6bc5f3e80d
- Add exhaust() helper for generator handling - Add verbose parameter to control output verbosity - Extend next_llm() to support direct index switching - Add list_llms() and get_llm_name() query methods - New tgapp.py: Telegram bot with streaming updates - Update stapp.py to use new LLM query API
93 lines
4.0 KiB
Python
93 lines
4.0 KiB
Python
import json, re
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from dataclasses import dataclass
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from typing import Any, Optional
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@dataclass
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class StepOutcome:
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data: Any
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next_prompt: Optional[str] = None
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should_exit: bool = False
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def try_call_generator(func, *args, **kwargs):
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ret = func(*args, **kwargs)
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if hasattr(ret, '__iter__') and not isinstance(ret, (str, bytes, dict, list)):
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ret = yield from ret
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return ret
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class BaseHandler:
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def tool_before_callback(self, tool_name, args, response): pass
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def tool_after_callback(self, tool_name, args, response, ret): pass
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def dispatch(self, tool_name, args, response):
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method_name = f"do_{tool_name}"
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if hasattr(self, method_name):
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_ = yield from try_call_generator(self.tool_before_callback, tool_name, args, response)
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ret = yield from try_call_generator(getattr(self, method_name), args, response)
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_ = yield from try_call_generator(self.tool_after_callback, tool_name, args, response, ret)
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return ret
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elif tool_name == 'bad_json':
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return StepOutcome(None, next_prompt=args.get('msg', 'bad_json'), should_exit=False)
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else:
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yield f"❌ 未知工具: {tool_name}\n"
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return StepOutcome(None, next_prompt=f"未知工具 {tool_name}", should_exit=False)
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def json_default(o):
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if isinstance(o, set): return list(o)
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return str(o)
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def exhaust(g):
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try:
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while True: next(g)
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except StopIteration as e: return e.value
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def get_pretty_json(data):
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if isinstance(data, dict) and "script" in data:
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data = data.copy()
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data["script"] = data["script"].replace("; ", ";\n ")
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return json.dumps(data, indent=2, ensure_ascii=False).replace('\\n', '\n')
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def agent_runner_loop(client, system_prompt, user_input, handler, tools_schema, max_turns=15, verbose=True):
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_input}
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]
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for turn in range(max_turns):
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yield f"**LLM Running (Turn {turn+1}) ...**\n\n"
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if (turn+1) % 10 == 0: client.last_tools = '' # 每10轮重置一次工具描述，避免上下文过大导致的模型性能下降
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response_gen = client.chat(messages=messages, tools=tools_schema)
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response = yield from response_gen
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if verbose: yield '\n\n'
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if not response.tool_calls:
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tool_name, args = 'no_tool', {}
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else:
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tool_call = response.tool_calls[0]
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tool_name = tool_call.function.name
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args = json.loads(tool_call.function.arguments)
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if tool_name == 'no_tool': pass
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else:
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yield f"🛠️ **正在调用工具:** `{tool_name}`"
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if verbose: yield f"📥**参数:**\n````text\n{get_pretty_json(args)}\n````\n"
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else: yield '\n\n\n'
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gen = handler.dispatch(tool_name, args, response)
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if verbose:
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yield '`````\n'
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outcome = yield from gen
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yield '`````\n'
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else:
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outcome = exhaust(gen)
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if outcome.next_prompt is None: return {'result': 'CURRENT_TASK_DONE', 'data': outcome.data}
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if outcome.should_exit: return {'result': 'EXITED', 'data': outcome.data}
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next_prompt = ""
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if outcome.data is not None:
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datastr = json.dumps(outcome.data, ensure_ascii=False, default=json_default) if type(outcome.data) in [dict, list] else str(outcome.data)
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next_prompt += f"<tool_result>\n{datastr}\n</tool_result>\n\n"
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next_prompt += outcome.next_prompt
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if (turn+1) % 5 == 0:
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next_prompt += f"\n\n[DANGER] 已连续执行第 {turn+1} 轮。禁止无效重试。若无有效进展，必须切换策略：1. 探测物理边界 2. 请求用户协助。"
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if (turn+1) % 25 == 0:
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next_prompt += f"\n\n### [DANGER] 已连续执行第 {turn+1} 轮。你必须总结情况进行ask_user，不允许继续重试。"
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messages = [{"role": "user", "content": next_prompt}]
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return {'result': 'MAX_TURNS_EXCEEDED'} |