""" 航空客服 Agent（实验 5-3） 两种模式： - control（控制组）：系统提示只有自然语言政策；工具描述极简、无 expected_* 参数； 工具内部不做任何校验（天真执行）。政策是否被遵守完全靠模型自身推理。 - codified（实验组）：三重保障—— (1) 系统提示保留同样的自然语言政策； (2) 工具描述列出完整政策，并以可选 expected_* 参数引导模型"调用前逐条核对"； (3) 工具内部基于数据库真值做代码化校验，可拒绝违规操作。 """ from __future__ import annotations import json import os import time from openai import OpenAI from airline_env import AirlineEnv MODEL = os.environ.get("MODEL", "qwen3:4b") MAX_TURNS = 6 # --- 通用 OpenRouter 兜底 --- OPENROUTER_BASE_URL = "https://openrouter.ai/api/v1" def _map_to_openrouter_model(model: str) -> str: """把直连模型名映射为 OpenRouter 上的 id（非可映射 id 统一兜底到当前廉价旗舰）。""" if not model or "/" in model: return model or "openai/gpt-5.6-luna" m = model.lower() if m.startswith(("gpt-", "o1", "o3", "o4")): return "openai/" + model if m.startswith("claude"): if "haiku" in m: return "anthropic/claude-haiku-4.5" if "sonnet" in m: return "anthropic/claude-sonnet-4.6" return "anthropic/claude-opus-4.8" if m.startswith("gemini"): return "google/" + model return "openai/gpt-5.6-luna" # --------------------------------------------------------------------------- # 自然语言政策（两组共享，放进系统提示） # --------------------------------------------------------------------------- NL_POLICY = """你是 SkyWing 航空公司的客服 Agent，可以帮乘客查询和取消预订。 【退款政策（自然语言）】 - 经济舱基础票（basic_economy）默认不可退款。 - 例外 1：下单后 24 小时内，可全额退款。 - 例外 2：航班被航司取消，或发生重大延误（延误 ≥ 3 小时），可全额退款。 - 经济舱灵活票（economy_flex）与商务舱（business）可全额退款。 - 若不可退款：应礼貌解释政策，并主动提议替代方案（如保留客票改签、申请旅行信用点）， 绝不能给用户退款。 请先判断能否退款，再决定是否调用取消/退款工具。乘客自述的信息（舱位、下单时间等） 可能不准确，应以系统查询到的预订信息为准。""" CONTROL_SYSTEM = NL_POLICY CODIFIED_SYSTEM = NL_POLICY + """ 【操作要求】 调用 cancel_reservation 前，请先用 get_reservation 查询真实预订信息，逐条核对退款政策， 并在 expected_refundable / expected_reason 参数中如实填写你的判断（这是一份调用前 checklist）。 系统会以数据库真值为准进行校验：若你的判断与真值不符或存在违规，调用会被拒绝。""" # --------------------------------------------------------------------------- # 工具 schema # --------------------------------------------------------------------------- GET_RESERVATION_TOOL = { "type": "function", "function": { "name": "get_reservation", "description": "查询预订的详细信息（舱位、下单时间、下单时长、航班状态、价格等，均为系统真值）。", "parameters": { "type": "object", "properties": { "reservation_id": {"type": "string", "description": "预订编号，如 R001"}, }, "required": ["reservation_id"], }, }, } CONTROL_CANCEL_TOOL = { "type": "function", "function": { "name": "cancel_reservation", "description": "取消一个预订并处理退款。", "parameters": { "type": "object", "properties": { "reservation_id": {"type": "string", "description": "预订编号"}, }, "required": ["reservation_id"], }, }, } CODIFIED_CANCEL_TOOL = { "type": "function", "function": { "name": "cancel_reservation", "description": ( "取消预订并按政策退款。调用前请逐条核对退款政策（这是一份 checklist）：\n" "1) 舱位是否为 basic_economy？非基础经济票可退。\n" "2) 若为基础经济票：下单是否在 24 小时内？（以系统返回的 hours_since_booking 为准）\n" "3) 若为基础经济票：航班是否被航司取消，或延误 ≥ 3 小时（重大延误）？\n" "满足 1 的非基础票、或满足 2/3 例外之一，才可退款。\n" "请在 expected_refundable / expected_reason 中如实填写你的核对结论。" "系统会以数据库真值校验，不可退款的调用将被拒绝。" ), "parameters": { "type": "object", "properties": { "reservation_id": {"type": "string", "description": "预订编号"}, "expected_refundable": { "type": "boolean", "description": "你核对政策后判断该预订是否可退款（checklist 自报值）。", }, "expected_reason": { "type": "string", "enum": ["flexible_fare", "within_24h", "airline_caused", "non_refundable_basic_economy"], "description": "你判断可退/不可退的政策依据。", }, }, "required": ["reservation_id", "expected_refundable", "expected_reason"], }, }, } def _make_client(model: str | None = None, provider: str = "ollama"): """构造客户端并解析模型名，含通用 OpenRouter 兜底。返回 (client, resolved_model)。 - 有 OPENAI_API_KEY：直连；但当 model 是 gpt-5.x 且同时设置了 OPENROUTER_API_KEY 时优先走 OpenRouter（直连 gpt-5.6 需组织实名认证）。 - 无 OPENAI_API_KEY 但有 OPENROUTER_API_KEY：改走 OpenRouter（模型名自动映射）。 """ model = model or MODEL if provider == "ollama": api_key = "ollama" base_url = os.environ.get("OLLAMA_BASE_URL", "http://127.0.0.1:11434/v1") elif provider == "openrouter": api_key = os.environ.get("OPENROUTER_API_KEY") base_url = OPENROUTER_BASE_URL model = _map_to_openrouter_model(model) elif provider == "openai": api_key = os.environ.get("OPENAI_API_KEY") base_url = os.environ.get("OPENAI_BASE_URL") elif provider == "moonshot": api_key = os.environ.get("MOONSHOT_API_KEY") base_url = "https://api.moonshot.cn/v1" elif provider == "ark": api_key = os.environ.get("ARK_API_KEY") base_url = "https://ark.cn-beijing.volces.com/api/v3" else: raise ValueError(f"unsupported provider: {provider}") if not api_key: raise RuntimeError("未设置 OPENAI_API_KEY（或 OPENROUTER_API_KEY 兜底），请参考 env.example 配置。") kw = {"api_key": api_key} if base_url: kw["base_url"] = base_url return OpenAI(**kw), model, provider def _dispatch(env: AirlineEnv, mode: str, name: str, args: dict) -> dict: """把模型的工具调用路由到对应模式的环境方法。""" if name == "get_reservation": return env.get_reservation(args.get("reservation_id", "")) if name == "cancel_reservation": if mode == "control": return env.cancel_reservation_naive(args.get("reservation_id", "")) return env.cancel_reservation_codified( args.get("reservation_id", ""), expected_refundable=args.get("expected_refundable"), expected_reason=args.get("expected_reason"), ) return {"status": "error", "message": f"未知工具 {name}"} def run_agent(env: AirlineEnv, user_message: str, mode: str, verbose: bool = False, model: str | None = None, provider: str = "ollama") -> dict: """跑一个 case，返回 {final_text, transcript}。env 被就地修改（状态即真值）。 model 为空时回退到模块级默认 MODEL（小模型）。三方对照实验里，可用它把 "控制组"跑在一个更大的模型上，验证"小模型+代码化规则"能否追平"大模型裸跑"。 """ assert mode in ("control", "codified") client, model, provider = _make_client(model or MODEL, provider) if mode == "control": system, tools = CONTROL_SYSTEM, [GET_RESERVATION_TOOL, CONTROL_CANCEL_TOOL] else: system, tools = CODIFIED_SYSTEM, [GET_RESERVATION_TOOL, CODIFIED_CANCEL_TOOL] messages = [ {"role": "system", "content": system}, {"role": "user", "content": user_message}, ] transcript: list[dict] = [] provider_receipts: list[dict] = [] final_text = "" started = time.monotonic() for _turn in range(MAX_TURNS): resp = _chat_with_retry(client, messages, tools, model=model) msg = resp.choices[0].message usage = getattr(resp, "usage", None) provider_receipts.append({ "turn": _turn + 1, "response_id": getattr(resp, "id", None), "response_model": getattr(resp, "model", None), "finish_reason": getattr(resp.choices[0], "finish_reason", None), "usage": { "prompt_tokens": getattr(usage, "prompt_tokens", None), "completion_tokens": getattr(usage, "completion_tokens", None), "total_tokens": getattr(usage, "total_tokens", None), "cached_prompt_tokens": getattr( getattr(usage, "prompt_tokens_details", None), "cached_tokens", None, ), }, }) if msg.tool_calls: messages.append({ "role": "assistant", "content": msg.content or "", "tool_calls": [ {"id": tc.id, "type": "function", "function": {"name": tc.function.name, "arguments": tc.function.arguments}} for tc in msg.tool_calls ], }) for tc in msg.tool_calls: try: args = json.loads(tc.function.arguments or "{}") except json.JSONDecodeError: args = {} result = _dispatch(env, mode, tc.function.name, args) transcript.append({"tool": tc.function.name, "args": args, "result": result}) if verbose: print(f" [tool] {tc.function.name}({args}) -> {result.get('status')}") messages.append({ "role": "tool", "tool_call_id": tc.id, "content": json.dumps(result, ensure_ascii=False), }) continue final_text = msg.content or "" messages.append({"role": "assistant", "content": final_text}) break return { "provider": provider, "model": model, "final_text": final_text, "transcript": transcript, "messages": messages, "provider_receipts": provider_receipts, "duration_s": round(time.monotonic() - started, 3), } def _chat_with_retry(client: OpenAI, messages, tools, model: str | None = None, retries: int = 3): last_err = None model = model or MODEL # 推理模型（gpt-5 / o 系列等）不接受 temperature=0，其余仍固定 0 以尽量复现。 _reasoning = any(k in (model or "").lower() for k in ("gpt-5", "o1", "o3", "o4", "thinking", "reasoner", "kimi-k3")) for i in range(retries): try: return client.chat.completions.create( model=model, messages=messages, tools=tools, temperature=1 if _reasoning else 0.0, # 尽量降低随机性，保证可复现 ) except Exception as e: # noqa: BLE001 —— 网络/限流等，简单重试 last_err = e time.sleep(2 * (i + 1)) raise RuntimeError(f"OpenAI 调用失败：{last_err}")