Simon
d9be75a98f
- agent/tools.py：Tool 元数据（JSON Schema）+ 白名单调用（异常转可读反馈，不中断对话） - agent/tools_impl.py：6 个受控工具 search_stocks / get_market_data / test_factor / run_backtest / get_experiment / compare_experiments —— 全部只读经 Job/Experiment 链路，研究自动归档；无 shell/任意执行/写删数据能力 - agent/llm.py：LLMClient 抽象 + OpenAI 兼容客户端（LLM_API_KEY/LLM_BASE_URL/LLM_MODEL 走 .env，未配置给出引导提示）+ 研究纪律 system prompt（反过拟合/样本外/成本） - agent/service.py：编排循环（tool/final JSON 决策 → 执行 → 回喂 → 结论），轮次上限兜底，未知工具拒绝 - /api/agent/chat；httpx 移至主依赖；Job 默认工厂抽取（api/agent/executor 复用） - 测试 6 项（白名单无 shell、完整研究循环产出、未知工具拒绝、轮次兜底），全量 79 passed / ruff clean
99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
"""Agent 编排：自然语言 → 受控工具调用循环 → 结论（AGENT.md §29 假设-实验-分析循环）。"""
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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 typing import Any
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from app.agent.llm import SYSTEM_TEMPLATE, LLMClient
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from app.agent.tools import Tool, tools_schema
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from app.agent.tools_impl import build_tools
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MAX_TOOL_ROUNDS = 5
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_DECISION_PATTERN = re.compile(r"\{.*\}", re.DOTALL)
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def _parse_decision(text: str) -> dict[str, Any]:
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"""容忍 LLM 输出中的代码块/前后缀，提取首个 JSON 对象。"""
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match = _DECISION_PATTERN.search(text)
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if not match:
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raise ValueError(f"无法从模型输出中解析动作 JSON：{text[:200]}")
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try:
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return json.loads(match.group(0))
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except json.JSONDecodeError as exc:
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raise ValueError(f"模型输出的 JSON 不合法：{text[:200]}") from exc
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class AgentService:
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"""受控研究 Agent：每轮让 LLM 决策 tool/final，执行工具并把结果回喂，直至 final。"""
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def __init__(self, llm: LLMClient, tools: list[Tool] | None = None) -> None:
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self._llm = llm
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self._tools = {t.name: t for t in (tools or build_tools())}
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def _by_name(self, name: str) -> Tool:
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tool = self._tools.get(name)
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if tool is None:
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raise ValueError(f"工具不存在：{name}（可用 {sorted(self._tools)}）")
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return tool
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def chat(self, message: str, max_rounds: int = MAX_TOOL_ROUNDS) -> dict:
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messages: list[dict] = [
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{
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"role": "system",
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"content": SYSTEM_TEMPLATE.format(tools=tools_schema(list(self._tools.values()))),
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},
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{"role": "user", "content": message},
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]
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actions: list[dict] = []
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for _round in range(max_rounds):
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try:
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decision = _parse_decision(self._llm.chat(messages))
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except ValueError as exc:
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# LLM 回复格式异常：回传错误要求重试一次结构化输出
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messages.append(
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{
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"role": "user",
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"content": f"输出格式错误，请只输出一行 JSON（tool 或 final）：{exc}",
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}
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)
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continue
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if "final" in decision:
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return {"reply": str(decision["final"]), "actions": actions}
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tool_name = str(decision.get("tool", ""))
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args = decision.get("args") or {}
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if not isinstance(args, dict):
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args = {}
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tool = self._by_name(tool_name) # 白名单之外的调用直接报错
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output = tool.invoke(args)
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actions.append({"tool": tool_name, "args": args, "output": output[:1000]})
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messages.append(
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{
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"role": "assistant",
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"content": f'{{"tool": "{tool_name}", "args": {json.dumps(args, ensure_ascii=False)}}}',
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}
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)
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messages.append(
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{
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"role": "user",
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"content": f"工具 {tool_name} 返回：\n{output}\n请继续（如需再调用输出 tool，否则输出 final）。",
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}
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)
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# 轮次耗尽：强制收尾
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messages.append(
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{
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"role": "user",
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"content": "已达工具调用轮次上限，请直接给出基于已有事实的最终结论（只输出 final JSON）。",
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}
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)
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try:
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decision = _parse_decision(self._llm.chat(messages))
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except ValueError:
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decision = {
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"final": "研究轮次耗尽且模型未给出结构化结论，请人工查看 actions 中的实验输出。"
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}
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return {"reply": str(decision.get("final", decision)), "actions": actions}
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