[MODIFY] Code optimization
This commit is contained in:
Mark
593
api/app/services/master_agent_router.py
Normal file
593
api/app/services/master_agent_router.py
Normal file
@@ -0,0 +1,593 @@
|
||||
"""Master Agent 路由器 - 让 Master Agent 真正成为决策中心"""
|
||||
import json
|
||||
import re
|
||||
import uuid
|
||||
from typing import Dict, Any, List, Optional, Tuple
|
||||
from sqlalchemy.orm import Session
|
||||
|
||||
from app.services.conversation_state_manager import ConversationStateManager
|
||||
from app.models import ModelConfig, AgentConfig
|
||||
from app.core.logging_config import get_business_logger
|
||||
|
||||
logger = get_business_logger()
|
||||
|
||||
|
||||
class MasterAgentRouter:
|
||||
"""Master Agent 路由器
|
||||
|
||||
让 Master Agent 作为"大脑"，负责：
|
||||
1. 分析用户意图
|
||||
2. 选择最合适的 Sub Agent
|
||||
3. 决定是否需要多 Agent 协作
|
||||
4. 管理会话上下文
|
||||
|
||||
优势：
|
||||
- 更智能的决策（基于完整上下文）
|
||||
- 减少 LLM 调用次数
|
||||
- 架构更清晰（Master Agent 真正起作用）
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
db: Session,
|
||||
master_agent_config: AgentConfig,
|
||||
master_model_config: ModelConfig,
|
||||
sub_agents: Dict[str, Any],
|
||||
state_manager: ConversationStateManager,
|
||||
enable_rule_fast_path: bool = True
|
||||
):
|
||||
"""初始化 Master Agent 路由器
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
master_agent_config: Master Agent 配置
|
||||
master_model_config: Master Agent 使用的模型配置
|
||||
sub_agents: 子 Agent 配置字典
|
||||
state_manager: 会话状态管理器
|
||||
enable_rule_fast_path: 是否启用规则快速路径（性能优化）
|
||||
"""
|
||||
self.db = db
|
||||
self.master_agent_config = master_agent_config
|
||||
self.master_model_config = master_model_config
|
||||
self.sub_agents = sub_agents
|
||||
self.state_manager = state_manager
|
||||
self.enable_rule_fast_path = enable_rule_fast_path
|
||||
|
||||
logger.info(
|
||||
"Master Agent 路由器初始化",
|
||||
extra={
|
||||
"master_agent": master_agent_config.name,
|
||||
"sub_agent_count": len(sub_agents),
|
||||
"enable_rule_fast_path": enable_rule_fast_path
|
||||
}
|
||||
)
|
||||
|
||||
async def route(
|
||||
self,
|
||||
message: str,
|
||||
conversation_id: Optional[str] = None,
|
||||
variables: Optional[Dict[str, Any]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""智能路由决策
|
||||
|
||||
Args:
|
||||
message: 用户消息
|
||||
conversation_id: 会话 ID
|
||||
variables: 变量参数
|
||||
|
||||
Returns:
|
||||
路由决策结果
|
||||
"""
|
||||
logger.info(
|
||||
"开始 Master Agent 路由",
|
||||
extra={
|
||||
"message_length": len(message),
|
||||
"conversation_id": conversation_id
|
||||
}
|
||||
)
|
||||
|
||||
# 1. 获取会话状态
|
||||
state = None
|
||||
if conversation_id:
|
||||
state = self.state_manager.get_state(conversation_id)
|
||||
|
||||
# 2. 尝试规则快速路径（可选的性能优化）
|
||||
if self.enable_rule_fast_path:
|
||||
rule_result = self._try_rule_fast_path(message, state)
|
||||
if rule_result:
|
||||
logger.info(
|
||||
"规则快速路径命中",
|
||||
extra={
|
||||
"agent_id": rule_result["selected_agent_id"],
|
||||
"confidence": rule_result["confidence"]
|
||||
}
|
||||
)
|
||||
|
||||
# 更新会话状态
|
||||
if conversation_id:
|
||||
self.state_manager.update_state(
|
||||
conversation_id,
|
||||
rule_result["selected_agent_id"],
|
||||
message,
|
||||
rule_result.get("topic"),
|
||||
rule_result["confidence"]
|
||||
)
|
||||
|
||||
return rule_result
|
||||
|
||||
# 3. 调用 Master Agent 做决策
|
||||
decision = await self._master_agent_decide(message, state, variables)
|
||||
|
||||
# 4. 更新会话状态
|
||||
if conversation_id:
|
||||
self.state_manager.update_state(
|
||||
conversation_id,
|
||||
decision["selected_agent_id"],
|
||||
message,
|
||||
decision.get("topic"),
|
||||
decision["confidence"]
|
||||
)
|
||||
|
||||
logger.info(
|
||||
"Master Agent 路由完成",
|
||||
extra={
|
||||
"agent_id": decision["selected_agent_id"],
|
||||
"strategy": decision["strategy"],
|
||||
"confidence": decision["confidence"]
|
||||
}
|
||||
)
|
||||
|
||||
return decision
|
||||
|
||||
def _try_rule_fast_path(
|
||||
self,
|
||||
message: str,
|
||||
state: Optional[Dict[str, Any]]
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""尝试规则快速路径（性能优化）
|
||||
|
||||
对于明确的关键词匹配，直接返回结果，不调用 Master Agent
|
||||
|
||||
Args:
|
||||
message: 用户消息
|
||||
state: 会话状态
|
||||
|
||||
Returns:
|
||||
如果命中规则返回决策结果，否则返回 None
|
||||
"""
|
||||
# 定义高置信度关键词规则
|
||||
high_confidence_rules = [
|
||||
{
|
||||
"keywords": ["数学", "方程", "计算", "求解"],
|
||||
"agent_role": "数学",
|
||||
"confidence_threshold": 0.9
|
||||
},
|
||||
{
|
||||
"keywords": ["物理", "力学", "电路", "光学"],
|
||||
"agent_role": "物理",
|
||||
"confidence_threshold": 0.9
|
||||
},
|
||||
{
|
||||
"keywords": ["订单", "发货", "物流", "快递"],
|
||||
"agent_role": "订单",
|
||||
"confidence_threshold": 0.9
|
||||
},
|
||||
{
|
||||
"keywords": ["退款", "退货", "售后"],
|
||||
"agent_role": "退款",
|
||||
"confidence_threshold": 0.9
|
||||
}
|
||||
]
|
||||
|
||||
message_lower = message.lower()
|
||||
|
||||
for rule in high_confidence_rules:
|
||||
matched_keywords = [kw for kw in rule["keywords"] if kw in message_lower]
|
||||
|
||||
if matched_keywords:
|
||||
confidence = len(matched_keywords) / len(rule["keywords"])
|
||||
|
||||
if confidence >= rule["confidence_threshold"]:
|
||||
# 查找对应的 agent
|
||||
for agent_id, agent_data in self.sub_agents.items():
|
||||
agent_info = agent_data.get("info", {})
|
||||
if agent_info.get("role") == rule["agent_role"]:
|
||||
return {
|
||||
"selected_agent_id": agent_id,
|
||||
"confidence": confidence,
|
||||
"strategy": "rule_fast_path",
|
||||
"reasoning": f"关键词匹配: {', '.join(matched_keywords)}",
|
||||
"topic": rule["agent_role"],
|
||||
"need_collaboration": False,
|
||||
"routing_method": "rule"
|
||||
}
|
||||
|
||||
return None
|
||||
|
||||
async def _master_agent_decide(
|
||||
self,
|
||||
message: str,
|
||||
state: Optional[Dict[str, Any]],
|
||||
variables: Optional[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""让 Master Agent 做路由决策
|
||||
|
||||
Args:
|
||||
message: 用户消息
|
||||
state: 会话状态
|
||||
variables: 变量参数
|
||||
|
||||
Returns:
|
||||
决策结果
|
||||
"""
|
||||
# 1. 构建决策 prompt
|
||||
prompt = self._build_decision_prompt(message, state, variables)
|
||||
|
||||
# 2. 调用 Master Agent 的 LLM
|
||||
try:
|
||||
response = await self._call_master_agent_llm(prompt)
|
||||
|
||||
# 3. 解析决策
|
||||
decision = self._parse_decision(response)
|
||||
|
||||
# 4. 验证决策
|
||||
decision = self._validate_decision(decision)
|
||||
|
||||
return decision
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Master Agent 决策失败: {str(e)}")
|
||||
# 降级到默认 agent
|
||||
return self._get_fallback_decision(message)
|
||||
|
||||
def _build_decision_prompt(
|
||||
self,
|
||||
message: str,
|
||||
state: Optional[Dict[str, Any]],
|
||||
variables: Optional[Dict[str, Any]]
|
||||
) -> str:
|
||||
"""构建 Master Agent 的决策 prompt
|
||||
|
||||
Args:
|
||||
message: 用户消息
|
||||
state: 会话状态
|
||||
variables: 变量参数
|
||||
|
||||
Returns:
|
||||
prompt 字符串
|
||||
"""
|
||||
# 1. 构建 Sub Agent 描述（简化版，提升性能）
|
||||
agent_descriptions = []
|
||||
for agent_id, agent_data in self.sub_agents.items():
|
||||
agent_info = agent_data.get("info", {})
|
||||
|
||||
name = agent_info.get("name", "未命名")
|
||||
role = agent_info.get("role", "")
|
||||
capabilities = agent_info.get("capabilities", [])
|
||||
|
||||
# 简化格式：一行描述
|
||||
desc = f"- {agent_id}: {name}"
|
||||
if role:
|
||||
desc += f" ({role})"
|
||||
if capabilities:
|
||||
desc += f" - {', '.join(capabilities[:3])}" # 只取前3个能力
|
||||
|
||||
agent_descriptions.append(desc)
|
||||
|
||||
agents_text = "\n".join(agent_descriptions)
|
||||
|
||||
# 2. 构建会话上下文
|
||||
context_text = ""
|
||||
if state:
|
||||
current_agent = state.get("current_agent_id")
|
||||
last_topic = state.get("last_topic")
|
||||
same_turns = state.get("same_agent_turns", 0)
|
||||
|
||||
if current_agent:
|
||||
context_text = f"""
|
||||
当前会话上下文：
|
||||
- 当前使用的 Agent: {current_agent}
|
||||
- 上一个主题: {last_topic}
|
||||
- 连续使用轮数: {same_turns}
|
||||
"""
|
||||
|
||||
# 获取第一个可用的 agent_id 作为示例
|
||||
example_agent_id = next(iter(self.sub_agents.keys())) if self.sub_agents else "agent_id"
|
||||
|
||||
# 3. 构建完整 prompt（简化版，提升性能）
|
||||
prompt = f"""路由任务：分析问题并选择合适的 Agent。
|
||||
|
||||
可用 Agent：
|
||||
{agents_text}
|
||||
{context_text}
|
||||
问题："{message}"
|
||||
|
||||
返回 JSON 格式决策：
|
||||
|
||||
**情况1：单一问题（最常见）**
|
||||
{{"selected_agent_id": "{example_agent_id}", "confidence": 0.9, "need_collaboration": false, "reasoning": "选择理由"}}
|
||||
|
||||
**情况2：需要拆分成多个独立子问题**
|
||||
当用户问题包含多个完全独立的子问题时使用（如"写诗+做数学题"）。
|
||||
必须提供 sub_questions 数组，每个子问题必须指定 agent_id：
|
||||
{{"selected_agent_id": "{example_agent_id}", "confidence": 0.9, "need_collaboration": true, "need_decomposition": true,
|
||||
"sub_questions": [
|
||||
{{"question": "具体的子问题1", "agent_id": "{example_agent_id}", "order": 1, "depends_on": []}},
|
||||
{{"question": "具体的子问题2", "agent_id": "{example_agent_id}", "order": 2, "depends_on": []}}
|
||||
],
|
||||
"collaboration_strategy": "decomposition", "reasoning": "问题包含X个独立子问题"}}
|
||||
|
||||
**情况3：需要多个Agent协作分析同一问题**
|
||||
{{"selected_agent_id": "{example_agent_id}", "confidence": 0.9, "need_collaboration": true,
|
||||
"collaboration_agents": [{{"agent_id": "{example_agent_id}", "role": "primary", "task": "主要任务", "order": 1}}],
|
||||
"collaboration_strategy": "sequential", "reasoning": "需要多角度分析"}}
|
||||
|
||||
重要规则：
|
||||
1. selected_agent_id 必须从上面的可用 Agent 列表中选择
|
||||
2. 如果选择情况2（拆分），sub_questions 数组不能为空，必须包含具体的子问题
|
||||
3. 每个子问题的 agent_id 也必须从可用列表中选择
|
||||
4. depends_on 表示依赖关系（如 [1] 表示依赖第1个子问题的结果）
|
||||
5. 大多数情况应该选择情况1（单一Agent），只有明确需要时才拆分或协作
|
||||
6. 只做路由决策，不要回答问题内容
|
||||
|
||||
请返回 JSON："""
|
||||
|
||||
return prompt
|
||||
|
||||
async def _call_master_agent_llm(self, prompt: str) -> str:
|
||||
"""调用 Master Agent 的 LLM
|
||||
|
||||
Args:
|
||||
prompt: 提示词
|
||||
|
||||
Returns:
|
||||
LLM 响应
|
||||
"""
|
||||
try:
|
||||
from app.core.models import RedBearLLM
|
||||
from app.core.models.base import RedBearModelConfig
|
||||
from app.models import ModelApiKey, ModelType
|
||||
|
||||
# 获取 API Key 配置
|
||||
api_key_config = self.db.query(ModelApiKey).filter(
|
||||
ModelApiKey.model_config_id == self.master_model_config.id,
|
||||
ModelApiKey.is_active == True
|
||||
).first()
|
||||
|
||||
if not api_key_config:
|
||||
raise Exception("Master Agent 模型没有可用的 API Key")
|
||||
|
||||
logger.info(
|
||||
"调用 Master Agent LLM",
|
||||
extra={
|
||||
"provider": api_key_config.provider,
|
||||
"model_name": api_key_config.model_name
|
||||
}
|
||||
)
|
||||
|
||||
# 创建 RedBearModelConfig
|
||||
model_config = RedBearModelConfig(
|
||||
model_name=api_key_config.model_name,
|
||||
provider=api_key_config.provider,
|
||||
api_key=api_key_config.api_key,
|
||||
base_url=api_key_config.api_base,
|
||||
temperature=0.3, # 决策任务使用较低温度
|
||||
max_tokens=1000
|
||||
)
|
||||
|
||||
# 创建 LLM 实例
|
||||
llm = RedBearLLM(model_config, type=ModelType.CHAT)
|
||||
|
||||
# 调用模型
|
||||
response = await llm.ainvoke(prompt)
|
||||
|
||||
# 提取响应内容
|
||||
if hasattr(response, 'content'):
|
||||
return response.content
|
||||
else:
|
||||
return str(response)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Master Agent LLM 调用失败: {str(e)}")
|
||||
raise
|
||||
|
||||
def _parse_decision(self, response: str) -> Dict[str, Any]:
|
||||
"""解析 Master Agent 的决策
|
||||
|
||||
Args:
|
||||
response: LLM 响应
|
||||
|
||||
Returns:
|
||||
决策字典
|
||||
"""
|
||||
try:
|
||||
# 提取 JSON
|
||||
json_match = re.search(r'\{[^{}]*(?:\{[^{}]*\}[^{}]*)*\}', response, re.DOTALL)
|
||||
if json_match:
|
||||
decision = json.loads(json_match.group())
|
||||
|
||||
# 添加默认值
|
||||
decision.setdefault("confidence", 0.8)
|
||||
decision.setdefault("strategy", "master_agent")
|
||||
decision.setdefault("routing_method", "master_agent")
|
||||
decision.setdefault("need_collaboration", False)
|
||||
decision.setdefault("collaboration_agents", [])
|
||||
|
||||
return decision
|
||||
else:
|
||||
raise ValueError("无法从响应中提取 JSON")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"解析 Master Agent 决策失败: {str(e)}")
|
||||
logger.debug(f"原始响应: {response}")
|
||||
raise
|
||||
|
||||
def _validate_decision(self, decision: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""验证决策的有效性
|
||||
|
||||
Args:
|
||||
decision: 决策字典
|
||||
|
||||
Returns:
|
||||
验证后的决策
|
||||
"""
|
||||
# 验证 agent_id
|
||||
selected_agent_id = decision.get("selected_agent_id")
|
||||
if selected_agent_id not in self.sub_agents:
|
||||
logger.warning(f"Master Agent 返回的 agent_id 无效: {selected_agent_id}")
|
||||
# 使用默认 agent
|
||||
decision["selected_agent_id"] = self._get_default_agent_id()
|
||||
decision["confidence"] = 0.5
|
||||
decision["reasoning"] = "原始选择无效，使用默认 Agent"
|
||||
|
||||
# 验证 confidence
|
||||
confidence = decision.get("confidence", 0.8)
|
||||
if not isinstance(confidence, (int, float)) or confidence < 0 or confidence > 1:
|
||||
decision["confidence"] = 0.8
|
||||
|
||||
# 验证协作 agents
|
||||
if decision.get("need_collaboration"):
|
||||
# 检查是否是问题拆分模式
|
||||
if decision.get("need_decomposition") or decision.get("sub_questions"):
|
||||
# 问题拆分模式
|
||||
sub_questions = decision.get("sub_questions", [])
|
||||
|
||||
# 验证每个子问题
|
||||
valid_sub_questions = []
|
||||
for sub_q in sub_questions:
|
||||
if isinstance(sub_q, dict):
|
||||
agent_id = sub_q.get("agent_id")
|
||||
question = sub_q.get("question")
|
||||
|
||||
if agent_id in self.sub_agents and question:
|
||||
# 确保有必要的字段
|
||||
sub_q.setdefault("order", len(valid_sub_questions) + 1)
|
||||
sub_q.setdefault("depends_on", [])
|
||||
valid_sub_questions.append(sub_q)
|
||||
else:
|
||||
# 记录验证失败的原因
|
||||
logger.warning(
|
||||
"子问题验证失败",
|
||||
extra={
|
||||
"agent_id": agent_id,
|
||||
"has_question": bool(question),
|
||||
"agent_exists": agent_id in self.sub_agents if agent_id else False,
|
||||
"available_agents": list(self.sub_agents.keys())
|
||||
}
|
||||
)
|
||||
|
||||
decision["sub_questions"] = valid_sub_questions
|
||||
|
||||
# 如果所有子问题都验证失败，降级处理
|
||||
if not valid_sub_questions and sub_questions:
|
||||
logger.warning(
|
||||
"所有子问题验证失败，降级到单 Agent 模式",
|
||||
extra={
|
||||
"original_sub_question_count": len(sub_questions),
|
||||
"available_agents": list(self.sub_agents.keys())
|
||||
}
|
||||
)
|
||||
# 降级：取消协作标记，使用默认 Agent
|
||||
decision["need_collaboration"] = False
|
||||
decision["need_decomposition"] = False
|
||||
decision["collaboration_strategy"] = None
|
||||
# 选择第一个可用的 Agent
|
||||
if self.sub_agents:
|
||||
first_agent_id = next(iter(self.sub_agents.keys()))
|
||||
decision["selected_agent_id"] = first_agent_id
|
||||
logger.info(f"降级使用默认 Agent: {first_agent_id}")
|
||||
|
||||
# 设置协作策略为 decomposition
|
||||
decision["collaboration_strategy"] = "decomposition"
|
||||
|
||||
logger.info(
|
||||
"问题拆分决策验证完成",
|
||||
extra={
|
||||
"sub_question_count": len(valid_sub_questions),
|
||||
"strategy": "decomposition"
|
||||
}
|
||||
)
|
||||
else:
|
||||
# 普通协作模式
|
||||
collaboration_agents = decision.get("collaboration_agents", [])
|
||||
|
||||
# 如果是简单列表格式，转换为详细格式
|
||||
if collaboration_agents and isinstance(collaboration_agents[0], str):
|
||||
collaboration_agents = [
|
||||
{
|
||||
"agent_id": agent_id,
|
||||
"role": "primary" if i == 0 else "secondary",
|
||||
"task": "协作处理",
|
||||
"order": i + 1
|
||||
}
|
||||
for i, agent_id in enumerate(collaboration_agents)
|
||||
]
|
||||
|
||||
# 验证每个协作 agent
|
||||
valid_agents = []
|
||||
for agent_info in collaboration_agents:
|
||||
if isinstance(agent_info, dict):
|
||||
agent_id = agent_info.get("agent_id")
|
||||
if agent_id in self.sub_agents:
|
||||
# 确保有必要的字段
|
||||
agent_info.setdefault("role", "secondary")
|
||||
agent_info.setdefault("task", "协作处理")
|
||||
agent_info.setdefault("order", len(valid_agents) + 1)
|
||||
valid_agents.append(agent_info)
|
||||
elif isinstance(agent_info, str) and agent_info in self.sub_agents:
|
||||
valid_agents.append({
|
||||
"agent_id": agent_info,
|
||||
"role": "secondary",
|
||||
"task": "协作处理",
|
||||
"order": len(valid_agents) + 1
|
||||
})
|
||||
|
||||
decision["collaboration_agents"] = valid_agents
|
||||
|
||||
# 设置默认协作策略
|
||||
if not decision.get("collaboration_strategy"):
|
||||
decision["collaboration_strategy"] = "sequential"
|
||||
|
||||
logger.info(
|
||||
"协作决策验证完成",
|
||||
extra={
|
||||
"collaboration_agent_count": len(valid_agents),
|
||||
"strategy": decision.get("collaboration_strategy")
|
||||
}
|
||||
)
|
||||
|
||||
return decision
|
||||
|
||||
def _get_fallback_decision(self, message: str) -> Dict[str, Any]:
|
||||
"""获取降级决策（当 Master Agent 失败时）
|
||||
|
||||
Args:
|
||||
message: 用户消息
|
||||
|
||||
Returns:
|
||||
降级决策
|
||||
"""
|
||||
default_agent_id = self._get_default_agent_id()
|
||||
|
||||
return {
|
||||
"selected_agent_id": default_agent_id,
|
||||
"confidence": 0.5,
|
||||
"strategy": "fallback",
|
||||
"reasoning": "Master Agent 决策失败，使用默认 Agent",
|
||||
"topic": "未知",
|
||||
"need_collaboration": False,
|
||||
"collaboration_agents": [],
|
||||
"routing_method": "fallback"
|
||||
}
|
||||
|
||||
def _get_default_agent_id(self) -> str:
|
||||
"""获取默认 Agent ID
|
||||
|
||||
Returns:
|
||||
默认 Agent ID
|
||||
"""
|
||||
if self.sub_agents:
|
||||
# 返回第一个 agent
|
||||
return next(iter(self.sub_agents.keys()))
|
||||
|
||||
return "default-agent"
|
||||
Reference in New Issue
Block a user