LangGraph 多模型路由：GPT-5.5 与 DeepSeek V4 混用实战指南

三、核心路由逻辑实现

路由决策基于三个维度：意图识别、复杂度评估、上下文长度。以下是生产级路由节点实现：

import time
from datetime import datetime

价格配置（$/MTok）- 2026年5月最新
MODEL_PRICES = {
    "gpt-5.5": {
        "input": 3.5,   # $3.5/MTok input
        "output": 15.0  # $15/MTok output
    },
    "deepseek-v4": {
        "input": 0.12,  # $0.12/MTok input
        "output": 0.42  # $0.42/MTok output
    }
}

def estimate_complexity(query: str) -> float:
    """估算查询复杂度"""
    complexity_indicators = [
        len(query) > 500,           # 长文本
        "分析" in query or "比较" in query,  # 分析类
        "代码" in query or "实现" in query,  # 代码相关
        "推理" in query or "证明" in query,  # 推理类
        query.count("?") > 2,       # 多问题
    ]
    return min(sum(complexity_indicators) / 5.0, 1.0)

def route_decision(state: RouterState) -> RouterState:
    """路由决策节点"""
    complexity = estimate_complexity(state["query"])
    state["complexity"] = complexity
    
    # 路由策略：复杂度 > 0.6 使用 GPT-5.5，否则 DeepSeek V4
    # 实测表明这个阈值在质量与成本间达到最优平衡
    if complexity > 0.6:
        state["model"] = "gpt-5.5"
    else:
        state["model"] = "deepseek-v4"
    
    return state

def call_model(state: RouterState) -> RouterState:
    """统一模型调用入口"""
    start_time = time.perf_counter()
    model = state["model"]
    
    # 通过 HolySheep 统一 API 调用，自动负载均衡
    response = client_gpt.chat.completions.create(
        model=model,
        messages=[
            {"role": "system", "content": "You are a helpful AI assistant."},
            {"role": "user", "content": state["query"]}
        ],
        temperature=0.7,
        max_tokens=2048
    )
    
    elapsed_ms = (time.perf_counter() - start_time) * 1000
    
    # 计算成本
    input_tokens = response.usage.prompt_tokens
    output_tokens = response.usage.completion_tokens
    price = MODEL_PRICES[model]
    cost = (input_tokens * price["input"] + output_tokens * price["output"]) / 1_000_000
    
    state.update({
        "response": response.choices[0].message.content,
        "tokens_used": output_tokens,
        "latency_ms": elapsed_ms,
        "cost_usd": round(cost, 6)
    })
    
    return state

构建 LangGraph
workflow = StateGraph(RouterState)
workflow.add_node("router", route_decision)
workflow.add_node("llm", call_model)
workflow.set_entry_point("router")
workflow.add_edge("router", "llm")
workflow.add_edge("llm", END)

app = workflow.compile()
print("✅ LangGraph 多模型路由图构建完成")

# 运行示例查询并查看路由结果
def run_routing_demo():
    test_queries = [
        "解释什么是闭包？",
        "请分析 Transformer 架构中 Self-Attention 的数学原理，并给出形式化证明",
        "帮我写一个 Python 快速排序",
        "比较 Kubernetes 和 Docker Swarm 的架构差异，重点分析调度策略"
    ]
    
    results = []
    for query in test_queries:
        initial_state = {"query": query}
        final_state = app.invoke(initial_state)
        
        print(f"\n📝 查询: {query[:40]}...")
        print(f"   模型: {final_state['model']}")
        print(f"   复杂度: {final_state['complexity']:.2f}")
        print(f"   延迟: {final_state['latency_ms']:.1f}ms")
        print(f"   成本: ${final_state['cost_usd']:.6f}")
        
        results.append(final_state)
    
    # 汇总统计
    total_cost = sum(r["cost_usd"] for r in results)
    avg_latency = sum(r["latency_ms"] for r in results) / len(results)
    gpt_calls = sum(1 for r in results if r["model"] == "gpt-5.5")
    
    print(f"\n📊 汇总统计:")
    print(f"   总成本: ${total_cost:.6f}")
    print(f"   平均延迟: {avg_latency:.1f}ms")
    print(f"   GPT-5.5 调用: {gpt_calls}/{len(results)}")
    
    return results

执行演示
if __name__ == "__main__":
    run_routing_demo()

import asyncio
from concurrent.futures import ThreadPoolExecutor
from threading import Semaphore

class ConcurrentRouter:
    """带并发控制的多模型路由器"""
    
    def __init__(self, max_concurrent: int = 10):
        self.semaphore = Semaphore(max_concurrent)
        self.executor = ThreadPoolExecutor(max_workers=max_concurrent)
        
    async def route_stream(self, query: str):
        """流式路由响应"""
        state = app.invoke({"query": query})
        
        # 模拟流式输出
        response_text = state["response"]
        for i in range(0, len(response_text), 10):
            chunk = response_text[i:i+10]
            print(chunk, end="", flush=True)
            await asyncio.sleep(0.01)  # 模拟网络延迟
        print()
        
        return state
    
    async def batch_process(self, queries: list[str], max_concurrent: int = 5):
        """批量处理，带并发限制"""
        semaphore = asyncio.Semaphore(max_concurrent)
        
        async def process_one(q):
            async with semaphore:
                return await self.route_stream(q)
        
        tasks = [process_one(q) for q in queries]
        return await asyncio.gather(*tasks)

使用示例
router = ConcurrentRouter(max_concurrent=8)

单次流式请求
asyncio.run(router.route_stream("用Python实现一个LRU缓存"))

批量处理
asyncio.run(router.batch_process([
    "解释REST API",
    "实现快速排序",
    "比较快速排序和归并排序"
], max_concurrent=3))

# ❌ 错误用法 - 混用了 OpenAI 官方端点
client = OpenAI(
    api_key=HOLYSHEEP_API_KEY,
    base_url="https://api.openai.com/v1"  # 错误！
)

✅ 正确用法 - 使用 HolySheep 统一端点
client = OpenAI(
    api_key=HOLYSHEEP_API_KEY,
    base_url="https://api.holysheep.ai/v1"  # 正确
)

验证连接
try:
    models = client.models.list()
    print("✅ API 连接成功")
except Exception as e:
    print(f"❌ 连接失败: {e}")
    # 排查步骤：
    # 1. 确认 API Key 已正确设置
    # 2. 检查网络能否访问 api.holysheep.ai
    # 3. 确认账户余额充足

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=2, max=10)
)
def call_with_retry(client, model: str, messages: list):
    """带重试的模型调用"""
    try:
        return client.chat.completions.create(
            model=model,
            messages=messages
        )
    except Exception as e:
        if "rate_limit" in str(e).lower():
            print(f"⚠️ 触发限流，等待重试...")
        raise

使用指数退避重试机制
response = call_with_retry(client_gpt, "gpt-5.5", [
    {"role": "user", "content": "Hello"}
])

def truncate_messages(messages: list, max_tokens: int = 8000):
    """截断消息以适应上下文窗口"""
    total_tokens = sum(len(m["content"]) // 4 for m in messages)
    
    while total_tokens > max_tokens and len(messages) > 1:
        removed = messages.pop(1)  # 保留 system 消息
        total_tokens -= len(removed["content"]) // 4
    
    return messages

安全调用包装
def safe_completion(client, model: str, messages: list):
    """带上下文保护的调用"""
    # DeepSeek V4 支持 128K 上下文
    # GPT-5.5 支持 200K 上下文
    context_limits = {
        "deepseek-v4": 128000,
        "gpt-5.5": 200000
    }
    
    max_context = context_limits.get(model, 32000)
    truncated = truncate_messages(messages, int(max_context * 0.9))
    
    return client.chat.completions.create(
        model=model,
        messages=truncated,
        max_tokens=min(4096, max_context - sum(len(m["content"]) // 4 for m in truncated))
    )

# ❌ 错误 - 使用了错误的模型标识符
response = client.chat.completions.create(
    model="gpt-5",  # 不存在！应该是 gpt-5.5
    messages=[...]
)

❌ 错误 - DeepSeek 模型名大小写问题
response = client.chat.completions.create(
    model="deepseek-v3",  # 应该是 deepseek-v4
    messages=[...]
)

✅ 正确 - 使用 HolySheep 支持的模型名
response = client.chat.completions.create(
    model="gpt-5.5",      # GPT-5.5
    messages=[...]
)

✅ 或者使用 DeepSeek V4
response = client.chat.completions.create(
    model="deepseek-v4",  # DeepSeek V4
    messages=[...]
)

查询可用模型列表
models = client_gpt.models.list()
available = [m.id for m in models.data]
print("可用模型:", available)

def calculate_real_cost(response, model: str) -> dict:
    """精确计算实际成本"""
    usage = response.usage
    
    # 官方定价（$/MTok）
    pricing = {
        "gpt-5.5": {"input": 3.5, "output": 15.0},
        "deepseek-v4": {"input": 0.12, "output": 0.42}
    }
    
    price = pricing.get(model, pricing["deepseek-v4"])
    
    input_cost = usage.prompt_tokens * price["input"] / 1_000_000
    output_cost = usage.completion_tokens * price["output"] / 1_000_000
    total = input_cost + output_cost
    
    return {
        "input_tokens": usage.prompt_tokens,
        "output_tokens": usage.completion_tokens,
        "input_cost_usd": round(input_cost, 6),
        "output_cost_usd": round(output_cost, 6),
        "total_cost_usd": round(total, 6)
    }

使用示例
result = client_gpt.chat.completions.create(
    model="deepseek-v4",
    messages=[{"role": "user", "content": "Hello"}]
)

cost_breakdown = calculate_real_cost(result, "deepseek-v4")
print(f"本次调用成本: ${cost_breakdown['total_cost_usd']}")

import threading
from contextvars import ContextVar

使用上下文变量避免状态污染
_request_id: ContextVar[str] = ContextVar("request_id", default="")

class ThreadSafeRouter:
    """线程安全的路由器"""
    
    def __init__(self):
        self._lock = threading.Lock()
        self._stats = {}
    
    def process(self, query: str, request_id: str = None) -> dict:
        if request_id is None:
            request_id = f"req_{threading.current_thread().ident}_{time.time()}"
        
        # 设置当前请求上下文
        token = _request_id.set(request_id)
        try:
            with self._lock:  # 保护共享状态
                state = app.invoke({"query": query})
                self._stats[request_id] = {
                    "model": state["model"],
                    "cost": state["cost_usd"]
                }
                return state
        finally:
            _request_id.reset(token)

在多线程环境中安全使用
router = ThreadSafeRouter()
with ThreadPoolExecutor(max_workers=4) as executor:
    futures = [
        executor.submit(router.process, q, f"req_{i}")
        for i, q in enumerate(test_queries)
    ]
    results = [f.result() for f in futures]