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