root
6dfa9f572e
All checks were successful
构建并部署 AI Agent 服务 / deploy (push) Successful in 5m24s
主要变更： - 删除 deprecated 文件夹（intent/hybrid_router/rag_nodes 等） - 删除 intent_classifier.py（未使用） - 删除 subgraph_wrapper.py（死代码） - 重构 agent.py：简化工厂函数，支持动态模型切换 - 重构 prompts.py：添加信息获取优先级、思维链要求、工具调用约束 - 优化 tools：统一位置，rag_search 返回置信度评估 - 新增 RAG 置信度评估：embedding(25%) + rerank(25%) + LLM(50%) - 添加循环检测：防止工具无限重复调用 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
292 lines
12 KiB
Python
292 lines
12 KiB
Python
"""
|
||
Agent 节点 - 简化版本
|
||
直接定义 agent_node 函数，支持动态模型切换和循环检测
|
||
"""
|
||
|
||
import hashlib
|
||
from typing import Dict, Any, Optional, List
|
||
from langchain_core.runnables.config import RunnableConfig
|
||
from langchain_core.messages import AIMessage, AIMessageChunk, SystemMessage, ToolMessage
|
||
|
||
from backend.app.main_graph.state import AgentState
|
||
from backend.app.logger import info, error
|
||
from backend.app.tools import ALL_TOOLS
|
||
from backend.app.agent.stream_context import get_stream_queue
|
||
from backend.app.agent.prompts import SYSTEM_PROMPT
|
||
|
||
|
||
def _normalize_args(args: dict) -> str:
|
||
"""标准化工具参数用于比较"""
|
||
return str(sorted(args.items()))
|
||
|
||
|
||
def _is_similar_result(results: List[str], threshold: float = 0.8) -> bool:
|
||
"""检测结果是否相似（简单实现：长度相似+部分内容重复）"""
|
||
if len(results) < 2:
|
||
return False
|
||
|
||
latest = results[-1]
|
||
prev = results[-2]
|
||
|
||
# 长度差异太大，不算相似
|
||
if len(latest) == 0 or len(prev) == 0:
|
||
return len(latest) == len(prev)
|
||
|
||
len_ratio = min(len(latest), len(prev)) / max(len(latest), len(prev))
|
||
if len_ratio < 0.5:
|
||
return False
|
||
|
||
# 检查内容重复度（简单：前100字符）
|
||
common_len = 0
|
||
for a, b in zip(latest[:100], prev[:100]):
|
||
if a == b:
|
||
common_len += 1
|
||
else:
|
||
break
|
||
|
||
return (common_len / 100) > threshold
|
||
|
||
|
||
def _should_stop_for_loop(tool_calls: List[dict], tool_results: List[str]) -> bool:
|
||
"""
|
||
检测是否应该停止（循环检测）
|
||
|
||
条件：连续2次调用相同工具 + 参数相似 + 结果相似
|
||
"""
|
||
if len(tool_calls) < 2:
|
||
return False
|
||
|
||
# 检查最近的工具调用是否相同
|
||
last_tc = tool_calls[-1]
|
||
prev_tc = tool_calls[-2]
|
||
|
||
if last_tc["name"] != prev_tc["name"]:
|
||
return False
|
||
|
||
# 参数是否相似
|
||
last_args = _normalize_args(last_tc["args"])
|
||
prev_args = _normalize_args(prev_tc["args"])
|
||
|
||
if last_args != prev_args:
|
||
return False
|
||
|
||
# 结果是否相似
|
||
if len(tool_results) >= 2:
|
||
return _is_similar_result(tool_results[-2:])
|
||
|
||
return False
|
||
|
||
|
||
def create_agent_node(chat_services: dict):
|
||
"""
|
||
创建 Agent 节点 - 支持动态模型切换
|
||
|
||
简化设计：
|
||
- 直接返回 async 函数，无需工厂包装
|
||
- 从 config 中获取模型名称，运行时动态切换
|
||
"""
|
||
|
||
async def agent_node(state: AgentState, config: Optional[RunnableConfig] = None) -> Dict[str, Any]:
|
||
"""Agent 节点：完整的 ReAct 循环"""
|
||
queue = get_stream_queue()
|
||
is_streaming = queue is not None
|
||
|
||
# 获取步数
|
||
current_step = getattr(state, "current_step", 0)
|
||
max_steps = getattr(state, "max_steps", 10)
|
||
info(f"[Agent] 从第 {current_step} 步开始，最大步数: {max_steps}，流式: {is_streaming}")
|
||
|
||
# 动态获取模型
|
||
model_name = "primary"
|
||
if config:
|
||
configurable = config.get("configurable", {})
|
||
model_name = configurable.get("model", "primary")
|
||
|
||
llm = chat_services.get(model_name)
|
||
if llm is None:
|
||
llm = next(iter(chat_services.values()))
|
||
info(f"[Agent] 模型 '{model_name}' 不可用，使用 '{type(llm).__name__}'")
|
||
|
||
llm_with_tools = llm.bind_tools(ALL_TOOLS)
|
||
|
||
# 获取记忆上下文
|
||
memory_context = getattr(state, "memory_context", "暂无用户背景信息")
|
||
|
||
# 组装消息（注入记忆上下文到提示词）
|
||
prompt_with_memory = SYSTEM_PROMPT.format(memory_context=memory_context)
|
||
messages = [SystemMessage(content=prompt_with_memory)] + list(state.messages)
|
||
turn = current_step
|
||
|
||
try:
|
||
while turn < max_steps:
|
||
turn += 1
|
||
info(f"[Agent] 第 {turn} 轮思考")
|
||
|
||
if is_streaming:
|
||
await queue.put({"type": "node_start", "node": "agent"})
|
||
|
||
# 选择 LLM（最后一轮不带工具）
|
||
if turn >= max_steps:
|
||
current_llm = llm.bind_tools([])
|
||
info(f"[Agent] 达到步数上限，使用无工具模型")
|
||
else:
|
||
current_llm = llm_with_tools
|
||
|
||
# 初始化
|
||
full_content = ""
|
||
full_reasoning_content = ""
|
||
pending_tool_calls = {}
|
||
final_tool_calls = []
|
||
|
||
# 循环检测：记录历史调用
|
||
tool_call_history: List[dict] = []
|
||
tool_result_history: List[str] = []
|
||
|
||
# 调用 LLM
|
||
if is_streaming:
|
||
async for chunk in current_llm.astream(messages):
|
||
if isinstance(chunk, AIMessageChunk):
|
||
if chunk.content:
|
||
full_content += chunk.content
|
||
await queue.put({
|
||
"type": "llm_token",
|
||
"node": "agent",
|
||
"token": chunk.content,
|
||
"reasoning_token": ""
|
||
})
|
||
|
||
if hasattr(chunk, 'additional_kwargs') and chunk.additional_kwargs:
|
||
reasoning = chunk.additional_kwargs.get("reasoning_content", "")
|
||
if reasoning:
|
||
full_reasoning_content += reasoning
|
||
await queue.put({
|
||
"type": "llm_token",
|
||
"node": "agent",
|
||
"token": "",
|
||
"reasoning_token": reasoning
|
||
})
|
||
|
||
if hasattr(chunk, 'tool_call_chunks') and chunk.tool_call_chunks:
|
||
for tc_chunk in chunk.tool_call_chunks:
|
||
idx = tc_chunk.get("index", 0)
|
||
if idx not in pending_tool_calls:
|
||
pending_tool_calls[idx] = {"id": "", "name": "", "args": ""}
|
||
|
||
if tc_chunk.get("id"):
|
||
pending_tool_calls[idx]["id"] += tc_chunk["id"]
|
||
if tc_chunk.get("name"):
|
||
pending_tool_calls[idx]["name"] += tc_chunk["name"]
|
||
if tc_chunk.get("args"):
|
||
args_val = tc_chunk["args"]
|
||
if isinstance(args_val, str):
|
||
pending_tool_calls[idx]["args"] += args_val
|
||
else:
|
||
import json
|
||
pending_tool_calls[idx]["args"] += json.dumps(args_val)
|
||
else:
|
||
result = await current_llm.ainvoke(messages)
|
||
full_content = result.content if result.content else ""
|
||
if hasattr(result, 'tool_calls') and result.tool_calls:
|
||
final_tool_calls = result.tool_calls
|
||
if hasattr(result, 'additional_kwargs'):
|
||
full_reasoning_content = result.additional_kwargs.get("reasoning_content", "")
|
||
|
||
# 整理工具调用
|
||
if is_streaming:
|
||
for idx in sorted(pending_tool_calls.keys()):
|
||
tc_data = pending_tool_calls[idx]
|
||
if tc_data["name"]:
|
||
args = {}
|
||
if tc_data["args"]:
|
||
try:
|
||
import json
|
||
args = json.loads(tc_data["args"])
|
||
except Exception as e:
|
||
info(f"[Agent] 解析参数失败: {e}")
|
||
final_tool_calls.append({
|
||
"id": tc_data["id"],
|
||
"name": tc_data["name"],
|
||
"args": args
|
||
})
|
||
|
||
# 执行工具
|
||
if final_tool_calls:
|
||
info(f"[Agent] 第 {turn} 轮：调用 {len(final_tool_calls)} 个工具")
|
||
new_messages = []
|
||
|
||
for tc in final_tool_calls:
|
||
tool_name = tc["name"]
|
||
tool_args = tc["args"]
|
||
tool_id = tc["id"]
|
||
|
||
if is_streaming:
|
||
await queue.put({
|
||
"type": "custom",
|
||
"data": {"type": "tool_start", "tool": tool_name, "args": tool_args, "id": tool_id}
|
||
})
|
||
|
||
# 查找并执行工具
|
||
tool_result = ""
|
||
tool_found = False
|
||
for tool in ALL_TOOLS:
|
||
if tool.name == tool_name:
|
||
tool_found = True
|
||
try:
|
||
tool_result = await tool.ainvoke(tool_args)
|
||
except Exception as e:
|
||
tool_result = f"工具调用出错: {str(e)}"
|
||
error(f"[Agent] 工具 {tool_name} 调用出错: {e}")
|
||
break
|
||
|
||
if not tool_found:
|
||
tool_result = f"未找到工具: {tool_name}"
|
||
|
||
if is_streaming:
|
||
await queue.put({
|
||
"type": "custom",
|
||
"data": {"type": "tool_end", "tool": tool_name, "id": tool_id, "result": str(tool_result)}
|
||
})
|
||
|
||
# 记录历史（用于循环检测）
|
||
tool_call_history.append({"name": tool_name, "args": tool_args})
|
||
tool_result_history.append(str(tool_result))
|
||
|
||
new_messages.append(ToolMessage(content=str(tool_result), tool_call_id=tool_id, name=tool_name))
|
||
|
||
# 循环检测：相同工具 + 相似参数 + 相似结果 → 终止
|
||
if _should_stop_for_loop(tool_call_history, tool_result_history):
|
||
info(f"[Agent] ⚠️ 检测到循环，强制终止")
|
||
# 添加一条终止消息
|
||
messages.append(AIMessage(content="[系统] 检测到工具调用循环，已终止。"))
|
||
break
|
||
|
||
messages.extend(new_messages)
|
||
continue
|
||
else:
|
||
info(f"[Agent] 第 {turn} 轮：完成，无工具调用")
|
||
break
|
||
|
||
# 构建响应
|
||
response_kwargs = {"content": full_content}
|
||
if final_tool_calls:
|
||
response_kwargs["tool_calls"] = final_tool_calls
|
||
response = AIMessage(**response_kwargs)
|
||
if full_reasoning_content:
|
||
response.additional_kwargs["reasoning_content"] = full_reasoning_content
|
||
|
||
return {
|
||
"messages": [response],
|
||
"current_step": turn,
|
||
"llm_calls": getattr(state, "llm_calls", 0) + 1
|
||
}
|
||
|
||
except Exception as e:
|
||
error(f"[Agent] ❌ 第 {turn} 轮出错: {e}")
|
||
import traceback
|
||
error(f"[Agent] 堆栈: {traceback.format_exc()}")
|
||
if is_streaming:
|
||
await queue.put({"type": "error", "message": str(e)})
|
||
raise
|
||
|
||
return agent_node
|