442 lines
14 KiB
Python
442 lines
14 KiB
Python
"""
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LLM 提供方测试服务
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"""
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import asyncio
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import json
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import socket
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import time
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import urllib.error
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import urllib.parse
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import urllib.request
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from typing import Any, Dict, List, Optional
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PROVIDER_CATALOG: List[Dict[str, str]] = [
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{
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"value": "openai",
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"label": "OpenAI",
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"default_endpoint_url": "https://api.openai.com/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "deepseek",
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"label": "DeepSeek",
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"default_endpoint_url": "https://api.deepseek.com/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "anthropic",
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"label": "Anthropic",
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"default_endpoint_url": "https://api.anthropic.com",
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"protocol": "anthropic",
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},
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{
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"value": "gemini",
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"label": "Google Gemini",
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"default_endpoint_url": "https://generativelanguage.googleapis.com/v1beta",
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"protocol": "gemini",
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},
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{
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"value": "dashscope",
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"label": "阿里百炼",
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"default_endpoint_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "zhipu",
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"label": "智谱 AI",
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"default_endpoint_url": "https://open.bigmodel.cn/api/paas/v4",
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"protocol": "openai_compatible",
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},
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{
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"value": "moonshot",
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"label": "Moonshot AI",
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"default_endpoint_url": "https://api.moonshot.cn/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "groq",
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"label": "Groq",
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"default_endpoint_url": "https://api.groq.com/openai/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "openrouter",
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"label": "OpenRouter",
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"default_endpoint_url": "https://openrouter.ai/api/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "siliconflow",
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"label": "SiliconFlow",
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"default_endpoint_url": "https://api.siliconflow.cn/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "ollama",
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"label": "Ollama",
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"default_endpoint_url": "http://localhost:11434/v1",
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"protocol": "openai_compatible",
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},
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{
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"value": "ark",
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"label": "火山方舟",
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"default_endpoint_url": "https://ark.cn-beijing.volces.com/api/v3",
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"protocol": "openai_compatible",
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},
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{
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"value": "custom",
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"label": "自定义兼容接口",
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"default_endpoint_url": "",
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"protocol": "openai_compatible",
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},
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]
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PROVIDER_MAP = {item["value"]: item for item in PROVIDER_CATALOG}
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class LLMProviderService:
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"""LLM 提供方测试服务"""
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@staticmethod
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def get_provider_catalog() -> List[Dict[str, str]]:
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return PROVIDER_CATALOG
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@staticmethod
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def get_provider_label(provider: Optional[str]) -> str:
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if not provider:
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return "自定义模型"
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return PROVIDER_MAP.get(provider, {}).get("label", provider)
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@staticmethod
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def get_default_endpoint_url(provider: Optional[str]) -> str:
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if not provider:
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return ""
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return PROVIDER_MAP.get(provider, {}).get("default_endpoint_url", "")
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@classmethod
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def build_model_name(cls, provider: Optional[str], llm_model_name: str) -> str:
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model_name = (llm_model_name or "").strip()
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label = cls.get_provider_label(provider)
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if not model_name:
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return label
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return f"{label} {model_name}"
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@staticmethod
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def build_model_code(provider: Optional[str], llm_model_name: str) -> str:
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provider_part = (provider or "custom").strip().lower()
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model_part = (llm_model_name or "").strip().lower()
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sanitized = []
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previous_is_separator = False
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for char in model_part:
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if char.isalnum():
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sanitized.append(char)
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previous_is_separator = False
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else:
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if not previous_is_separator:
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sanitized.append("_")
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previous_is_separator = True
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model_slug = "".join(sanitized).strip("_") or "model"
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return f"llm_{provider_part}_{model_slug}"
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@classmethod
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async def test_model_connection(cls, payload: Dict[str, Any]) -> Dict[str, Any]:
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provider = payload.get("provider")
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provider_meta = PROVIDER_MAP.get(provider, PROVIDER_MAP["custom"])
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endpoint_url = (payload.get("endpoint_url") or provider_meta.get("default_endpoint_url") or "").strip()
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llm_model_name = (payload.get("llm_model_name") or "").strip()
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api_key = (payload.get("api_key") or "").strip()
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if not endpoint_url:
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raise ValueError("缺少接口地址，请先选择提供方或手动填写 base_url")
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if not llm_model_name:
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raise ValueError("缺少模型名称，请填写 llm_model_name")
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if provider not in {"ollama"} and not api_key:
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raise ValueError("缺少 API Key，请填写后再测试")
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timeout = int(payload.get("llm_timeout") or 120)
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temperature = float(payload.get("llm_temperature") or 0.7)
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top_p = float(payload.get("llm_top_p") or 0.9)
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max_tokens = int(payload.get("llm_max_tokens") or 2048)
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system_prompt = (payload.get("llm_system_prompt") or "").strip()
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started_at = time.perf_counter()
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preview = await asyncio.to_thread(
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cls._send_test_request,
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provider_meta.get("protocol", "openai_compatible"),
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provider,
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endpoint_url,
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api_key,
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llm_model_name,
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timeout,
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temperature,
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top_p,
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max_tokens,
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system_prompt,
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)
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latency_ms = int((time.perf_counter() - started_at) * 1000)
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return {
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"provider": provider,
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"endpoint_url": endpoint_url,
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"llm_model_name": llm_model_name,
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"latency_ms": latency_ms,
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"preview": preview[:200],
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}
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@classmethod
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def _send_test_request(
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cls,
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protocol: str,
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provider: Optional[str],
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endpoint_url: str,
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api_key: str,
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llm_model_name: str,
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timeout: int,
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temperature: float,
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top_p: float,
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max_tokens: int,
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system_prompt: str,
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) -> str:
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if protocol == "anthropic":
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return cls._test_anthropic(
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endpoint_url,
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api_key,
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llm_model_name,
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timeout,
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temperature,
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top_p,
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max_tokens,
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system_prompt,
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)
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if protocol == "gemini":
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return cls._test_gemini(
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endpoint_url,
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api_key,
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llm_model_name,
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timeout,
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temperature,
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top_p,
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max_tokens,
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system_prompt,
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)
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return cls._test_openai_compatible(
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provider,
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endpoint_url,
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api_key,
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llm_model_name,
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timeout,
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temperature,
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top_p,
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max_tokens,
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system_prompt,
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)
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@classmethod
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def _test_openai_compatible(
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cls,
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provider: Optional[str],
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endpoint_url: str,
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api_key: str,
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llm_model_name: str,
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timeout: int,
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temperature: float,
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top_p: float,
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max_tokens: int,
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system_prompt: str,
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) -> str:
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url = cls._join_endpoint(endpoint_url, "/chat/completions")
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headers = {
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"Content-Type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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payload = {
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"model": llm_model_name,
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"messages": cls._build_openai_messages(system_prompt),
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"temperature": temperature,
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"top_p": top_p,
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"max_tokens": min(max_tokens, 256),
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"stream": False,
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}
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response = cls._request_json(url, headers, payload, timeout)
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choices = response.get("choices") or []
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if not choices:
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raise ValueError("测试请求已发送，但未收到模型返回内容")
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content = choices[0].get("message", {}).get("content", "")
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if isinstance(content, list):
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content = "".join(
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item.get("text", "") if isinstance(item, dict) else str(item)
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for item in content
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)
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content = str(content).strip()
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if not content:
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raise ValueError("模型返回成功，但内容为空")
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return content
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@classmethod
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def _test_anthropic(
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cls,
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endpoint_url: str,
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api_key: str,
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llm_model_name: str,
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timeout: int,
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temperature: float,
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top_p: float,
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max_tokens: int,
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system_prompt: str,
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) -> str:
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url = cls._join_endpoint(endpoint_url, "/v1/messages")
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headers = {
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"Content-Type": "application/json",
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"x-api-key": api_key,
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"anthropic-version": "2023-06-01",
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}
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payload = {
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"model": llm_model_name,
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"max_tokens": min(max_tokens, 256),
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"temperature": temperature,
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"top_p": top_p,
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"messages": [
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{
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"role": "user",
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"content": "请只回复“连接测试成功”。",
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}
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],
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}
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if system_prompt:
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payload["system"] = system_prompt
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response = cls._request_json(url, headers, payload, timeout)
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content = response.get("content") or []
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texts = []
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for item in content:
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if isinstance(item, dict) and item.get("type") == "text":
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texts.append(item.get("text", ""))
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preview = "".join(texts).strip()
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if not preview:
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raise ValueError("模型返回成功，但内容为空")
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return preview
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@classmethod
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def _test_gemini(
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cls,
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endpoint_url: str,
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api_key: str,
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llm_model_name: str,
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timeout: int,
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temperature: float,
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top_p: float,
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max_tokens: int,
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system_prompt: str,
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) -> str:
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model_path = llm_model_name if llm_model_name.startswith("models/") else f"models/{llm_model_name}"
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encoded_model_path = "/".join(urllib.parse.quote(part) for part in model_path.split("/"))
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url = f"{endpoint_url.rstrip('/')}/{encoded_model_path}:generateContent?key={urllib.parse.quote(api_key)}"
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headers = {
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"Content-Type": "application/json",
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}
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payload = {
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"contents": [
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{
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"role": "user",
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"parts": [{"text": "请只回复“连接测试成功”。"}],
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}
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],
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"generationConfig": {
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"temperature": temperature,
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"topP": top_p,
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"maxOutputTokens": min(max_tokens, 256),
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},
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}
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if system_prompt:
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payload["systemInstruction"] = {
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"parts": [{"text": system_prompt}],
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}
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response = cls._request_json(url, headers, payload, timeout)
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candidates = response.get("candidates") or []
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if not candidates:
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raise ValueError("测试请求已发送，但未收到模型返回内容")
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parts = candidates[0].get("content", {}).get("parts", [])
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preview = "".join(
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part.get("text", "") for part in parts if isinstance(part, dict)
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).strip()
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if not preview:
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raise ValueError("模型返回成功，但内容为空")
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return preview
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@staticmethod
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def _build_openai_messages(system_prompt: str) -> List[Dict[str, str]]:
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messages: List[Dict[str, str]] = []
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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": "请只回复“连接测试成功”。"})
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return messages
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@staticmethod
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def _join_endpoint(base_url: str, suffix: str) -> str:
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normalized = base_url.rstrip("/")
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if normalized.endswith(suffix):
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return normalized
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return f"{normalized}{suffix}"
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@classmethod
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def _request_json(
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cls,
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url: str,
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headers: Dict[str, str],
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payload: Dict[str, Any],
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timeout: int,
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) -> Dict[str, Any]:
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request = urllib.request.Request(
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url,
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data=json.dumps(payload).encode("utf-8"),
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headers=headers,
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method="POST",
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)
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try:
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with urllib.request.urlopen(request, timeout=timeout) as response:
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body = response.read().decode("utf-8")
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if not body:
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return {}
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return json.loads(body)
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except urllib.error.HTTPError as exc:
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error_body = exc.read().decode("utf-8", errors="ignore")
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message = cls._extract_error_message(error_body) or error_body[:300] or str(exc)
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raise ValueError(f"模型测试失败（HTTP {exc.code}）：{message}") from exc
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except urllib.error.URLError as exc:
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reason = exc.reason
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if isinstance(reason, socket.timeout):
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raise ValueError("模型测试超时，请检查网络或调大超时时间") from exc
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raise ValueError(f"模型测试失败：{reason}") from exc
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except socket.timeout as exc:
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raise ValueError("模型测试超时，请检查网络或调大超时时间") from exc
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except json.JSONDecodeError as exc:
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raise ValueError("模型服务返回了无法解析的响应，请检查接口地址是否正确") from exc
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@staticmethod
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def _extract_error_message(error_body: str) -> Optional[str]:
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if not error_body:
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return None
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try:
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payload = json.loads(error_body)
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except json.JSONDecodeError:
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return error_body.strip()
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if isinstance(payload, dict):
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if isinstance(payload.get("error"), dict):
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return payload["error"].get("message") or payload["error"].get("type")
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return payload.get("message") or payload.get("detail")
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return None
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