119 lines
5.4 KiB
Python
119 lines
5.4 KiB
Python
"""自主学习闭环第一步：任务成功后自动生成 learned 草稿。
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来源两路：
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1. 产物直接映射：entry_url -> 入口地址，form_fields -> 表单字段，menus -> 菜单结构
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2. 一次模型调用：从 Agent 的总结和访问过的页面里提炼"下次同类任务能直接复用的操作知识"
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草稿写入 learned_entries(status=draft)，用户在桌面端采纳后才会注入 prompt。
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"""
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import json
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from typing import Any
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from backend.browser.llm_factory import get_llm_config
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from backend.nlu import _call_llm
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ARTIFACT_KEYS = {
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"entry_url": "入口地址",
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"form_fields": "表单字段",
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"menus": "菜单结构",
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}
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MAX_ENTRIES = 8
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def _is_submit_step(step: dict) -> bool:
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return bool(step.get("human_confirm_before") or step.get("on_success_status") == "submitted")
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def _from_artifacts(artifacts: dict, step: dict) -> list[dict]:
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entries = []
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for art_key, learned_key in ARTIFACT_KEYS.items():
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value = artifacts.get(art_key)
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if not value:
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continue
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# 填报类步骤的 form_fields 是本次填的具体值（班级、人名…），不是表单结构，不能当知识
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if art_key == "form_fields" and _is_submit_step(step):
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continue
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entries.append({"key": learned_key, "value": str(value).strip()[:2000], "category": "path"})
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return entries
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def _build_prompt(
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business: dict, step: dict, summary: str, history: dict, existing: dict, existing_drafts: dict | None = None
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) -> str:
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urls = history.get("urls") or []
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existing_lines = "\n".join(f"- {k}：{str(v)[:120]}" for k, v in existing.items()) or "（无）"
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draft_lines = "\n".join(f"- {k}：{str(v)[:120]}" for k, v in (existing_drafts or {}).items()) or "（无）"
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return "\n".join(
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[
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"你是学校办事助手的知识沉淀模块。下面是一次成功执行的记录，请提炼出「下次执行同类任务时能直接复用的操作知识」。",
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"",
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f"业务：{business.get('name', '')}",
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f"本步目标：{step.get('goal', '')}",
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f"访问过的页面（按顺序）：{json.dumps(urls[:20], ensure_ascii=False)}",
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f"执行步数：{history.get('steps')}，用时 {history.get('duration_s')} 秒",
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"",
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"Agent 的执行总结：",
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summary[:3000],
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"",
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"已经生效的知识（不要重复）：",
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existing_lines,
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"",
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"待审核的草稿（如果这次又观察到同一条知识，请沿用完全相同的 key，value 可以补充得更完整；这是它被多次验证的依据）：",
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draft_lines,
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"",
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"要求：",
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"1. 只记录操作知识：入口路径、控件操作技巧（如某个下拉要点文字容器才能选中、日期要键入后回车）、系统规则和前置条件、容易踩的坑",
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"2. 不要记录本次的具体业务数据：班级、日期、人名、课程名、填了什么内容都不要",
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"2b. 不要记录本次用户临时指令的处理方式：取消、测试、干跑、评测、不要提交这类都不是知识",
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"2c. 不要复述任务提示里已经写明的流程（如提交前要确认），只记录执行中新发现的东西",
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"3. 每条 key 用 4 到 12 个字的名词短语，value 用一到三句话，具体可执行",
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"4. 每条给 category：control_tip（控件操作技巧）/ path（入口路径、页面地址、菜单结构）/ rule（系统规则、前置条件、达标要求、审批流程）/ note（其他）",
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"5. 最多 5 条，没有新知识就返回空数组",
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'输出 JSON：{"entries": [{"key": "...", "value": "...", "category": "control_tip"}]}',
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]
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)
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async def draft_learned(
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business: dict,
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step: dict,
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artifacts: dict,
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history: dict | None,
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summary: str,
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existing: dict | None = None,
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existing_drafts: dict | None = None,
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) -> tuple[list[dict], str]:
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"""返回 (草稿列表, 备注)。每条 {key, value, category}。模型不可用时只返回产物映射的条目。"""
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existing = existing or {}
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entries: list[dict] = _from_artifacts(artifacts or {}, step)
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note = "产物映射"
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cfg = get_llm_config()
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if cfg.get("api_key"):
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try:
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result: Any = await _call_llm(
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_build_prompt(business, step, summary or "", history or {}, existing, existing_drafts), cfg
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)
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llm_entries = (result or {}).get("entries") or []
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for e in llm_entries:
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if not isinstance(e, dict):
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continue
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key = str(e.get("key") or "").strip()
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value = str(e.get("value") or "").strip()
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category = str(e.get("category") or "note").strip()
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if key and value:
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entries.append({"key": key[:128], "value": value[:2000], "category": category})
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note = f"产物映射 + 模型提炼 {len(llm_entries)} 条"
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except Exception as e: # noqa: BLE001
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note = f"产物映射（模型提炼失败：{type(e).__name__}）"
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seen: set[str] = set()
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unique: list[dict] = []
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for e in entries:
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if e["key"] in seen:
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continue
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if existing.get(e["key"]) == e["value"]:
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continue
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seen.add(e["key"])
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unique.append(e)
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return unique[:MAX_ENTRIES], note
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