wehub-resource-sync
270 lines
9.6 KiB
Python
270 lines
9.6 KiB
Python
"""
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GenericAgent - 通用 JSON 格式 Agent，支持任意 VLM 模型。
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输出格式为标准 JSON，适配大多数视觉语言模型。
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"""
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from __future__ import annotations
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import json
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from typing import Any, ClassVar, Optional
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from bench_env.agent.base import BaseAgent, AgentConfig, ActionMapping, AgentStepRecord
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from bench_env.env.base import Action, ActionType, Observation
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from bench_env.llm import LLMClient
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class GenericAgent(BaseAgent):
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"""
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通用 JSON 格式 Agent。
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适用于任意支持视觉的 LLM 模型（如 GPT-4o、Gemini、Qwen-VL 等）。
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模型输出标准 JSON 格式，易于解析和调试。
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"""
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SYSTEM_PROMPT: ClassVar[str] = """你是一个手机 GUI-Agent 操作专家。你需要根据用户下发的任务、手机屏幕截图以及历史操作记录，输出一个动作来与手机交互，从而完成任务。
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坐标系：左上角为原点，x 向右，y 向下，取值范围均为 0-1000（归一化坐标）。
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你必须**只输出一个 JSON 对象**（不要输出任何额外文本/Markdown/代码块），schema 如下：
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{
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"action": "CLICK|TYPE|SWIPE|LONGPRESS|BACK|AWAKE|WAIT|INFO|COMPLETE|ABORT",
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"thought": "你的思考过程（可选）",
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"explain": "简短解释（可选）",
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"point": [x, y], // CLICK/LONGPRESS 需要
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"point1": [x1, y1], "point2": [x2, y2], // SWIPE/SLIDE 需要（SLIDE 会被映射到 SWIPE）
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"value": "..." , // TYPE/WAIT/AWAKE/INFO/ABORT 需要
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"clear": true, // TYPE 可选：先清空输入框再输入（默认 false，追加到已有文本后面）
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"return": "..." // COMPLETE 需要
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}
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要求：
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- 坐标必须为数字，范围 0-1000
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- 如果 action=WAIT，value 为秒数（数字）
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- 如果 action=AWAKE，value 为应用名称
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- 如果 action=COMPLETE，return 为任务完成的说明
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- 如果 action=INFO，value 为要询问用户的问题
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"""
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ACTION_MAP: ActionMapping = {
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"CLICK": (ActionType.CLICK, lambda p: {"point": p.get("point")}),
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"TAP": (ActionType.CLICK, lambda p: {"point": p.get("point")}),
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"LONGPRESS": (ActionType.LONG_PRESS, lambda p: {"point": p.get("point")}),
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"LONG_PRESS": (ActionType.LONG_PRESS, lambda p: {"point": p.get("point")}),
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"TYPE": (ActionType.TYPE, lambda p: {"value": p.get("value", p.get("text", "")), "clear": p.get("clear", False)}),
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"SLIDE": (ActionType.SWIPE, lambda p: {"point1": p.get("point1", p.get("start")), "point2": p.get("point2", p.get("end"))}),
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"SWIPE": (ActionType.SWIPE, lambda p: {"point1": p.get("point1", p.get("start")), "point2": p.get("point2", p.get("end"))}),
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"DRAG": (ActionType.DRAG, lambda p: {"point1": p.get("point1", p.get("start")), "point2": p.get("point2", p.get("end"))}),
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"BACK": (ActionType.BACK, lambda p: {}),
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"HOME": (ActionType.HOME, lambda p: {}),
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"RECENT": (ActionType.RECENT, lambda p: {}),
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"ENTER": (ActionType.ENTER, lambda p: {}),
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"WAIT": (ActionType.WAIT, lambda p: {"value": float(p.get("value", p.get("duration", 1.0)))}),
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"AWAKE": (ActionType.AWAKE, lambda p: {"value": p.get("value", p.get("app", ""))}),
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"LAUNCH": (ActionType.AWAKE, lambda p: {"value": p.get("value", p.get("app", ""))}),
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"INFO": (ActionType.INFO, lambda p: {"value": p.get("value", p.get("question", ""))}),
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"COMPLETE": (ActionType.COMPLETE, lambda p: {"return": p.get("return", p.get("message", ""))}),
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"FINISH": (ActionType.COMPLETE, lambda p: {"return": p.get("return", p.get("message", ""))}),
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"ABORT": (ActionType.ABORT, lambda p: {"value": p.get("value", p.get("reason", ""))}),
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}
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DEFAULT_MODEL_ARGS: ClassVar[dict[str, Any]] = {
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"temperature": 0.1,
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"top_p": 0.95,
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"frequency_penalty": 0.0,
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"max_tokens": 4096,
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}
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# ==================== 初始化 ====================
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def __init__(self, llm: LLMClient, config: Optional[AgentConfig] = None):
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super().__init__(config)
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self.llm = llm
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self._pending_comment: str = ""
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merged_args = dict(self.DEFAULT_MODEL_ARGS)
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merged_args.update(self.config.model_args or {})
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self.config.model_args = merged_args
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@property
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def name(self) -> str:
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return "GenericAgent"
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def reset(self, task: str) -> None:
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self._task = task
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self._history = []
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self._pending_comment = ""
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def add_user_comment(self, comment: str) -> None:
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"""添加用户回复（用于 INFO 动作）"""
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self._pending_comment = str(comment or "")
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# ==================== 响应解析 ====================
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@staticmethod
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def _extract_first_json(text: str) -> Optional[str]:
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"""从文本中提取第一个 JSON 对象"""
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s = text
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start = s.find("{")
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if start < 0:
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return None
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in_str = False
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esc = False
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depth = 0
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for i in range(start, len(s)):
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ch = s[i]
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if in_str:
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if esc:
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esc = False
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elif ch == "\\":
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esc = True
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elif ch == '"':
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in_str = False
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else:
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if ch == '"':
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in_str = True
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elif ch == "{":
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depth += 1
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elif ch == "}":
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depth -= 1
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if depth == 0:
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return s[start : i + 1]
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return None
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def _parse_llm_output(self, response_text: str) -> dict[str, Any]:
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"""解析 LLM 输出为 dict"""
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raw = str(response_text or "").strip()
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if not raw:
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return {"_error": "empty_response"}
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parsed: Any = None
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try:
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parsed = json.loads(raw)
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except Exception:
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extracted = self._extract_first_json(raw)
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if extracted:
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try:
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parsed = json.loads(extracted)
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except Exception:
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pass
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if not isinstance(parsed, dict):
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return {"_error": "invalid_json", "raw": raw}
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return parsed
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def parse_response(self, response_text: str) -> Action:
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"""解析 LLM 响应为 Action"""
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parsed = self._parse_llm_output(response_text)
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if "_error" in parsed:
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return Action(
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action_type=ActionType.ABORT,
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data={"value": parsed.get("_error", "parse_error")},
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raw_response=response_text,
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)
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action_name = str(parsed.get("action") or parsed.get("action_type") or "").strip().upper()
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thought = str(parsed.get("thought", parsed.get("cot", "")) or "")
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explain = str(parsed.get("explain", "") or "")
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return self.parse_action(
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action_name,
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parsed,
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thought=thought,
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explain=explain,
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raw_response=response_text,
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)
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# ==================== 消息构建 ====================
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def _format_history(self, max_items: int = 6) -> str:
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"""格式化历史操作"""
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if not self._history:
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return "[]"
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items = self._history[-max_items:]
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out = []
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for s in items:
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out.append({
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"step": s.step_idx,
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"action": s.action.action_type,
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"data": s.action.data,
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})
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return json.dumps(out, ensure_ascii=False)
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def build_messages(self, obs: Observation) -> list[dict]:
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"""构建发送给 LLM 的消息"""
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route_app = obs.current_app or ""
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route_path = obs.route.get("path", "") if obs.route else ""
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user_comment = ""
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if self._pending_comment:
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user_comment = f"\n\n[用户补充信息]\n{self._pending_comment}\n"
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text = f"""[任务]
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{self._task}{user_comment}
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[当前路由]
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app={route_app} path={route_path}
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[历史操作]
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{self._format_history()}
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[当前截图]
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"""
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return [
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{"role": "system", "content": self.SYSTEM_PROMPT},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": text},
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{"type": "image_url", "image_url": {"url": obs.image_data_url}},
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{"type": "text", "text": "\n请只输出 JSON。"},
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],
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},
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]
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# ==================== 核心逻辑 ====================
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def act(self, obs: Observation) -> Action:
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"""生成动作"""
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messages = self.build_messages(obs)
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if self.config.verbose:
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print(f"\n[GenericAgent] Step {obs.step_idx}, sending prompt...")
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response = self.llm.chat(
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messages=messages,
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args={
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**self.config.model_args,
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"stream": self.config.stream,
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"stream_print": self.config.stream and self.config.verbose,
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},
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)
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if self.config.verbose and not self.config.stream:
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print(f"\n[LLM Response]\n{response.content}\n")
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action = self.parse_response(response.content)
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self._history.append(AgentStepRecord(
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step_idx=obs.step_idx,
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observation=obs,
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action=action,
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llm_response=response.content,
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llm_prompt=messages,
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user_comment=self._pending_comment,
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))
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# 内存瘦身：历史仅用文本，保留最近 2 条完整记录
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self._evict_old_records(keep_recent=2)
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self._pending_comment = ""
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if self.config.verbose:
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print(f"[GenericAgent] Action: {action.action_type}, Data: {action.data}")
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return action
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