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import ast
import json
import os
import re
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
@dataclass
class ToolCall:
name: str
args: Dict[str, Any]
@dataclass
class ToolResult:
ok: bool
output: str
artifacts: List[str]
def _tokenize(s: str) -> List[str]:
s = s.lower()
s = re.sub(r"[^0-9a-z\u4e00-\u9fff]+", " ", s)
parts = [p.strip() for p in s.split() if p.strip()]
return parts
def tool_search_kb(query: str, kb_dir: str, top_k: int = 4) -> ToolResult:
base = Path(kb_dir)
if not base.exists():
return ToolResult(ok=True, output="[]", artifacts=[])
q_tokens = _tokenize(query)
if not q_tokens:
return ToolResult(ok=True, output="[]", artifacts=[])
hits: List[Tuple[int, str, str]] = []
for p in sorted(base.glob("**/*.md")):
try:
text = p.read_text(encoding="utf-8")
except Exception:
continue
chunks = [c.strip() for c in re.split(r"\n\s*\n+", text) if c.strip()]
for c in chunks:
c_tokens = _tokenize(c)
score = sum(1 for t in q_tokens if t in c_tokens)
if score <= 0:
continue
hits.append((score, str(p), c))
hits.sort(key=lambda x: (-x[0], x[1]))
picked = hits[: max(0, int(top_k))]
out = [{"score": s, "source": src, "text": txt} for (s, src, txt) in picked]
return ToolResult(ok=True, output=json.dumps(out, ensure_ascii=False, indent=2), artifacts=[])
class _SafeEval(ast.NodeVisitor):
def visit(self, node):
return super().visit(node)
def generic_visit(self, node):
raise ValueError(f"unsupported: {node.__class__.__name__}")
def visit_Expression(self, node: ast.Expression):
return self.visit(node.body)
def visit_BinOp(self, node: ast.BinOp):
left = self.visit(node.left)
right = self.visit(node.right)
if isinstance(node.op, ast.Add):
return left + right
if isinstance(node.op, ast.Sub):
return left - right
if isinstance(node.op, ast.Mult):
return left * right
if isinstance(node.op, ast.Div):
return left / right
if isinstance(node.op, ast.FloorDiv):
return left // right
if isinstance(node.op, ast.Mod):
return left % right
if isinstance(node.op, ast.Pow):
return left ** right
raise ValueError("unsupported operator")
def visit_UnaryOp(self, node: ast.UnaryOp):
v = self.visit(node.operand)
if isinstance(node.op, ast.UAdd):
return +v
if isinstance(node.op, ast.USub):
return -v
raise ValueError("unsupported unary operator")
def visit_Constant(self, node: ast.Constant):
if isinstance(node.value, (int, float)):
return node.value
raise ValueError("only numbers allowed")
def tool_calc(expression: str) -> ToolResult:
expr = expression.strip()
if not re.fullmatch(r"[0-9\.\s\+\-\*\/\(\)\%\^]+", expr):
return ToolResult(ok=False, output="表达式包含不允许的字符", artifacts=[])
expr = expr.replace("^", "**")
try:
tree = ast.parse(expr, mode="eval")
value = _SafeEval().visit(tree)
except Exception as e:
return ToolResult(ok=False, output=f"计算失败: {e}", artifacts=[])
return ToolResult(ok=True, output=str(value), artifacts=[])
def tool_write_text(path: str, text: str) -> ToolResult:
p = Path(path)
p.parent.mkdir(parents=True, exist_ok=True)
p.write_text(text, encoding="utf-8")
return ToolResult(ok=True, output=f"wrote: {p}", artifacts=[str(p)])
def tool_read_text(path: str) -> ToolResult:
p = Path(path)
if not p.exists():
return ToolResult(ok=False, output="not found", artifacts=[])
return ToolResult(ok=True, output=p.read_text(encoding="utf-8"), artifacts=[])
def _extract_math_expr(goal: str) -> Optional[str]:
m = re.search(r"(\d[\d\s\+\-\*\/\(\)\%\^\.]*\d)", goal)
if not m:
return None
expr = m.group(1)
expr = re.sub(r"\s+", "", expr)
if len(expr) > 120:
return None
if not re.search(r"[\+\-\*\/\%\^]", expr):
return None
return expr
def _extract_faq_count(goal: str) -> Optional[int]:
m = re.search(r"(\d+)\s*条\s*FAQ", goal, flags=re.IGNORECASE)
if m:
return max(1, min(20, int(m.group(1))))
m = re.search(r"(\d+)\s*条", goal)
if m and ("faq" in goal.lower() or "常见问题" in goal):
return max(1, min(20, int(m.group(1))))
if "faq" in goal.lower() or "常见问题" in goal:
return 5
return None
def _norm_space(s: str) -> str:
return re.sub(r"\s+", " ", (s or "").strip())
def _lead_to_goal(lead: Dict[str, Any]) -> str:
company = _norm_space(str(lead.get("company") or ""))
contact = _norm_space(str(lead.get("contact") or ""))
role = _norm_space(str(lead.get("role") or ""))
channel = _norm_space(str(lead.get("channel") or ""))
product = _norm_space(str(lead.get("product") or ""))
stage = _norm_space(str(lead.get("stage") or ""))
pain = _norm_space(str(lead.get("pain_points") or ""))
budget = _norm_space(str(lead.get("budget") or ""))
timeline = _norm_space(str(lead.get("timeline") or ""))
notes = _norm_space(str(lead.get("notes") or ""))
parts: List[str] = []
parts.append("生成一份销售线索跟进方案（含补问清单、下一步任务清单、跟进话术：微信/电话/邮件、风险提示），并写进报告。")
if company:
parts.append(f"公司/组织：{company}")
if contact or role:
who = contact if contact else "（未提供姓名）"
if role:
who = f"{who}（{role}）"
parts.append(f"联系人：{who}")
if channel:
parts.append(f"来源渠道：{channel}")
if product:
parts.append(f"关注产品：{product}")
if stage:
parts.append(f"当前阶段：{stage}")
if pain:
parts.append(f"主要诉求/痛点：{pain}")
if budget:
parts.append(f"预算：{budget}")
if timeline:
parts.append(f"期望时间：{timeline}")
if notes:
parts.append(f"备注：{notes}")
return "\n".join(parts)
def make_plan(goal: str, kb_dir: str, out_report: str) -> List[ToolCall]:
plan: List[ToolCall] = []
plan.append(ToolCall(name="search_kb", args={"query": goal, "kb_dir": kb_dir, "top_k": 6}))
expr = _extract_math_expr(goal)
if expr:
plan.append(ToolCall(name="calc", args={"expression": expr}))
plan.append(ToolCall(name="write_report", args={"goal": goal, "out_report": out_report}))
return plan
def _format_faq_items(snippets: List[Dict[str, Any]], n: int) -> List[str]:
pool: List[str] = []
for s in snippets:
t = str(s.get("text", "")).strip()
t = re.sub(r"\s+", " ", t)
if len(t) >= 12:
pool.append(t)
if not pool:
pool = [
"我们提供从获客到转化的自动化流程编排能力，减少重复操作。",
"支持接入常见广告与社媒渠道，并统一沉淀线索与素材。",
"关键数据可追踪，可用规则触发通知与任务分配。",
"提供基础的权限与审计，方便多人协作与风控。",
"支持将常见问题与业务规范整理进知识库，供智能体检索。",
]
items: List[str] = []
i = 0
while len(items) < n:
items.append(pool[i % len(pool)])
i += 1
return items
def _render_report(goal: str, snippets: List[Dict[str, Any]], faq_count: Optional[int], math_expr: Optional[str], math_result: Optional[str]) -> str:
lines: List[str] = []
lines.append("# 智能体生成报告")
lines.append("")
lines.append("## 目标")
lines.append(goal.strip())
lines.append("")
if snippets:
lines.append("## 知识库检索摘要")
for i, s in enumerate(snippets[:4], start=1):
src = os.path.basename(str(s.get("source", "")))
txt = str(s.get("text", "")).strip().replace("\n", " ")
txt = re.sub(r"\s+", " ", txt)
lines.append(f"- 片段{i}（{src}）：{txt}")
lines.append("")
if faq_count:
lines.append(f"## FAQ（{faq_count} 条）")
items = _format_faq_items(snippets, faq_count)
for it in items:
lines.append(f"- {it}")
lines.append("")
if math_expr:
lines.append("## 计算结果")
if math_result is None:
lines.append(f"- 表达式：{math_expr}")
lines.append("- 结果：计算失败")
else:
lines.append(f"- 表达式：{math_expr}")
lines.append(f"- 结果：{math_result}")
lines.append("")
lines.append("## 结论")
lines.append("已基于本地知识库完成检索，并生成可复核的报告工件。")
lines.append("")
return "\n".join(lines)
def _lead_sections(goal: str, snippets: List[Dict[str, Any]]) -> Dict[str, Any]:
lines = goal.splitlines()
facts: Dict[str, str] = {}
for ln in lines:
if "：" in ln:
k, v = ln.split("：", 1)
k = _norm_space(k)
v = _norm_space(v)
if k and v:
facts[k] = v
company = facts.get("公司/组织", "")
product = facts.get("关注产品", "")
stage = facts.get("当前阶段", "")
pain = facts.get("主要诉求/痛点", "")
budget = facts.get("预算", "")
timeline = facts.get("期望时间", "")
summary = "；".join([p for p in [company, product, stage, pain] if p]) or "已记录线索信息。"
questions: List[str] = [
"当前目标是什么（增长/获客/转化/复购/效率/风控）？优先级如何排序？",
"现有流程里最耗时或最容易丢信息的环节是哪一步？",
"线索来源结构与规模（渠道、日均量、转化率、客单价）大概是多少？",
"团队协作方式（谁负责、如何交接、是否需要权限与审计）？",
"期望在多长周期内看到可量化改进？评估指标是什么？",
]
if budget:
questions.append("预算口径（一次性/按月/按量）与采购流程（招采/合同/付款节点）？")
if timeline:
questions.append("上线时间点是否有外部约束（活动/发布/投放节奏）？")
tasks: List[Dict[str, str]] = [
{"task": "补齐关键字段：目标、现状流程、线索规模、关键指标", "owner": "你", "due": "今天"},
{"task": "确认决策链路：需求方/使用方/审批方/预算方", "owner": "你", "due": "今天"},
{"task": "整理 3 个典型线索样本与跟进记录（脱敏）", "owner": "客户", "due": "本周"},
{"task": "对齐一版 SOP：触达→跟进→转化→复盘的关键动作与责任人", "owner": "你", "due": "本周"},
{"task": "输出落地方案：自动化触发点、权限与审计、指标口径", "owner": "你", "due": "本周"},
{"task": "安排演示/试用：用样本数据跑一遍闭环", "owner": "你", "due": "下周"},
]
wechat = "我先基于你们当前的线索流程整理一版“可落地的跟进方案+自动化触发点”，你看方便补充下：目标/线索规模/当前痛点三项吗？补齐后我今天给你一版可执行清单。"
phone = "你好，我是这边负责增长流程自动化方案的。想快速了解下：你们目前线索从哪个渠道进来、谁负责跟进、最容易卡在哪一步？我这边把方案做成你们团队可直接执行的清单。"
email = "\n".join(
[
"主题：线索跟进闭环方案梳理（补问清单 + 可执行任务）",
"",
"你好，",
"",
"我已根据当前线索信息先拟了一版跟进方案（含补问清单、下一步任务、话术与风险点）。",
"为保证方案能直接落地，麻烦补充 3 点：目标/线索规模/当前流程卡点（可简单一句话）。",
"",
"我收到后会在当天给出：",
"1) 可执行的任务清单（负责人/截止时间）",
"2) 自动化触发点建议",
"3) 指标口径与复盘方式",
"",
"谢谢",
]
)
risks = [
"对外触达需避免夸大承诺，话术以“可量化目标/可验证步骤”为准。",
"涉及个人信息与线索数据时，需明确数据来源与留存周期，按权限最小化原则访问。",
"对接渠道 API/表单时，先做字段映射与去重规则，避免重复触达与口径混乱。",
]
cited: List[str] = []
for s in snippets[:3]:
src = os.path.basename(str(s.get("source", "")))
txt = _norm_space(str(s.get("text", "")).replace("\n", " "))
if txt:
cited.append(f"{src}：{txt}")
return {
"summary": summary,
"questions": questions[:7],
"tasks": tasks,
"scripts": {"wechat": wechat, "phone": phone, "email": email},
"risks": risks,
"cited": cited,
}
def _render_lead_report(goal: str, snippets: List[Dict[str, Any]]) -> Tuple[str, Dict[str, Any]]:
data = _lead_sections(goal=goal, snippets=snippets)
lines: List[str] = []
lines.append("# 线索跟进方案")
lines.append("")
lines.append("## 线索摘要")
lines.append(data["summary"])
lines.append("")
lines.append("## 需要补问（建议按顺序）")
for q in data["questions"]:
lines.append(f"- {q}")
lines.append("")
lines.append("## 下一步任务清单")
for t in data["tasks"]:
lines.append(f"- {t['task']}（负责人：{t['owner']}；截止：{t['due']}）")
lines.append("")
lines.append("## 跟进话术")
lines.append("### 微信/IM")
lines.append(data["scripts"]["wechat"])
lines.append("")
lines.append("### 电话开场")
lines.append(data["scripts"]["phone"])
lines.append("")
lines.append("### 邮件")
lines.append(data["scripts"]["email"])
lines.append("")
lines.append("## 风险提示")
for r in data["risks"]:
lines.append(f"- {r}")
lines.append("")
if data["cited"]:
lines.append("## 参考知识片段")
for c in data["cited"]:
lines.append(f"- {c}")
lines.append("")
lines.append("## 原始目标")
lines.append(goal.strip())
lines.append("")
return "\n".join(lines), data
def _needs_report(goal: str) -> bool:
g = goal.lower()
if "报告" in goal or "写进报告" in goal or "写入报告" in goal:
return True
if "report" in g:
return True
return True
def check_closure(goal: str, out_report: str, mode: str) -> Tuple[bool, str]:
p = Path(out_report)
if _needs_report(goal):
if not p.exists():
return False, "报告文件不存在"
txt = p.read_text(encoding="utf-8")
if mode == "lead_followup":
if "线索跟进方案" not in txt:
return False, "报告缺少标题"
if "需要补问" not in txt or "下一步任务清单" not in txt or "跟进话术" not in txt:
return False, "报告结构不完整"
if len(re.findall(r"^\-\s+", txt, flags=re.MULTILINE)) < 8:
return False, "清单项不足"
else:
if "智能体生成报告" not in txt:
return False, "报告缺少标题"
faq_count = _extract_faq_count(goal)
if faq_count:
if len(re.findall(r"^\-\s+", txt, flags=re.MULTILINE)) < faq_count:
return False, "FAQ 条数不足"
expr = _extract_math_expr(goal)
if expr:
if expr not in txt:
return False, "报告未包含表达式"
calc_res = tool_calc(expr)
if calc_res.ok and str(calc_res.output) not in txt:
return False, "报告未包含计算结果"
return True, "ok"
def run_agent(goal: str, kb_dir: str, out_report: str, max_rounds: int = 3, mode: str = "general") -> Dict[str, Any]:
round_logs: List[Dict[str, Any]] = []
state: Dict[str, Any] = {"snippets": [], "math_expr": None, "math_result": None, "artifacts": [], "structured": None}
for r in range(1, max_rounds + 1):
plan = make_plan(goal=goal, kb_dir=kb_dir, out_report=out_report)
step_logs: List[Dict[str, Any]] = []
for step in plan:
if step.name == "search_kb":
res = tool_search_kb(**step.args)
if res.ok:
try:
state["snippets"] = json.loads(res.output)
except Exception:
state["snippets"] = []
step_logs.append({"tool": step.name, "ok": res.ok, "output": res.output[:800]})
continue
if step.name == "calc":
state["math_expr"] = step.args.get("expression")
res = tool_calc(**step.args)
state["math_result"] = res.output if res.ok else None
step_logs.append({"tool": step.name, "ok": res.ok, "output": res.output})
continue
if step.name == "write_report":
faq_count = _extract_faq_count(goal)
if mode == "lead_followup":
report, structured = _render_lead_report(goal=goal, snippets=state.get("snippets") or [])
state["structured"] = structured
else:
report = _render_report(
goal=goal,
snippets=state.get("snippets") or [],
faq_count=faq_count,
math_expr=state.get("math_expr"),
math_result=state.get("math_result"),
)
res = tool_write_text(path=out_report, text=report)
state["artifacts"].extend(res.artifacts)
step_logs.append({"tool": step.name, "ok": res.ok, "output": res.output})
continue
step_logs.append({"tool": step.name, "ok": False, "output": "unknown tool"})
ok, reason = check_closure(goal=goal, out_report=out_report, mode=mode)
round_logs.append({"round": r, "plan": [c.__dict__ for c in plan], "steps": step_logs, "check": {"ok": ok, "reason": reason}})
if ok:
break
final = []
final.append("已完成闭环执行：规划→工具执行→自检→产出。")
final.append(f"报告工件：{out_report}")
expr = state.get("math_expr")
if expr:
final.append(f"计算：{expr} = {state.get('math_result')}")
faq_count = _extract_faq_count(goal)
if faq_count:
final.append(f"FAQ：{faq_count} 条（见报告）")
return {
"goal": goal,
"out_report": out_report,
"round_logs": round_logs,
"final_answer": "\n".join(final),
"artifacts": state.get("artifacts"),
"structured": state.get("structured"),
"mode": mode,
}
def run_agent_mode(mode: str, payload: Dict[str, Any], kb_dir: str, out_report: str, max_rounds: int = 3) -> Dict[str, Any]:
mode = (mode or "general").strip()
if mode == "lead_followup":
lead = payload.get("lead") or {}
goal = _lead_to_goal(lead if isinstance(lead, dict) else {})
result = run_agent(goal=goal, kb_dir=kb_dir, out_report=out_report, max_rounds=max_rounds, mode=mode)
return result
goal = str(payload.get("goal") or "").strip()
return run_agent(goal=goal, kb_dir=kb_dir, out_report=out_report, max_rounds=max_rounds, mode="general")
def _print_round_logs(round_logs: List[Dict[str, Any]]) -> None:
for r in round_logs:
print(f"\n=== Round {r['round']} ===")
print("Plan:")
for s in r["plan"]:
print(f"- {s['name']} {json.dumps(s['args'], ensure_ascii=False)}")
print("Steps:")
for s in r["steps"]:
out = s["output"]
out = out.replace("\n", " ")
if len(out) > 180:
out = out[:180] + "..."
print(f"- {s['tool']}: ok={s['ok']} output={out}")
print(f"Check: ok={r['check']['ok']} reason={r['check']['reason']}")
def cmd_demo() -> int:
here = Path(__file__).resolve().parent
goal = "基于知识库，生成一份 5 条 FAQ，并计算 17*23 的结果写进报告"
out_report = str(here / "out" / "demo_report.md")
kb_dir = str(here / "kb")
result = run_agent(goal=goal, kb_dir=kb_dir, out_report=out_report, max_rounds=3)
_print_round_logs(result["round_logs"])
print("\n=== Final ===")
print(result["final_answer"])
try:
txt = (here / "out" / "demo_report.md").read_text(encoding="utf-8")
head = "\n".join(txt.splitlines()[:22])
print("\n=== Report Preview (Top) ===")
print(head)
except Exception:
pass
return 0
def cmd_run(goal: str) -> int:
here = Path(__file__).resolve().parent
out_report = str(here / "out" / "demo_report.md")
kb_dir = str(here / "kb")
result = run_agent(goal=goal, kb_dir=kb_dir, out_report=out_report, max_rounds=3)
_print_round_logs(result["round_logs"])
print("\n=== Final ===")
print(result["final_answer"])
return 0
def main(argv: List[str]) -> int:
p = argparse.ArgumentParser(prog="agent.py")
sub = p.add_subparsers(dest="cmd", required=True)
sub.add_parser("demo")
p_run = sub.add_parser("run")
p_run.add_argument("--goal", required=True)
args = p.parse_args(argv)
if args.cmd == "demo":
return cmd_demo()
if args.cmd == "run":
return cmd_run(goal=args.goal)
return 2
if __name__ == "__main__":
raise SystemExit(main(sys.argv[1:]))
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