wsb1224
67b2dac23f
迁移系统加固 — 加 advisory lock 防并发，失败回滚并中止启动（原来是 catch-and-continue） 安全漏洞 — Settings/History API 权限收紧，API key 返回掩码值，海报 case 所有权校验 依赖缺失 — requirements.txt 补齐 python-pptx/openai/Pillow，Dockerfile 改为统一安装 海报鉴权下载 — 前端全部改用 authenticated blob，不再 window.open 无 token URL LLM 配置分离 — 海报文案读取 poster_llm_*（不再复用 ppt_llm_*），支持 config namespace 图片生成器 — 兼容 b64_json 和 URL 两种响应格式，追踪 generation_mode/provider/model 种子数据 — 新环境自动获得 2 个海报模板 + 2 个文案模板
502 lines
18 KiB
Python
502 lines
18 KiB
Python
"""统一 LLM 客户端 — 多供应商自动切换 + 限流保护。
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支持: DeepSeek → MiniMax → Gemini
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特点:
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- 限流保护：多用户并发时自动排队
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- 失败切换：一个供应商失败自动切换下一个
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- 统一接口：所有 LLM 调用走这里
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"""
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import os
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import re
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import json
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import time
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import asyncio
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import logging
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from dataclasses import dataclass, field
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from typing import Optional
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import httpx
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logger = logging.getLogger(__name__)
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DEFAULT_TIMEOUT_MS = 180_000
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# ─── 配置 ───────────────────────────────────────────────
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@dataclass
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class LLMProviderConfig:
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name: str
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base_url: str
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model: str
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max_retries: int = 2
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rate_limit: int = 0 # 每分钟请求数，0=无限制
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PROVIDERS = {
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"openai": LLMProviderConfig(
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name="openai",
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base_url="https://api.openai.com/v1",
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model="gpt-4o",
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max_retries=2,
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rate_limit=0,
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),
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"deepseek": LLMProviderConfig(
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name="deepseek",
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base_url="https://api.deepseek.com/v1",
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model="deepseek-v4-pro",
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max_retries=2,
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rate_limit=0,
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),
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"minimax": LLMProviderConfig(
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name="minimax",
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base_url="https://api.minimax.chat/v1",
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model="MiniMax-2.7-Flash",
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max_retries=2,
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rate_limit=30,
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),
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"gemini": LLMProviderConfig(
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name="gemini",
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base_url="https://generativelanguage.googleapis.com/v1/models",
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model="gemini-2.5-flash",
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max_retries=1,
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rate_limit=60,
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),
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}
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def _format_exception(exc: Exception) -> str:
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message = str(exc) or repr(exc)
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return f"{exc.__class__.__name__}: {message}"
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def _parse_timeout_ms(value: Optional[str], default: int = DEFAULT_TIMEOUT_MS) -> int:
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try:
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timeout_ms = int(str(value or "").strip())
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except (TypeError, ValueError):
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return default
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return timeout_ms if timeout_ms > 0 else default
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# ─── 限流器 ─────────────────────────────────────────────
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class RateLimiter:
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"""Token Bucket 限流器。"""
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def __init__(self, max_tokens: int, refill_ms: int = 60_000):
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self.max_tokens = max_tokens
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self.refill_ms = refill_ms / 1000 # 转换为秒
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self.tokens = [0.0] * max_tokens
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self.last_refill = time.monotonic()
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async def acquire(self, timeout_ms: int = 30_000) -> bool:
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"""获取一个令牌，超时返回 False。"""
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if self.max_tokens == 0:
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return True
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start = time.monotonic()
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timeout_s = timeout_ms / 1000
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while True:
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self._refill_if_needed()
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now = time.monotonic()
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# 找最早可用的令牌槽位
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for i, t in enumerate(self.tokens):
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if now - t >= self.refill_ms:
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self.tokens[i] = now
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return True
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# 所有槽位都在使用中，等待最老的释放
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oldest = min(self.tokens)
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wait = min(self.refill_ms - (now - oldest), timeout_s)
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if wait <= 0 or (time.monotonic() - start) >= timeout_s:
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return False
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await asyncio.sleep(min(wait, 2.0))
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def _refill_if_needed(self):
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now = time.monotonic()
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if now - self.last_refill >= self.refill_ms:
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self.tokens = [0.0] * self.max_tokens
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self.last_refill = now
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# ─── 响应类型 ────────────────────────────────────────────
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@dataclass
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class LLMResponse:
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content: str
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provider: str
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tokens: Optional[dict] = None # {"input": int, "output": int}
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latency_ms: float = 0
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# ─── 单供应商调用 ─────────────────────────────────────────
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async def _call_provider(
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config: LLMProviderConfig,
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api_key: str,
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messages: list[dict],
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timeout_ms: int = 60_000,
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) -> LLMResponse:
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"""调用单个 LLM 供应商。"""
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start = time.monotonic()
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timeout_s = timeout_ms / 1000
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headers = {"Content-Type": "application/json"}
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if config.name in ("deepseek", "minimax"):
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# OpenAI 兼容格式
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headers["Authorization"] = f"Bearer {api_key}"
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if config.name == "minimax":
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url = f"{config.base_url}/text/chatcompletion_v2"
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else:
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url = f"{config.base_url}/chat/completions"
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body = {
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"model": config.model,
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"messages": messages,
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"temperature": 0.3,
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"max_tokens": 4096,
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}
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elif config.name == "gemini":
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# Gemini 格式
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model_part = f"{config.model}:generateContent" if ":" not in config.model else config.model
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url = f"{config.base_url}/{model_part}?key={api_key}"
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contents = []
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for m in messages:
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role = "model" if m["role"] == "assistant" else "user"
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contents.append({"role": role, "parts": [{"text": m["content"]}]})
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body = {
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"contents": contents,
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"generationConfig": {"temperature": 0.3, "maxOutputTokens": 4096},
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}
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else:
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# 自定义供应商：OpenAI 兼容格式
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headers["Authorization"] = f"Bearer {api_key}"
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base = config.base_url.rstrip("/")
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url = f"{base}/chat/completions"
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body = {
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"model": config.model,
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"messages": messages,
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"temperature": 0.3,
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"max_tokens": 4096,
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}
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async with httpx.AsyncClient(timeout=timeout_s) as client:
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resp = await client.post(url, json=body, headers=headers)
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resp.raise_for_status()
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data = resp.json()
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latency_ms = (time.monotonic() - start) * 1000
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# 解析响应
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if config.name == "gemini":
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content = ""
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candidates = data.get("candidates", [])
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if candidates:
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parts = candidates[0].get("content", {}).get("parts", [])
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if parts:
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content = parts[0].get("text", "")
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usage_meta = data.get("usageMetadata")
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tokens = None
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if usage_meta:
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tokens = {
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"input": usage_meta.get("promptTokenCount", 0),
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"output": usage_meta.get("candidatesTokenCount", 0),
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}
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else:
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# OpenAI 兼容格式（deepseek / minimax / 自定义供应商）
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content = ""
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choices = data.get("choices", [])
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if choices:
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content = choices[0].get("message", {}).get("content", "")
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usage = data.get("usage")
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tokens = None
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if usage:
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tokens = {
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"input": usage.get("prompt_tokens", 0),
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"output": usage.get("completion_tokens", 0),
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}
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return LLMResponse(
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content=content,
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provider=config.name,
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tokens=tokens,
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latency_ms=latency_ms,
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)
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async def _call_dify(
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model: str,
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messages: list[dict],
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timeout_ms: int = 60_000,
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) -> LLMResponse:
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"""通过 Dify Chat API 调用模型（复用 Dify 已配置的 API Key）。"""
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import flask
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dify_base_url = flask.current_app.config.get("DIFY_BASE_URL", os.getenv("DIFY_BASE_URL", "http://localhost:5001"))
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dify_api_key = flask.current_app.config.get("BAODAN_CHAT_API_KEY", os.getenv("DIFY_CHAT_APP_API_KEY", ""))
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if not dify_api_key:
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raise RuntimeError("未配置 DIFY_CHAT_APP_API_KEY，无法使用 Dify 模式")
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# 合并 messages 为 query（Dify Chat API 不支持多轮 messages 格式）
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system_parts = []
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user_parts = []
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for m in messages:
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if m["role"] == "system":
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system_parts.append(m["content"])
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else:
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user_parts.append(m["content"])
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query = "\n\n".join(user_parts)
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if system_parts:
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query = "\n\n".join(system_parts) + "\n\n" + query
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# 如果有指定模型，在 query 前加上模型提示
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if model:
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query = f"[请使用模型 {model} 回答]\n\n{query}"
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start = time.monotonic()
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timeout_s = timeout_ms / 1000
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url = f"{dify_base_url.rstrip('/')}/v1/chat-messages"
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headers = {
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"Authorization": f"Bearer {dify_api_key}",
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"Content-Type": "application/json",
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}
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body = {
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"inputs": {},
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"query": query,
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"response_mode": "blocking",
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"user": "insurance-system",
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}
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async with httpx.AsyncClient(timeout=timeout_s) as client:
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resp = await client.post(url, json=body, headers=headers)
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resp.raise_for_status()
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data = resp.json()
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latency_ms = (time.monotonic() - start) * 1000
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content = data.get("answer", "")
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tokens = None
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usage = data.get("metadata", {}).get("usage", {})
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if usage:
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tokens = {
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"input": usage.get("prompt_tokens", 0),
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"output": usage.get("completion_tokens", 0),
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}
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return LLMResponse(content=content, provider="dify", tokens=tokens, latency_ms=latency_ms)
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# ─── 统一客户端 ───────────────────────────────────────────
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class LLMClient:
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"""多供应商 LLM 客户端，支持自动切换和速率限制。
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config_prefix: 数据库配置键前缀，如 "ppt" 读取 ppt_llm_*，"poster" 读取 poster_llm_*。
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"""
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def __init__(self, config_prefix: str = "ppt"):
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self._configs: list[tuple[LLMProviderConfig, str]] = []
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self._limiters: dict[str, RateLimiter] = {}
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self._active_idx = 0
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self._config_prefix = config_prefix
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self._timeout_ms = _parse_timeout_ms(os.getenv(f"{config_prefix.upper()}_LLM_TIMEOUT_MS"))
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self._db_config_time: float = 0 # 上次从数据库加载配置的时间戳
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self._db_config_ttl: float = 60 # 配置缓存有效期（秒）
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self._load_env_config()
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def _load_env_config(self):
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"""从环境变量加载默认配置。"""
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prefix = self._config_prefix.upper()
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deepseek_key = os.environ.get(f"{prefix}_LLM_API_KEY") or os.environ.get("DEEPSEEK_API_KEY") or os.environ.get("OPENAI_API_KEY", "")
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minimax_key = os.environ.get("MINIMAX_API_KEY", "")
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gemini_key = os.environ.get("GEMINI_API_KEY", "")
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if deepseek_key:
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self._configs.append((PROVIDERS["deepseek"], deepseek_key))
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self._limiters["deepseek"] = RateLimiter(PROVIDERS["deepseek"].rate_limit)
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if minimax_key:
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self._configs.append((PROVIDERS["minimax"], minimax_key))
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self._limiters["minimax"] = RateLimiter(PROVIDERS["minimax"].rate_limit)
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if gemini_key:
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self._configs.append((PROVIDERS["gemini"], gemini_key))
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self._limiters["gemini"] = RateLimiter(PROVIDERS["gemini"].rate_limit)
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if not self._configs:
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logger.warning("[LLMClient] 未配置任何 API Key，使用 mock 模式")
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def _try_load_db_config(self):
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"""尝试从数据库加载模型配置（带缓存，不阻塞）。"""
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now = time.monotonic()
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if now - self._db_config_time < self._db_config_ttl:
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return
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self._db_config_time = now
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prefix = self._config_prefix
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try:
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from insurance.models.system_setting import SystemSetting
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settings = {s.key: s.value for s in SystemSetting.query.filter(
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SystemSetting.key.in_([
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f"{prefix}_llm_provider", f"{prefix}_llm_model",
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f"{prefix}_llm_api_key", f"{prefix}_llm_base_url",
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f"{prefix}_llm_timeout_ms",
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])
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).all()}
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self._timeout_ms = _parse_timeout_ms(
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settings.get(f"{prefix}_llm_timeout_ms"),
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_parse_timeout_ms(os.getenv(f"{prefix.upper()}_LLM_TIMEOUT_MS")),
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)
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provider = settings.get(f"{prefix}_llm_provider", "").strip()
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if not provider:
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return
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model = settings.get(f"{prefix}_llm_model", "").strip()
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# Dify 模式：通过 Dify Chat API 调用，无需独立 API Key
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if provider == "dify":
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cfg = LLMProviderConfig(
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name="dify", base_url="", model=model or "",
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)
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self._configs = [(cfg, "")]
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self._limiters = {"dify": RateLimiter(0)}
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self._active_idx = 0
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logger.info(f"[LLMClient:{prefix}] 使用 Dify 模式: {model}")
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return
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api_key = settings.get(f"{prefix}_llm_api_key", "").strip()
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if not api_key:
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return
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base_url = settings.get(f"{prefix}_llm_base_url", "").strip()
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# 内置供应商：替换对应配置
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if provider in PROVIDERS and not base_url:
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cfg = PROVIDERS[provider]
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if model:
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cfg = LLMProviderConfig(
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name=cfg.name, base_url=cfg.base_url, model=model,
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max_retries=cfg.max_retries, rate_limit=cfg.rate_limit,
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)
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self._configs = [(cfg, api_key)]
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self._limiters = {provider: RateLimiter(cfg.rate_limit)}
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self._active_idx = 0
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logger.info(f"[LLMClient:{prefix}] 使用数据库配置: {provider}/{cfg.model}")
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return
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# 自定义供应商
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if not base_url:
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return
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cfg = LLMProviderConfig(
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name=provider, base_url=base_url, model=model or "gpt-4o-mini",
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)
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self._configs = [(cfg, api_key)]
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self._limiters = {provider: RateLimiter(0)}
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self._active_idx = 0
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logger.info(f"[LLMClient:{prefix}] 使用自定义模型: {provider}/{cfg.model}")
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except Exception:
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pass # 无 Flask 上下文或数据库不可用，使用环境变量配置
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async def chat(self, prompt: str, system_prompt: str = "") -> LLMResponse:
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"""简单聊天。"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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return await self._call(messages)
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async def structured_output(
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self,
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prompt: str,
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system_prompt: str = "",
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schema: Optional[dict] = None,
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) -> tuple[dict, LLMResponse]:
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"""结构化输出（返回 JSON）。返回 (parsed_data, response)。"""
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messages = []
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if system_prompt:
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messages.append({"role": "system", "content": system_prompt})
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full_prompt = prompt
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if schema:
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full_prompt += f"\n\n请以JSON格式输出，格式如下：\n{json.dumps(schema, ensure_ascii=False, indent=2)}"
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full_prompt += "\n重要：只输出JSON，不要任何额外文字。"
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messages.append({"role": "user", "content": full_prompt})
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response = await self._call(messages)
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# 解析 JSON
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json_str = response.content.strip()
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# 尝试提取 markdown 代码块或裸 JSON
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match = re.search(r"```(?:json)?\s*([\s\S]*?)```|(\{[\s\S]*\}|\[[\s\S]*\])$", json_str)
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if match:
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json_str = match.group(1) or match.group(2)
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try:
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data = json.loads(json_str)
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return data, response
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except json.JSONDecodeError:
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# 清理尾逗号
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json_str = re.sub(r",\s*([\]}])", r"\1", json_str)
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try:
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data = json.loads(json_str)
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return data, response
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except json.JSONDecodeError:
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raise ValueError(f"[LLMClient] JSON 解析失败: {json_str[:200]}")
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async def _call(self, messages: list[dict], attempt: int = 0) -> LLMResponse:
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"""多供应商自动切换调用。"""
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self._try_load_db_config()
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if not self._configs:
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raise RuntimeError(
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"未配置任何 LLM API Key，请在管理后台 > 系统配置中设置 "
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"ppt_llm_provider / ppt_llm_api_key，或设置环境变量 DEEPSEEK_API_KEY / OPENAI_API_KEY"
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)
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||
|
||
start_idx = self._active_idx
|
||
tried = set()
|
||
|
||
for i in range(len(self._configs)):
|
||
idx = (start_idx + i) % len(self._configs)
|
||
config, api_key = self._configs[idx]
|
||
|
||
if config.name in tried:
|
||
continue
|
||
tried.add(config.name)
|
||
|
||
# Dify 模式：通过 Dify Chat API 调用
|
||
if config.name == "dify":
|
||
try:
|
||
return await _call_dify(config.model, messages, timeout_ms=self._timeout_ms)
|
||
except Exception as e:
|
||
logger.warning(f"[LLMClient] Dify 调用失败: {_format_exception(e)}")
|
||
continue
|
||
|
||
# 限流
|
||
limiter = self._limiters.get(config.name)
|
||
if limiter:
|
||
acquired = await limiter.acquire(timeout_ms=30_000)
|
||
if not acquired:
|
||
logger.warning(f"[LLMClient] {config.name} 限流超时")
|
||
continue
|
||
|
||
# 调用
|
||
try:
|
||
response = await _call_provider(config, api_key, messages, timeout_ms=self._timeout_ms)
|
||
self._active_idx = idx
|
||
return response
|
||
except Exception as e:
|
||
logger.warning(f"[LLMClient] {config.name} 失败: {_format_exception(e)}")
|
||
if attempt < 3 and i < len(self._configs) - 1:
|
||
self._active_idx = (idx + 1) % len(self._configs)
|
||
|
||
raise RuntimeError(f"所有 LLM 供应商均失败，请检查 API Key、Base URL、模型名称或超时设置（当前 {self._timeout_ms}ms）")
|
||
|
||
def get_status(self) -> dict:
|
||
"""获取当前供应商信息。"""
|
||
available = [c[0].name for c in self._configs]
|
||
active = self._configs[self._active_idx][0].name if self._configs else "none"
|
||
return {"available": available, "active": active}
|
||
|
||
|
||
# ─── 单例 ────────────────────────────────────────────────
|
||
llm_client = LLMClient(config_prefix="ppt")
|
||
poster_llm_client = LLMClient(config_prefix="poster")
|