167 lines
5.2 KiB
Python
167 lines
5.2 KiB
Python
"""洛书枢 deliberative 规划器领域模块。
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纯数据结构和纯函数（禁止 IO），定义显式计划的数据模型与解析逻辑。
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IO 相关编排在 ``app/application/planning/`` 中完成。
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"""
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from __future__ import annotations
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import json
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import re
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from dataclasses import dataclass, field
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@dataclass
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class PlanStep:
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"""计划步骤。"""
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id: int
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title: str
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description: str
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tool: str | None = None
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risk_level: str = "L2"
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@dataclass
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class DeliberativePlan:
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"""显式计划。"""
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title: str
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steps: list[PlanStep]
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assumptions: list[str] = field(default_factory=list)
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risks: list[str] = field(default_factory=list)
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def parse_plan(raw: str) -> DeliberativePlan | None:
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"""解析 LLM 返回的 JSON 计划文本。
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Args:
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raw: LLM 原始输出（可能含 ```json 围栏）。
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Returns:
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解析成功的 DeliberativePlan，失败返回 None。
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"""
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text = raw.strip()
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matched = re.search(r"```(?:json)?\s*(\{.*?\})\s*```", text, re.DOTALL)
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if matched:
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text = matched.group(1)
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else:
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brace_start = text.find("{")
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brace_end = text.rfind("}")
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if brace_start != -1 and brace_end > brace_start:
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text = text[brace_start : brace_end + 1]
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try:
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data = json.loads(text)
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except json.JSONDecodeError:
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return None
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if not isinstance(data, dict):
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return None
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title = str(data.get("title", "")).strip()
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raw_steps = data.get("steps", [])
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if not title or not isinstance(raw_steps, list) or not raw_steps:
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return None
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steps: list[PlanStep] = []
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for i, s in enumerate(raw_steps):
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if not isinstance(s, dict):
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continue
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step_title = str(s.get("title", "")).strip()
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if not step_title:
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continue
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tool_raw = str(s.get("tool", "")).strip()
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tool = tool_raw if tool_raw and tool_raw != "None" else None
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steps.append(
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PlanStep(
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id=s.get("id", i + 1),
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title=step_title,
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description=str(s.get("description", "")).strip(),
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tool=tool,
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risk_level=str(s.get("risk_level", "L2")).upper(),
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)
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)
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if not steps:
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return None
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return DeliberativePlan(
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title=title,
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steps=steps,
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assumptions=[str(a).strip() for a in data.get("assumptions", []) if a],
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risks=[str(r).strip() for r in data.get("risks", []) if r],
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)
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def build_planning_prompt(
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request: str,
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*,
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goal_directive: str = "",
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available_tools: list[str] | None = None,
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memory_context: str = "",
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) -> str:
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"""构建规划 prompt。
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当请求明显需要多步骤（调用多个工具、查询多处信息等）时，
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要求 LLM 先生成一个显式计划，再按计划逐步执行。
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Args:
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request: 用户请求文本。
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goal_directive: 当前活跃目标描述。
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available_tools: 可用工具列表。
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memory_context: 相关记忆上下文。
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Returns:
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规划 prompt 文本。
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"""
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tools_str = ""
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if available_tools:
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tools_str = "\n可用工具：\n" + "\n".join(f"- {t}" for t in available_tools)
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return (
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"请判断以下用户请求是否需要多步计划。如果需要，请输出 JSON 格式的计划。\n\n"
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f"用户请求：{request}\n"
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f"{f'当前目标：{goal_directive}' if goal_directive else ''}"
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f"{tools_str}"
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f"{f'\n上下文记忆：{memory_context}' if memory_context else ''}"
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"\n\n"
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"输出格式（如需多步计划）：\n"
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'```json\n{\n "title": "计划标题",\n'
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' "assumptions": ["假设1", "假设2"],\n'
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' "risks": ["风险1", "风险2"],\n'
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' "steps": [\n'
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' {"id": 1, "title": "步骤1", "description": "说明", '
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'"tool": "工具名（可选）", "risk_level": "L2"}\n'
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" ]\n"
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"}\n```\n\n"
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"如果不需要多步计划（简单问题），只回复：NO_PLAN_NEEDED"
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)
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def format_plan_for_prompt(plan: DeliberativePlan) -> str:
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"""将计划格式化为 system prompt 可用文本。"""
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lines = [f"## 计划：{plan.title}\n"]
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if plan.assumptions:
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lines.append("假设：")
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lines.extend(f"- {a}" for a in plan.assumptions)
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lines.append("")
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if plan.risks:
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lines.append("风险：")
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lines.extend(f"- {r}" for r in plan.risks)
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lines.append("")
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lines.append("执行步骤：")
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for step in plan.steps:
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tool_info = f" [工具：{step.tool}]" if step.tool else ""
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lines.append(f"{step.id}. {step.title}{tool_info}")
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if step.description:
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lines.append(f" {step.description}")
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return "\n".join(lines)
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def should_plan(request: str) -> bool:
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"""判断请求是否需要多步计划。
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简单规则：包含需要多工具的关键词时返回 True。
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"""
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triggers = [
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"搜索", "查找", "研究", "调研", "分析", "比较",
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"整理", "总结", "汇总", "规划", "安排", "计划",
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"search", "research", "analyze", "compare",
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"summarize", "plan", "organize",
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]
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return any(t in request for t in triggers)
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