用合适的工具来获取信息，最终给出准确的答案。
|
|
|
|
|
|
|
|
|
|
|
|
## 可用工具
|
|
|
|
|
|
{tools}
|
|
|
|
|
|
|
|
|
|
|
|
## 工作流程
|
|
|
|
|
|
请严格按照以下格式进行回应，每次只能执行一个步骤：
|
|
|
|
|
|
|
|
|
|
|
|
**Thought:** 分析当前问题，思考需要什么信息或采取什么行动。
|
|
|
|
|
|
**Action:** 选择一个行动，格式必须是以下之一：
|
|
|
|
|
|
- `{{tool_name}}[{{tool_input}}]` - 调用指定工具
|
|
|
|
|
|
- `Finish[最终答案]` - 当你有足够信息给出最终答案时
|
|
|
|
|
|
|
|
|
|
|
|
## 重要提醒
|
|
|
|
|
|
1. 每次回应必须包含Thought和Action两部分
|
|
|
|
|
|
2. 工具调用的格式必须严格遵循：工具名[参数]
|
|
|
|
|
|
3. 只有当你确信有足够信息回答问题时，才使用Finish
|
|
|
|
|
|
4. 如果工具返回的信息不够，继续使用其他工具或相同工具的不同参数
|
|
|
|
|
|
|
|
|
|
|
|
## 当前任务
|
|
|
|
|
|
**Question:** {question}
|
|
|
|
|
|
|
|
|
|
|
|
## 执行历史
|
|
|
|
|
|
{history}
|
|
|
|
|
|
|
|
|
|
|
|
现在开始你的推理和行动：
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
import re
|
|
|
|
|
|
from typing import Optional, List, Tuple
|
|
|
|
|
|
from hello_agents import ReActAgent, HelloAgentsLLM, Config, Message, ToolRegistry
|
|
|
|
|
|
|
|
|
|
|
|
class MyReActAgent(ReActAgent):
|
|
|
|
|
|
"""
|
|
|
|
|
|
重写的ReAct Agent - 推理与行动结合的智能体
|
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
|
|
def __init__(
|
|
|
|
|
|
self,
|
|
|
|
|
|
name: str,
|
|
|
|
|
|
llm: HelloAgentsLLM,
|
|
|
|
|
|
tool_registry: ToolRegistry,
|
|
|
|
|
|
system_prompt: Optional[str] = None,
|
|
|
|
|
|
config: Optional[Config] = None,
|
|
|
|
|
|
max_steps: int = 5,
|
|
|
|
|
|
custom_prompt: Optional[str] = None
|
|
|
|
|
|
):
|
|
|
|
|
|
super().__init__(name, llm, system_prompt, config)
|
|
|
|
|
|
self.tool_registry = tool_registry
|
|
|
|
|
|
self.max_steps = max_steps
|
|
|
|
|
|
self.current_history: List[str] = []
|
|
|
|
|
|
self.prompt_template = custom_prompt if custom_prompt else MY_REACT_PROMPT
|
|
|
|
|
|
print(f"✅ {name} 初始化完成，最大步数: {max_steps}")
|
|
|
|
|
|
|
|
|
|
|
|
def run(self, input_text: str, **kwargs) -> str:
|
|
|
|
|
|
"""运行ReAct Agent"""
|
|
|
|
|
|
self.current_history = []
|
|
|
|
|
|
current_step = 0
|
|
|
|
|
|
|
|
|
|
|
|
print(f"\n🤖 {self.name} 开始处理问题: {input_text}")
|
|
|
|
|
|
|
|
|
|
|
|
while current_step < self.max_steps:
|
|
|
|
|
|
current_step += 1
|
|
|
|
|
|
print(f"\n--- 第 {current_step} 步 ---")
|
|
|
|
|
|
|
|
|
|
|
|
# 1. 构建提示词
|
|
|
|
|
|
tools_desc = self.tool_registry.get_tools_description()
|
|
|
|
|
|
history_str = "\n".join(self.current_history)
|
|
|
|
|
|
prompt = self.prompt_template.format(
|
|
|
|
|
|
tools=tools_desc,
|
|
|
|
|
|
question=input_text,
|
|
|
|
|
|
history=history_str
|
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
# 2. 调用LLM
|
|
|
|
|
|
messages = [{"role": "user", "content": prompt}]
|
|
|
|
|
|
response_text = self.llm.invoke(messages, **kwargs)
|
|
|
|
|
|
|
|
|
|
|
|
# 3. 解析输出
|
|
|
|
|
|
thought, action = self._parse_output(response_text)
|
|
|
|
|
|
|
|
|
|
|
|
# 4. 检查完成条件
|
|
|
|
|
|
if action and action.startswith("Finish"):
|
|
|
|
|
|
final_answer = self._parse_action_input(action)
|
|
|
|
|
|
self._save_to_history(input_text, final_answer)
|
|
|
|
|
|
return final_answer
|
|
|
|
|
|
|
|
|
|
|
|
# 5. 执行工具调用
|
|
|
|
|
|
if action:
|
|
|
|
|
|
tool_name, tool_input = self._parse_action(action)
|
|
|
|
|
|
observation = self.tool_registry.execute_tool(tool_name, tool_input)
|
|
|
|
|
|
self.current_history.append(f"Action: {action}")
|
|
|
|
|
|
self.current_history.append(f"Observation: {observation}")
|
|
|
|
|
|
|
|
|
|
|
|
# 达到最大步数
|
|
|
|
|
|
final_answer = "抱歉，我无法在限定步数内完成这个任务。"
|
|
|
|
|
|
self._save_to_history(input_text, final_answer)
|
|
|
|
|
|
return final_answer
|
