refactor: 重构 agent 节点为单步推理（移除 while 循环）
This commit is contained in:
@@ -1,15 +1,15 @@
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"""
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Agent 节点 - 简化版本
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直接定义 agent_node 函数，支持动态模型切换和循环检测
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Agent 节点 - 简化版本（单步推理）
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只负责一次 LLM 调用，不执行工具
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"""
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import hashlib
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import json
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from typing import Dict, Any, Optional, List
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from langchain_core.runnables.config import RunnableConfig
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from langchain_core.messages import AIMessage, AIMessageChunk, SystemMessage, ToolMessage
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from backend.app.main_graph.state import AgentState
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from backend.app.logger import info, error
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from backend.app.logger import info, error, debug
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from backend.app.tools import ALL_TOOLS
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from backend.app.agent.stream_context import get_stream_queue
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from backend.app.agent.prompts import SYSTEM_PROMPT
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@@ -28,7 +28,6 @@ def _is_similar_result(results: List[str], threshold: float = 0.8) -> bool:
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latest = results[-1]
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prev = results[-2]
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# 长度差异太大，不算相似
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if len(latest) == 0 or len(prev) == 0:
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return len(latest) == len(prev)
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@@ -36,7 +35,6 @@ def _is_similar_result(results: List[str], threshold: float = 0.8) -> bool:
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if len_ratio < 0.5:
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return False
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# 检查内容重复度（简单：前100字符）
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common_len = 0
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for a, b in zip(latest[:100], prev[:100]):
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if a == b:
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@@ -50,27 +48,23 @@ def _is_similar_result(results: List[str], threshold: float = 0.8) -> bool:
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def _should_stop_for_loop(tool_calls: List[dict], tool_results: List[str]) -> bool:
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"""
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检测是否应该停止（循环检测）
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条件：连续2次调用相同工具 + 参数相似 + 结果相似
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"""
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if len(tool_calls) < 2:
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return False
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# 检查最近的工具调用是否相同
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last_tc = tool_calls[-1]
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prev_tc = tool_calls[-2]
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if last_tc["name"] != prev_tc["name"]:
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return False
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# 参数是否相似
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last_args = _normalize_args(last_tc["args"])
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prev_args = _normalize_args(prev_tc["args"])
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if last_args != prev_args:
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return False
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# 结果是否相似
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if len(tool_results) >= 2:
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return _is_similar_result(tool_results[-2:])
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@@ -79,22 +73,43 @@ def _should_stop_for_loop(tool_calls: List[dict], tool_results: List[str]) -> bo
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def create_agent_node(chat_services: dict):
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"""
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创建 Agent 节点 - 支持动态模型切换
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创建 Agent 节点 - 单步推理版本
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简化设计：
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- 直接返回 async 函数，无需工厂包装
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- 从 config 中获取模型名称，运行时动态切换
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设计：
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- 只做一次 LLM 调用
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- 不执行工具（工具执行由 tools 节点负责）
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- 返回 AIMessage（可能包含 tool_calls）
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"""
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async def agent_node(state: AgentState, config: Optional[RunnableConfig] = None) -> Dict[str, Any]:
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"""Agent 节点：完整的 ReAct 循环"""
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"""Agent 节点：单步 LLM 调用"""
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queue = get_stream_queue()
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is_streaming = queue is not None
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# 获取步数
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current_step = getattr(state, "current_step", 0)
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max_steps = getattr(state, "max_steps", 10)
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info(f"[Agent] 从第 {current_step} 步开始，最大步数: {max_steps}，流式: {is_streaming}")
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info(f"[Agent] 第 {current_step + 1} 步开始")
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# 步数已达上限
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if current_step >= max_steps:
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info("[Agent] 达到步数上限，强制结束")
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return {
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"messages": [AIMessage(content="[系统] 已达到最大步数限制。")],
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"stop": True,
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"stop_reason": "max_steps",
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}
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# 循环检测
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tool_history = getattr(state, "tool_call_history", [])
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result_history = getattr(state, "tool_result_history", [])
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if _should_stop_for_loop(tool_history, result_history):
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info("[Agent] 检测到循环，终止推理")
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return {
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"messages": [AIMessage(content="[系统] 检测到工具调用循环，已终止。")],
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"stop": True,
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"stop_reason": "loop_detected",
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}
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# 动态获取模型
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model_name = "primary"
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@@ -111,160 +126,95 @@ def create_agent_node(chat_services: dict):
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# 获取记忆上下文
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memory_context = getattr(state, "memory_context", "暂无用户背景信息")
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# 组装消息（注入记忆上下文到提示词）
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prompt_with_memory = SYSTEM_PROMPT.format(memory_context=memory_context)
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messages = [SystemMessage(content=prompt_with_memory)] + list(state.messages)
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turn = current_step
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# 发送节点开始事件
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if is_streaming:
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await queue.put({"type": "node_start", "node": "agent"})
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# 选择 LLM（最后一轮不带工具）
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if current_step + 1 >= max_steps:
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current_llm = llm.bind_tools([])
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info(f"[Agent] 达到步数上限，使用无工具模型")
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else:
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current_llm = llm_with_tools
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# 初始化
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full_content = ""
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full_reasoning_content = ""
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pending_tool_calls = {}
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final_tool_calls = []
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try:
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while turn < max_steps:
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turn += 1
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info(f"[Agent] 第 {turn} 轮思考")
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# 调用 LLM
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if is_streaming:
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async for chunk in current_llm.astream(messages):
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if isinstance(chunk, AIMessageChunk):
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if chunk.content:
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full_content += chunk.content
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await queue.put({
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"type": "llm_token",
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"node": "agent",
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"token": chunk.content,
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"reasoning_token": ""
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})
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if is_streaming:
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await queue.put({"type": "node_start", "node": "agent"})
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# 选择 LLM（最后一轮不带工具）
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if turn >= max_steps:
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current_llm = llm.bind_tools([])
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info(f"[Agent] 达到步数上限，使用无工具模型")
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else:
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current_llm = llm_with_tools
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# 初始化
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full_content = ""
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full_reasoning_content = ""
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pending_tool_calls = {}
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final_tool_calls = []
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# 循环检测：记录历史调用
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tool_call_history: List[dict] = []
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tool_result_history: List[str] = []
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# 调用 LLM
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if is_streaming:
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async for chunk in current_llm.astream(messages):
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if isinstance(chunk, AIMessageChunk):
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if chunk.content:
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full_content += chunk.content
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if hasattr(chunk, 'additional_kwargs') and chunk.additional_kwargs:
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reasoning = chunk.additional_kwargs.get("reasoning_content", "")
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if reasoning:
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full_reasoning_content += reasoning
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await queue.put({
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"type": "llm_token",
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"node": "agent",
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"token": chunk.content,
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"reasoning_token": ""
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"token": "",
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"reasoning_token": reasoning
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})
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if hasattr(chunk, 'additional_kwargs') and chunk.additional_kwargs:
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reasoning = chunk.additional_kwargs.get("reasoning_content", "")
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if reasoning:
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full_reasoning_content += reasoning
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await queue.put({
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"type": "llm_token",
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"node": "agent",
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"token": "",
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"reasoning_token": reasoning
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})
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if hasattr(chunk, 'tool_call_chunks') and chunk.tool_call_chunks:
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for tc_chunk in chunk.tool_call_chunks:
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idx = tc_chunk.get("index", 0)
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if idx not in pending_tool_calls:
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pending_tool_calls[idx] = {"id": "", "name": "", "args": ""}
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if hasattr(chunk, 'tool_call_chunks') and chunk.tool_call_chunks:
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for tc_chunk in chunk.tool_call_chunks:
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idx = tc_chunk.get("index", 0)
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if idx not in pending_tool_calls:
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pending_tool_calls[idx] = {"id": "", "name": "", "args": ""}
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if tc_chunk.get("id"):
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pending_tool_calls[idx]["id"] += tc_chunk["id"]
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if tc_chunk.get("name"):
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pending_tool_calls[idx]["name"] += tc_chunk["name"]
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if tc_chunk.get("args"):
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args_val = tc_chunk["args"]
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if isinstance(args_val, str):
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pending_tool_calls[idx]["args"] += args_val
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else:
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pending_tool_calls[idx]["args"] += json.dumps(args_val)
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else:
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result = await current_llm.ainvoke(messages)
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full_content = result.content if result.content else ""
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if hasattr(result, 'tool_calls') and result.tool_calls:
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final_tool_calls = result.tool_calls
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if hasattr(result, 'additional_kwargs'):
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full_reasoning_content = result.additional_kwargs.get("reasoning_content", "")
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if tc_chunk.get("id"):
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pending_tool_calls[idx]["id"] += tc_chunk["id"]
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if tc_chunk.get("name"):
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pending_tool_calls[idx]["name"] += tc_chunk["name"]
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if tc_chunk.get("args"):
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args_val = tc_chunk["args"]
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if isinstance(args_val, str):
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pending_tool_calls[idx]["args"] += args_val
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else:
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import json
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pending_tool_calls[idx]["args"] += json.dumps(args_val)
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else:
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result = await current_llm.ainvoke(messages)
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full_content = result.content if result.content else ""
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if hasattr(result, 'tool_calls') and result.tool_calls:
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final_tool_calls = result.tool_calls
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if hasattr(result, 'additional_kwargs'):
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full_reasoning_content = result.additional_kwargs.get("reasoning_content", "")
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# 整理工具调用
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if is_streaming:
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for idx in sorted(pending_tool_calls.keys()):
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tc_data = pending_tool_calls[idx]
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if tc_data["name"]:
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args = {}
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if tc_data["args"]:
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try:
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args = json.loads(tc_data["args"])
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except Exception as e:
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info(f"[Agent] 解析参数失败: {e}")
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final_tool_calls.append({
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"id": tc_data["id"],
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"name": tc_data["name"],
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"args": args
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})
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# 整理工具调用
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if is_streaming:
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for idx in sorted(pending_tool_calls.keys()):
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tc_data = pending_tool_calls[idx]
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if tc_data["name"]:
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args = {}
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if tc_data["args"]:
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try:
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import json
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args = json.loads(tc_data["args"])
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except Exception as e:
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info(f"[Agent] 解析参数失败: {e}")
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final_tool_calls.append({
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"id": tc_data["id"],
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"name": tc_data["name"],
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"args": args
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})
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# 执行工具
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if final_tool_calls:
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info(f"[Agent] 第 {turn} 轮：调用 {len(final_tool_calls)} 个工具")
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new_messages = []
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for tc in final_tool_calls:
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tool_name = tc["name"]
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tool_args = tc["args"]
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tool_id = tc["id"]
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if is_streaming:
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await queue.put({
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"type": "custom",
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"data": {"type": "tool_start", "tool": tool_name, "args": tool_args, "id": tool_id}
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})
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# 查找并执行工具
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tool_result = ""
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tool_found = False
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for tool in ALL_TOOLS:
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if tool.name == tool_name:
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tool_found = True
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try:
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tool_result = await tool.ainvoke(tool_args)
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except Exception as e:
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tool_result = f"工具调用出错: {str(e)}"
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error(f"[Agent] 工具 {tool_name} 调用出错: {e}")
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break
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if not tool_found:
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tool_result = f"未找到工具: {tool_name}"
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if is_streaming:
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await queue.put({
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"type": "custom",
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"data": {"type": "tool_end", "tool": tool_name, "id": tool_id, "result": str(tool_result)}
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})
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# 记录历史（用于循环检测）
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tool_call_history.append({"name": tool_name, "args": tool_args})
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tool_result_history.append(str(tool_result))
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new_messages.append(ToolMessage(content=str(tool_result), tool_call_id=tool_id, name=tool_name))
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# 循环检测：相同工具 + 相似参数 + 相似结果 → 终止
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if _should_stop_for_loop(tool_call_history, tool_result_history):
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info(f"[Agent] ⚠️ 检测到循环，强制终止")
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# 添加一条终止消息
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messages.append(AIMessage(content="[系统] 检测到工具调用循环，已终止。"))
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break
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messages.extend(new_messages)
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continue
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else:
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info(f"[Agent] 第 {turn} 轮：完成，无工具调用")
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break
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# 发送节点结束事件
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if is_streaming:
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await queue.put({"type": "node_end", "node": "agent"})
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# 构建响应
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response_kwargs = {"content": full_content}
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@@ -274,14 +224,15 @@ def create_agent_node(chat_services: dict):
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if full_reasoning_content:
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response.additional_kwargs["reasoning_content"] = full_reasoning_content
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info(f"[Agent] 完成 - content长度: {len(full_content)}, tool_calls: {len(final_tool_calls)}")
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return {
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"messages": [response],
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"current_step": turn,
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"llm_calls": getattr(state, "llm_calls", 0) + 1
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}
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except Exception as e:
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error(f"[Agent] ❌ 第 {turn} 轮出错: {e}")
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error(f"[Agent] 执行出错: {e}")
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import traceback
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error(f"[Agent] 堆栈: {traceback.format_exc()}")
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if is_streaming:
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