class CollaborativeAgent:
    def __init__(self, role, expertise, comm_protocol):
        self.role = role  # 如 '分析师'、'执行者'
        self.expertise = expertise  # 领域知识向量
        self.comm = CommunicationLayer(protocol=comm_protocol)
    
    def route_task(self, task_vector):
        # 计算任务与专长的余弦相似度
        similarity = cosine_similarity(task_vector, self.expertise)
        return similarity > 0.7

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

decompose_template = """
给定任务：{task}
请按步骤拆解，输出JSON格式：
{
  "steps": [
    {"step1": "描述", "agent_type": "类型"},
    ...
  ]
}
"""

decompose_chain = LLMChain(
    llm=ChatGPT4(model="gpt-4-turbo"),
    prompt=PromptTemplate.from_template(decompose_template)
)

# 执行分解
task = "分析公司季度财报并生成可视化报告"
steps = decompose_chain.run(task=task)
print(json.loads(steps))

from langchain.agents import AgentExecutor, Tool
from langchain.memory import ConversationBufferMemory

class SupportAgentSystem:
    def __init__(self):
        self.agents = {
            "product_specialist": self._create_agent("产品专家", "product_db"),
            "price_analyst": self._create_agent("价格分析师", "pricing_db"),
            "policy_advisor": self._create_agent("政策顾问", "policy_db")
        }
        self.router = RouterModel()  # 自定义路由模型

    def _create_agent(self, role, knowledge_base):
        tools = [
            Tool(
                name=f"{role}_knowledge",
                func=lambda q: query_vector_db(q, knowledge_base),
                description=f"{role}领域知识库"
            ),
            Tool(
                name="human_proxy",
                func=self._escalate_to_human,
                description="转接人工服务"
            )
        ]
        return AgentExecutor.from_agent_and_tools(
            agent=ReActAgent(llm=GPT4, tools=tools),
            tools=tools,
            memory=ConversationBufferMemory()
        )

    def handle_request(self, user_query):
        # 步骤1：意图识别
        intent = self.router.detect_intent(user_query)
        
        # 步骤2：智能体分配
        primary_agent = self.agents[intent.primary]
        secondary_agents = [self.agents[a] for a in intent.secondaries]
        
        # 步骤3：协作执行
        result = primary_agent.run(user_query)
        for agent in secondary_agents:
            result = agent.refine(result)  # 结果精炼
        
        # 步骤4：人类对齐校验
        if not self.human_approve(result):
            result = self.agents['human_proxy'].run(user_query)
            
        return result

def compress_message(message: str) -> str:
    # 步骤1：移除重复描述
    compressed = re.sub(r'重复描述', '', message)
    
    # 步骤2：使用缩写词典
    abbreviation_map = {
        "价格分析报告": "价报",
        "产品规格说明书": "产规"
    }
    for full, abbr in abbreviation_map.items():
        compressed = compressed.replace(full, abbr)
        
    # 步骤3：JSON精简
    if is_json(compressed):
        compressed = json.dumps(json.loads(compressed), separators=(',', ':'))
    
    return compressed[:500]  # 截断控制

def detect_intent_drift(original, current):
    # 计算语义相似度
    emb_orig = embed(original)
    emb_curr = embed(current)
    similarity = cosine_similarity(emb_orig, emb_curr)
    
    # 动态阈值策略
    drift_threshold = 0.7 - (0.1 * len(original)/100) 
    
    if similarity < drift_threshold:
        raise IntentDriftError(f"意图偏移度达{1-similarity:.2f}")