331 lines
12 KiB
Python
331 lines
12 KiB
Python
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"""
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Agent Runtime 核心 —— 自主 ReAct 循环。
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流程：
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1. 接收用户输入 → 追加到消息列表
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2. 调用 LLM（携带 tools schema）
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3. 如果 LLM 返回工具调用 → 执行工具 → 结果追加到消息列表 → 回到 2
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4. 如果 LLM 返回文本 → 作为最终回答返回
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5. 超过 max_iterations → 强制终止
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"""
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from __future__ import annotations
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import json
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import logging
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from typing import Any, Callable, Dict, List, Optional
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from app.agent_runtime.schemas import (
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AgentConfig,
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AgentResult,
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)
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from app.agent_runtime.context import AgentContext
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from app.agent_runtime.memory import AgentMemory
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from app.agent_runtime.tool_manager import AgentToolManager
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logger = logging.getLogger(__name__)
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# 可重试的 API 异常
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_RETRYABLE_ERRORS = (
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"timed out",
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"timeout",
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"connection error",
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"temporarily unavailable",
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"server disconnected",
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"rate limit",
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"too many requests",
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"internal server error",
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"service unavailable",
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)
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class AgentRuntime:
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"""
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自主 Agent 运行时。
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用法：
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runtime = AgentRuntime(config)
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result = await runtime.run("帮我写个Python脚本")
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"""
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def __init__(
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self,
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config: Optional[AgentConfig] = None,
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context: Optional[AgentContext] = None,
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memory: Optional[AgentMemory] = None,
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tool_manager: Optional[AgentToolManager] = None,
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execution_logger: Optional[Any] = None,
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on_tool_executed: Optional[Callable[[str], Any]] = None,
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):
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self.config = config or AgentConfig()
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self.context = context or AgentContext(
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system_prompt=self.config.system_prompt,
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user_id=self.config.user_id,
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)
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self.memory = memory or AgentMemory(
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scope_id=self.config.user_id or self.config.name,
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max_history=self.config.memory.max_history_messages,
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persist=self.config.memory.persist_to_db,
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)
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self.tool_manager = tool_manager or AgentToolManager(
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include_tools=self.config.tools.include_tools,
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exclude_tools=self.config.tools.exclude_tools,
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)
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self.execution_logger = execution_logger
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self.on_tool_executed = on_tool_executed
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self._memory_context_loaded = False
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async def run(self, user_input: str) -> AgentResult:
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"""
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执行 Agent 单轮对话。
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流程：加载记忆 → 追加用户消息 → ReAct 循环 → 保存记忆 → 返回结果。
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"""
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max_iter = max(1, self.config.llm.max_iterations)
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self.context.iteration = 0
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self.context.tool_calls_made = 0
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# 1. 首次运行时加载长期记忆到 system prompt
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if not self._memory_context_loaded:
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await self._inject_memory_context()
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self._memory_context_loaded = True
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# 2. 追加用户消息
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self.context.add_user_message(user_input)
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# 3. ReAct 循环
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llm = _LLMClient(self.config.llm)
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tool_schemas = self.tool_manager.get_tool_schemas()
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has_tools = self.tool_manager.has_tools()
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while self.context.iteration < max_iter:
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self.context.iteration += 1
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# 裁剪过长历史
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messages = self.memory.trim_messages(self.context.messages)
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# 调用 LLM
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try:
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response = await llm.chat(
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messages=messages,
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tools=tool_schemas if has_tools and self.context.iteration == 1 else
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(tool_schemas if has_tools else None),
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iteration=self.context.iteration,
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)
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except Exception as e:
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err_str = str(e)
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logger.error("LLM 调用失败 (iteration=%s): %s", self.context.iteration, err_str)
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if self.context.iteration < max_iter and self._is_retryable(err_str):
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continue
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return AgentResult(
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success=False,
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content=f"LLM 调用失败: {err_str}",
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iterations_used=self.context.iteration,
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tool_calls_made=self.context.tool_calls_made,
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error=err_str,
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)
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# 解析工具调用
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tool_calls = self._extract_tool_calls(response)
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content = self._extract_content(response)
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if not tool_calls:
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# LLM 直接返回文本 → 结束
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self.context.add_assistant_message(content)
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final_text = content or "（模型未返回有效内容）"
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# 保存记忆
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await self.memory.save_context(user_input, final_text)
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return AgentResult(
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success=True,
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content=final_text,
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iterations_used=self.context.iteration,
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tool_calls_made=self.context.tool_calls_made,
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)
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# 有工具调用 → 先记录 assistant 消息（含 tool_calls + reasoning_content）
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reasoning = getattr(response, "reasoning_content", None) or (
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response.get("reasoning_content") if isinstance(response, dict) else None
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)
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self.context.add_assistant_message(content or "", tool_calls, reasoning)
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if self.execution_logger:
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self.execution_logger.info(
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f"Agent 调用 {len(tool_calls)} 个工具",
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data={"tool_calls": [tc["function"]["name"] for tc in tool_calls],
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"iteration": self.context.iteration},
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)
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# 逐一执行工具
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for tc in tool_calls:
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tfn = tc.get("function", {})
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tname = tfn.get("name", "unknown")
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tcid = tc.get("id", f"call_{self.context.iteration}_{self.context.tool_calls_made}")
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try:
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targs = json.loads(tfn.get("arguments", "{}"))
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except (json.JSONDecodeError, TypeError):
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targs = {}
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logger.info("Agent 执行工具 [%s]: %s", tname, targs)
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result = await self.tool_manager.execute(tname, targs)
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self.context.add_tool_result(tcid, tname, result)
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self.context.tool_calls_made += 1
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if self.on_tool_executed:
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try:
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await self.on_tool_executed(tname)
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except Exception:
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pass
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if self.execution_logger:
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preview = result[:300] + "..." if len(result) > 300 else result
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self.execution_logger.info(
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f"工具 {tname} 执行完成",
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data={"tool_name": tname, "result_preview": preview},
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)
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# 达到最大迭代次数
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last_content = ""
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for m in reversed(self.context.messages):
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if m.get("role") == "assistant" and m.get("content"):
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last_content = m["content"]
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break
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logger.warning("Agent 达到最大迭代次数 (%s)", max_iter)
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await self.memory.save_context(user_input, last_content or "（已达最大迭代次数）")
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return AgentResult(
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success=True,
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content=last_content or "已达最大迭代次数，但模型未返回最终回答。",
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truncated=True,
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iterations_used=self.context.iteration,
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tool_calls_made=self.context.tool_calls_made,
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)
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async def _inject_memory_context(self) -> None:
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"""加载长期记忆并注入 system prompt。"""
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mem_text = await self.memory.initialize()
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if mem_text:
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enriched = (
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self.config.system_prompt.rstrip("\n")
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+ "\n\n"
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+ mem_text
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)
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self.context.set_system_prompt(enriched)
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logger.info("Agent 已注入长期记忆上下文")
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@staticmethod
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def _extract_tool_calls(response: Any) -> List[Dict[str, Any]]:
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"""从 LLM 响应中提取工具调用列表。"""
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if response is None:
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return []
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# OpenAI SDK 格式
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if hasattr(response, "tool_calls") and response.tool_calls:
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result = []
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for tc in response.tool_calls:
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result.append({
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"id": tc.id,
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"type": tc.type,
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments,
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},
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})
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return result
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# 字典格式
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if isinstance(response, dict):
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tc_list = response.get("tool_calls") or []
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if tc_list:
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return tc_list
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# 检查 content 中是否嵌入了 DSML
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content = response.get("content") or ""
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if "invoke" in content or "function_call" in content:
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from app.services.llm_service import _parse_dsml_tool_invocations
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dsml = _parse_dsml_tool_invocations(content)
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if dsml:
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return [
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{
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"id": f"dsml-{i}",
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"type": "function",
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"function": {
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"name": inv["name"],
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"arguments": json.dumps(inv["arguments"], ensure_ascii=False),
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},
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}
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for i, inv in enumerate(dsml)
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]
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return []
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@staticmethod
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def _extract_content(response: Any) -> str:
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"""从 LLM 响应中提取文本内容。"""
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if response is None:
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return ""
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if hasattr(response, "content"):
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return response.content or ""
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if isinstance(response, dict):
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return response.get("content") or ""
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return str(response)
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@staticmethod
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def _is_retryable(err_str: str) -> bool:
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"""判断错误是否可重试。"""
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err_lower = err_str.lower()
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return any(kw in err_lower for kw in _RETRYABLE_ERRORS)
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class _LLMClient:
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"""轻量 LLM 客户端包装，复用已有 LLMService 能力。"""
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def __init__(self, config: Any):
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from app.services.llm_service import llm_service
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self._service = llm_service
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self._config = config
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async def chat(
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self,
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messages: List[Dict[str, Any]],
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tools: Optional[List[Dict[str, Any]]] = None,
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iteration: int = 1,
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) -> Any:
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"""
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调用 LLM。
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优先使用 llm_service.call_openai_with_tools（支持 ReAct 的多次工具调用）。
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但为避免外层 ReAct 与内部 ReAct 冲突：
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- 第 1 轮：使用标准 chat（无内部 ReAct），由外层 AgentRuntime 控制循环
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- 后续轮次：也使用标准 chat，仅追加工具结果
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"""
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# 直接用 OpenAI/DeepSeek SDK 调用，由 AgentRuntime 控制循环
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from openai import AsyncOpenAI
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from app.core.config import settings
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# 优先从配置读取，其次从 settings（.env 加载），最后 os.environ
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api_key = self._config.api_key or settings.OPENAI_API_KEY or ""
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base_url = self._config.base_url or settings.OPENAI_BASE_URL or ""
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if not api_key or api_key == "your-openai-api-key":
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# 尝试 DeepSeek
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api_key = self._config.api_key or settings.DEEPSEEK_API_KEY or ""
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base_url = self._config.base_url or settings.DEEPSEEK_BASE_URL or "https://api.deepseek.com"
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if not api_key:
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raise ValueError("未配置 API Key")
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client = AsyncOpenAI(api_key=api_key, base_url=base_url)
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kwargs: Dict[str, Any] = {
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"model": self._config.model,
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"messages": messages,
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"temperature": self._config.temperature,
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"timeout": self._config.request_timeout,
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}
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if self._config.max_tokens:
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kwargs["max_tokens"] = self._config.max_tokens
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if self._config.extra_body:
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kwargs["extra_body"] = self._config.extra_body
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if tools:
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kwargs["tools"] = tools
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kwargs["tool_choice"] = "auto"
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response = await client.chat.completions.create(**kwargs)
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return response.choices[0].message
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