218 lines
12 KiB
Python
218 lines
12 KiB
Python
import os, sys, threading, queue, time, json, re, random
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if sys.stdout is None: sys.stdout = open(os.devnull, "w")
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elif hasattr(sys.stdout, 'reconfigure'): sys.stdout.reconfigure(errors='replace')
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if sys.stderr is None: sys.stderr = open(os.devnull, "w")
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elif hasattr(sys.stderr, 'reconfigure'): sys.stderr.reconfigure(errors='replace')
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from llmcore import SiderLLMSession, LLMSession, ToolClient, ClaudeSession, XaiSession, build_multimodal_content
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from agent_loop import agent_runner_loop, StepOutcome, BaseHandler
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from ga import GenericAgentHandler, smart_format, get_global_memory, format_error
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script_dir = os.path.dirname(os.path.abspath(__file__))
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with open(os.path.join(script_dir, 'assets/tools_schema.json'), 'r', encoding='utf-8') as f:
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TS = f.read()
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TOOLS_SCHEMA = json.loads(TS if os.name == 'nt' else TS.replace('powershell', 'bash'))
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mem_dir = os.path.join(script_dir, 'memory')
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if not os.path.exists(mem_dir): os.makedirs(mem_dir)
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mem_txt = os.path.join(mem_dir, 'global_mem.txt')
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if not os.path.exists(mem_txt): open(mem_txt, 'w', encoding='utf-8').write('')
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mem_insight = os.path.join(mem_dir, 'global_mem_insight.txt')
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if not os.path.exists(mem_insight):
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t = os.path.join(script_dir, 'assets/global_mem_insight_template.txt')
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open(mem_insight, 'w', encoding='utf-8').write(open(t, encoding='utf-8').read() if os.path.exists(t) else '')
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cdp_cfg = os.path.join(script_dir, 'assets/tmwd_cdp_bridge/config.js')
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if not os.path.exists(cdp_cfg):
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try:
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os.makedirs(os.path.dirname(cdp_cfg), exist_ok=True)
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open(cdp_cfg, 'w', encoding='utf-8').write(f"const TID = '__ljq_{hex(random.randint(0, 99999999))[2:8]}';")
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except Exception as e: print(f'[WARN] CDP config init failed: {e} — advanced web features (tmwebdriver) will be unavailable.')
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def get_system_prompt():
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with open(os.path.join(script_dir, 'assets/sys_prompt.txt'), 'r', encoding='utf-8') as f: prompt = f.read()
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prompt += f"\nToday: {time.strftime('%Y-%m-%d %a')}\n"
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prompt += get_global_memory()
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return prompt
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class GeneraticAgent:
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def __init__(self):
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script_dir = os.path.dirname(os.path.abspath(__file__))
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temp_dir = os.path.join(script_dir, 'temp')
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if not os.path.exists(temp_dir): os.makedirs(temp_dir)
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from llmcore import mykeys
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llm_sessions = []
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for k, cfg in mykeys.items():
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if not any(x in k for x in ['api', 'config', 'cookie']): continue
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try:
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if 'claude' in k: llm_sessions += [ClaudeSession(api_key=cfg['apikey'], api_base=cfg['apibase'], model=cfg['model'])]
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if 'oai' in k: llm_sessions += [LLMSession(
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api_key=cfg['apikey'], api_base=cfg['apibase'], model=cfg['model'], proxy=cfg.get('proxy'),
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api_mode=cfg.get('api_mode', 'chat_completions'),
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max_retries=cfg.get('max_retries', 2),
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connect_timeout=cfg.get('connect_timeout', 10),
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read_timeout=cfg.get('read_timeout', 120),
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)]
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if 'xai' in k: llm_sessions += [XaiSession(cfg, mykeys.get('proxy', ''))]
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if 'sider' in k: llm_sessions += [SiderLLMSession(cfg, default_model=x) for x in \
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["gemini-3.0-flash", "gpt-5.4"]]
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except: pass
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if len(llm_sessions) > 0: self.llmclient = ToolClient(llm_sessions, auto_save_tokens=True)
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else: self.llmclient = None
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self.lock = threading.Lock()
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self.history = []
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self.task_queue = queue.Queue()
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self.is_running, self.stop_sig = False, False
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self.llm_no = 0
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self.inc_out = False
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self.handler = None
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self.verbose = True
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def next_llm(self, n=-1):
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self.llm_no = ((self.llm_no + 1) if n < 0 else n) % len(self.llmclient.backends)
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self.llmclient.last_tools = ''
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def list_llms(self): return [(i, f"{type(b).__name__}/{b.default_model}", i == self.llm_no) for i, b in enumerate(self.llmclient.backends)]
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def get_llm_name(self):
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b = self.llmclient.backends[self.llm_no]
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return f"{type(b).__name__}/{b.default_model}"
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def abort(self):
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print('Abort current task...')
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if not self.is_running: return
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self.stop_sig = True
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if self.handler is not None:
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self.handler.code_stop_signal.append(1)
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def put_task(self, query, source="user", images=None):
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display_queue = queue.Queue()
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self.task_queue.put({"query": query, "source": source, "images": images or [], "output": display_queue})
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return display_queue
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def run(self):
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while True:
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task = self.task_queue.get()
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self.is_running = True
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raw_query, source, images, display_queue = task["query"], task["source"], task.get("images") or [], task["output"]
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rquery = smart_format(raw_query.replace('\n', ' '), max_str_len=200)
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self.history.append(f"[USER]: {rquery}")
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sys_prompt = get_system_prompt()
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script_dir = os.path.dirname(os.path.abspath(__file__))
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handler = GenericAgentHandler(None, self.history, os.path.join(script_dir, 'temp'))
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if self.handler and self.handler.key_info:
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handler.key_info = self.handler.key_info
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if '清除工作记忆' not in handler.key_info:
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handler.key_info += '\n[SYSTEM] 若开始新任务，先更新或清除工作记忆\n'
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self.handler = handler
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self.llmclient.backend = self.llmclient.backends[self.llm_no]
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user_input = raw_query
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if source == 'feishu' and len(self.history) > 1: # 如果有历史记录且来自飞书，注入到首轮 user_input 中（支持/restore恢复上下文）
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user_input = handler._get_anchor_prompt() + f"\n\n### 用户当前消息\n{raw_query}"
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initial_user_content = None
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if images and isinstance(self.llmclient.backend, LLMSession):
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initial_user_content = build_multimodal_content(user_input, images)
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elif images:
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print(f"[INFO] backend {type(self.llmclient.backend).__name__} does not support direct multimodal input, fallback to text attachment hints.")
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gen = agent_runner_loop(self.llmclient, sys_prompt, user_input,
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handler, TOOLS_SCHEMA, max_turns=40, verbose=self.verbose,
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initial_user_content=initial_user_content)
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try:
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full_resp = ""; last_pos = 0
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for chunk in gen:
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if self.stop_sig: break
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full_resp += chunk
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if len(full_resp) - last_pos > 50:
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display_queue.put({'next': full_resp[last_pos:] if self.inc_out else full_resp, 'source': source})
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last_pos = len(full_resp)
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if self.inc_out and last_pos < len(full_resp): display_queue.put({'next': full_resp[last_pos:], 'source': source})
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if '</summary>' in full_resp: full_resp = full_resp.replace('</summary>', '</summary>\n\n')
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if '</file_content>' in full_resp: full_resp = re.sub(r'<file_content>\s*(.*?)\s*</file_content>', r'\n````\n<file_content>\n\1\n</file_content>\n````', full_resp, flags=re.DOTALL)
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display_queue.put({'done': full_resp, 'source': source})
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self.history = handler.history_info
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except Exception as e:
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print(f"Backend Error: {format_error(e)}")
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display_queue.put({'done': full_resp + f'\n```\n{format_error(e)}\n```', 'source': source})
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finally:
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self.is_running = self.stop_sig = False
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self.task_queue.task_done()
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if self.handler is not None: self.handler.code_stop_signal.append(1)
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if __name__ == '__main__':
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import argparse
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from datetime import datetime
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parser = argparse.ArgumentParser()
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parser.add_argument('--scheduled', action='store_true', help='计划任务轮询模式')
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parser.add_argument('--task', metavar='IODIR', help='一次性任务模式(文件IO)')
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parser.add_argument('--reflect', metavar='SCRIPT', help='反射模式：加载监控脚本，check()触发时发任务')
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parser.add_argument('--llm_no', type=int, default=0, help='LLM编号')
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args = parser.parse_args()
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agent = GeneraticAgent()
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agent.llm_no = args.llm_no
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agent.verbose = False
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threading.Thread(target=agent.run, daemon=True).start()
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if args.task:
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script_dir = os.path.dirname(os.path.abspath(__file__))
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d = os.path.join(script_dir, f'temp/{args.task}'); rp = os.path.join(d, 'reply.txt'); nround = ''
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with open(os.path.join(d, 'input.txt'), encoding='utf-8') as f: raw = f.read()
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while True:
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dq = agent.put_task(raw, source='task')
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while 'done' not in (item := dq.get(timeout=120)):
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if 'next' in item and random.random() < 0.95: # 概率写一次中间结果
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with open(f'{d}/output{nround}.txt', 'w', encoding='utf-8') as f: f.write(item.get('next', ''))
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with open(f'{d}/output{nround}.txt', 'w', encoding='utf-8') as f: f.write(item['done'] + '\n\n[ROUND END]\n')
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for _ in range(150): # 等reply.txt，5分钟超时
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time.sleep(2)
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if os.path.exists(rp):
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with open(rp, encoding='utf-8') as f: raw = f.read()
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os.remove(rp); break
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else: break
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nround = int(nround) + 1 if nround.isdigit() else 1
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elif args.reflect:
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import importlib.util
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spec = importlib.util.spec_from_file_location('reflect_script', args.reflect)
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mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
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interval = getattr(mod, 'INTERVAL', 5)
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once = getattr(mod, 'ONCE', False)
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on_done = getattr(mod, 'on_done', None)
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print(f'[Reflect] loaded {args.reflect}, interval={interval}s, once={once}')
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while True:
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time.sleep(interval)
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try: task = mod.check()
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except Exception as e:
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print(f'[Reflect] check() error: {e}'); continue
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if task is None: continue
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print(f'[Reflect] triggered: {task[:80]}')
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dq = agent.put_task(task, source='reflect')
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try:
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while 'done' not in (item := dq.get(timeout=120)): pass
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result = item['done']
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print(result)
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except Exception as e:
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if once: raise
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print(f'[Reflect] drain error: {e}'); result = f'[ERROR] {e}'
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script_dir = os.path.dirname(os.path.abspath(__file__))
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open(os.path.join(script_dir, './temp/reflect.log'), 'a', encoding='utf-8').write(f'[{datetime.now():%m-%d %H:%M}]\n{result}\n\n')
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if on_done:
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try: on_done(result)
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except Exception as e: print(f'[Reflect] on_done error: {e}')
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if once: print('[Reflect] ONCE=True, exiting.'); break
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elif args.scheduled:
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print('moved to reflect mode')
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else:
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agent.inc_out = True
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while True:
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q = input('> ').strip()
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if not q: continue
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try:
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dq = agent.put_task(q, source='user')
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while True:
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item = dq.get()
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if 'next' in item: print(item['next'], end='', flush=True)
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if 'done' in item: print(); break
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except KeyboardInterrupt:
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agent.abort()
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print('\n[Interrupted]')
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