feat(stats): 完善调用记录详情并持久化到JSONL文件
- log_call保存完整request/routing/llm三层数据（含NVIDIA分类原始输出） - 新增/stats/raw接口返回原始调用记录（支持分页） - /stats摘要新增tier_distribution、task_type_distribution、avg_routing_ms - 调用历史持久化到data/call_history.jsonl，重启自动恢复 - data/目录加入.gitignore
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3
.gitignore
vendored
3
.gitignore
vendored
@@ -10,6 +10,9 @@ __pycache__/
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.env
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.venv
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# Data (call history logs)
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data/
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# IDE
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.vscode/
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.idea/
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259
main.py
259
main.py
@@ -4,8 +4,11 @@ MVP版 LLM 路由服务
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支持: OpenAI, Anthropic, Gemini, Ollama 等 100+ 提供商
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"""
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import time
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import json
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import os
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import tiktoken
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from typing import List, Dict, Any, Optional
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from pathlib import Path
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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@@ -32,9 +35,29 @@ def get_router():
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return _nvidia_router
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# 调用历史记录
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# 调用历史 - JSON 文件持久化
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CALL_LOG_DIR = Path(__file__).parent / "data"
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CALL_LOG_DIR.mkdir(exist_ok=True)
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CALL_LOG_FILE = CALL_LOG_DIR / "call_history.jsonl"
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# 内存缓存（启动时从文件加载）
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call_history: List[Dict[str, Any]] = []
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def _load_history():
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"""启动时从 JSONL 文件加载历史记录"""
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if CALL_LOG_FILE.exists():
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with open(CALL_LOG_FILE, "r", encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if line:
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try:
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call_history.append(json.loads(line))
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except json.JSONDecodeError:
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continue
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print(f"Loaded {len(call_history)} historical records from {CALL_LOG_FILE}")
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_load_history()
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class Message(BaseModel):
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role: str
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@@ -58,15 +81,6 @@ class ChatResponse(BaseModel):
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latency_ms: float
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class StatsResponse(BaseModel):
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total_calls: int
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total_cost_usd: float
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avg_latency_ms: float
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model_distribution: Dict[str, int]
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provider_distribution: Dict[str, int]
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recent_calls: List[Dict[str, Any]]
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app = FastAPI(
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title="LLM Router MVP",
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description="基于 LiteLLM + NVIDIA 分类器的多提供商路由服务（支持3-tier智能路由）",
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@@ -102,27 +116,46 @@ def select_model_by_length(messages: List[Message]) -> str:
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return DEFAULT_ROUTING["complex"]
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def select_model_by_nvidia_classifier(messages: List[Message]) -> str:
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def select_model_by_nvidia_classifier(messages: List[Message]) -> tuple:
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"""
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基于 NVIDIA 多头分类器选择模型（3-tier路由）
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NVIDIA 输出: 多维度复杂度评分
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映射到 Qwen 模型:
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- simple -> qwen-flash (简单任务)
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- medium -> qwen-plus (中等任务)
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- complex -> qwen-max (复杂任务)
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Returns:
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(model_key, routing_detail) - 模型名称 + 路由分类细节
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"""
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# 取最后一条用户消息作为查询
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query = messages[-1].content if messages else ""
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try:
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router = get_router()
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model = router.select_model(query)
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return model
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start = time.time()
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result = router.predict(query)
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routing_ms = (time.time() - start) * 1000
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model_map = {"simple": "qwen-flash", "medium": "qwen-plus", "complex": "qwen-max"}
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model_key = model_map[result["tier"]]
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routing_detail = {
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"method": "nvidia_classifier",
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"query": query,
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"routing_latency_ms": round(routing_ms, 2),
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"tier": result["tier"],
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"complexity_score": result["complexity_score"],
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"task_type": result["task_type"],
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"domain_knowledge": result["domain_knowledge"],
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"reasoning": result["reasoning"],
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"creativity": result["creativity"],
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}
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return model_key, routing_detail
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except Exception as e:
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# NVIDIA 分类器失败时回退到 token 长度策略
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print(f"NVIDIA routing failed: {e}, falling back to token length")
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return select_model_by_length(messages)
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model_key = select_model_by_length(messages)
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routing_detail = {
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"method": "fallback_token_length",
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"query": query,
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"routing_latency_ms": 0,
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"error": str(e),
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}
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return model_key, routing_detail
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def get_provider_model(model_key: str) -> str:
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@@ -154,61 +187,114 @@ def get_provider_from_model(model_name: str) -> str:
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return "unknown"
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def log_call(model: str, provider: str, cost: float, latency_ms: float, tokens: int):
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"""记录调用历史"""
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call_history.append({
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"model": model,
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"provider": provider,
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"cost_usd": cost,
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"latency_ms": latency_ms,
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"tokens": tokens,
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def log_call(
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model: str,
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provider: str,
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cost: float,
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latency_ms: float,
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input_tokens: int,
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output_tokens: int,
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messages: List[Dict[str, str]],
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response_content: str,
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response_id: str,
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routing_detail: Optional[Dict[str, Any]],
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request_params: Dict[str, Any],
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):
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"""记录完整调用历史（含路由细节 + LLM 原始数据，供后续调优）"""
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record = {
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"timestamp": time.time(),
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})
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# 请求
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"request": {
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"messages": messages,
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"temperature": request_params.get("temperature"),
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"max_tokens": request_params.get("max_tokens"),
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"user_specified_model": request_params.get("user_specified_model"),
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},
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# 路由决策
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"routing": routing_detail,
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# LLM 调用
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"llm": {
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"model": model,
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"provider": provider,
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"response_id": response_id,
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"response_content": response_content,
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"input_tokens": input_tokens,
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"output_tokens": output_tokens,
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"total_tokens": input_tokens + output_tokens,
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"cost_usd": cost,
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"llm_latency_ms": round(latency_ms, 2),
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},
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}
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call_history.append(record)
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# 追加写入 JSONL 文件
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with open(CALL_LOG_FILE, "a", encoding="utf-8") as f:
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f.write(json.dumps(record, ensure_ascii=False) + "\n")
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@app.post("/v1/chat/completions", response_model=ChatResponse)
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async def chat_completions(request: ChatRequest):
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"""
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聊天完成接口
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如果 request.model 未指定，则根据 token 长度自动路由
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如果 request.model 未指定，则使用 NVIDIA 分类器智能路由
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"""
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# 选择模型
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routing_detail = None
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if request.model:
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model_key = request.model
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routing_detail = {
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"method": "user_specified",
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"query": request.messages[-1].content if request.messages else "",
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}
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else:
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# 使用 NVIDIA 多头分类器智能路由（支持3-tier）
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model_key = select_model_by_nvidia_classifier(request.messages)
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model_key, routing_detail = select_model_by_nvidia_classifier(request.messages)
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# 获取 LiteLLM 模型名称
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provider_model = get_provider_model(model_key)
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provider = get_provider_from_model(provider_model)
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messages_raw = [{"role": m.role, "content": m.content} for m in request.messages]
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start_time = time.time()
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try:
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# 使用 LiteLLM 统一调用
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response = await acompletion(
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model=provider_model,
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messages=[{"role": m.role, "content": m.content} for m in request.messages],
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messages=messages_raw,
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temperature=request.temperature,
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max_tokens=request.max_tokens,
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)
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latency_ms = (time.time() - start_time) * 1000
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# 计算成本
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input_tokens = response.usage.prompt_tokens
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output_tokens = response.usage.completion_tokens
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cost = calculate_cost(model_key, input_tokens, output_tokens)
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response_content = response.choices[0].message.content
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# 记录调用
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log_call(model_key, provider, cost, latency_ms, input_tokens + output_tokens)
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# 记录完整调用数据
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log_call(
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model=model_key,
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provider=provider,
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cost=cost,
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latency_ms=latency_ms,
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input_tokens=input_tokens,
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output_tokens=output_tokens,
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messages=messages_raw,
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response_content=response_content,
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response_id=response.id,
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routing_detail=routing_detail,
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request_params={
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"temperature": request.temperature,
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"max_tokens": request.max_tokens,
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"user_specified_model": request.model,
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},
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)
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return ChatResponse(
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id=response.id,
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model=model_key,
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provider=provider,
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content=response.choices[0].message.content,
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content=response_content,
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usage={
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"prompt_tokens": input_tokens,
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"completion_tokens": output_tokens,
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@@ -238,52 +324,73 @@ async def list_models():
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}
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@app.get("/stats", response_model=StatsResponse)
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@app.get("/stats")
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async def get_stats():
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"""获取调用统计"""
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"""获取调用统计摘要"""
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if not call_history:
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return StatsResponse(
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total_calls=0,
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total_cost_usd=0.0,
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avg_latency_ms=0.0,
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model_distribution={},
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provider_distribution={},
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recent_calls=[],
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)
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return {
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"total_calls": 0,
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"total_cost_usd": 0.0,
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"avg_latency_ms": 0.0,
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"model_distribution": {},
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"tier_distribution": {},
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"task_type_distribution": {},
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}
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total_calls = len(call_history)
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total_cost = sum(c["cost_usd"] for c in call_history)
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avg_latency = sum(c["latency_ms"] for c in call_history) / total_calls
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total_cost = sum(c["llm"]["cost_usd"] for c in call_history)
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avg_latency = sum(c["llm"]["llm_latency_ms"] for c in call_history) / total_calls
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# 模型分布
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model_dist: Dict[str, int] = {}
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provider_dist: Dict[str, int] = {}
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tier_dist: Dict[str, int] = {}
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task_dist: Dict[str, int] = {}
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for call in call_history:
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model = call["model"]
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provider = call["provider"]
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model = call["llm"]["model"]
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model_dist[model] = model_dist.get(model, 0) + 1
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provider_dist[provider] = provider_dist.get(provider, 0) + 1
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# 最近 10 条记录
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recent = [
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{
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"model": c["model"],
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"provider": c["provider"],
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"cost_usd": round(c["cost_usd"], 6),
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"latency_ms": round(c["latency_ms"], 2),
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"tokens": c["tokens"],
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}
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for c in call_history[-10:]
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]
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routing = call.get("routing") or {}
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if routing.get("tier"):
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tier = routing["tier"]
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tier_dist[tier] = tier_dist.get(tier, 0) + 1
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if routing.get("task_type"):
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task = routing["task_type"]
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task_dist[task] = task_dist.get(task, 0) + 1
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return StatsResponse(
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total_calls=total_calls,
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total_cost_usd=round(total_cost, 6),
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avg_latency_ms=round(avg_latency, 2),
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model_distribution=model_dist,
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provider_distribution=provider_dist,
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recent_calls=recent,
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)
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return {
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"total_calls": total_calls,
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"total_cost_usd": round(total_cost, 6),
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"avg_latency_ms": round(avg_latency, 2),
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"avg_routing_ms": round(
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sum(c.get("routing", {}).get("routing_latency_ms", 0) for c in call_history) / total_calls, 2
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),
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"model_distribution": model_dist,
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"tier_distribution": tier_dist,
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"task_type_distribution": task_dist,
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}
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@app.get("/stats/raw")
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async def get_stats_raw(limit: int = 50, offset: int = 0):
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"""
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获取原始调用记录（含路由分类细节 + LLM 完整数据）
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用于后续调优和分析
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参数:
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- limit: 返回条数（默认50）
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- offset: 偏移量（默认0，从最新开始）
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"""
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total = len(call_history)
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# 倒序返回（最新在前）
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records = list(reversed(call_history))
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page = records[offset:offset + limit]
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return {
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"total": total,
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"limit": limit,
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"offset": offset,
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"records": page,
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}
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@app.get("/health")
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