254 lines
8.8 KiB
Python
254 lines
8.8 KiB
Python
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"""
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LLM Studio适配器
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支持本地LLM Studio服务
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"""
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import json
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from datetime import datetime
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from typing import List, Dict, Any, Optional, AsyncGenerator
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from loguru import logger
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from .base_adapter import BaseAdapter, ChatMessage, AdapterResponse
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from utils.proxy_handler import get_http_client
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class LLMStudioAdapter(BaseAdapter):
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"""
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LLM Studio API适配器
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兼容OpenAI API格式的本地服务
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"""
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DEFAULT_BASE_URL = "http://localhost:1234/v1"
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def __init__(
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self,
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api_key: str = "lm-studio", # LLM Studio使用固定key
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base_url: str = "",
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model: str = "local-model",
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use_proxy: bool = False, # 本地服务不需要代理
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proxy_config: Optional[Dict[str, Any]] = None,
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timeout: int = 120, # 本地模型可能需要更长时间
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**kwargs
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):
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super().__init__(
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api_key=api_key,
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base_url=base_url or self.DEFAULT_BASE_URL,
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model=model,
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use_proxy=use_proxy,
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proxy_config=proxy_config,
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timeout=timeout,
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**kwargs
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)
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async def chat(
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self,
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messages: List[ChatMessage],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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**kwargs
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) -> AdapterResponse:
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"""发送聊天请求"""
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start_time = datetime.utcnow()
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try:
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=self.timeout
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) as client:
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"messages": self._build_messages(messages),
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"temperature": temperature,
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"max_tokens": max_tokens,
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**kwargs
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}
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response = await client.post(
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload
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)
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if response.status_code != 200:
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error_text = response.text
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logger.error(f"LLM Studio API错误: {response.status_code} - {error_text}")
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return AdapterResponse(
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success=False,
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error=f"API错误: {response.status_code} - {error_text}",
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latency_ms=self._calculate_latency(start_time)
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)
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data = response.json()
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choice = data.get("choices", [{}])[0]
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message = choice.get("message", {})
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usage = data.get("usage", {})
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return AdapterResponse(
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success=True,
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content=message.get("content", ""),
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model=data.get("model", self.model),
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finish_reason=choice.get("finish_reason", ""),
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prompt_tokens=usage.get("prompt_tokens", 0),
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completion_tokens=usage.get("completion_tokens", 0),
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total_tokens=usage.get("total_tokens", 0),
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latency_ms=self._calculate_latency(start_time)
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)
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except Exception as e:
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logger.error(f"LLM Studio请求异常: {e}")
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return AdapterResponse(
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success=False,
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error=str(e),
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latency_ms=self._calculate_latency(start_time)
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)
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async def chat_stream(
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self,
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messages: List[ChatMessage],
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temperature: float = 0.7,
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max_tokens: int = 2000,
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**kwargs
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) -> AsyncGenerator[str, None]:
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"""发送流式聊天请求"""
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try:
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=self.timeout
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) as client:
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": self.model,
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"messages": self._build_messages(messages),
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"temperature": temperature,
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"max_tokens": max_tokens,
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"stream": True,
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**kwargs
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}
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async with client.stream(
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"POST",
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f"{self.base_url}/chat/completions",
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headers=headers,
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json=payload
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) as response:
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async for line in response.aiter_lines():
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if line.startswith("data: "):
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data_str = line[6:]
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if data_str.strip() == "[DONE]":
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break
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try:
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data = json.loads(data_str)
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delta = data.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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yield content
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except json.JSONDecodeError:
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continue
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except Exception as e:
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logger.error(f"LLM Studio流式请求异常: {e}")
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yield f"[错误: {str(e)}]"
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async def test_connection(self) -> Dict[str, Any]:
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"""测试API连接"""
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start_time = datetime.utcnow()
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try:
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# 首先检查服务是否在运行
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=10
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) as client:
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# 获取模型列表
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response = await client.get(
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f"{self.base_url}/models",
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headers={"Authorization": f"Bearer {self.api_key}"}
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)
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if response.status_code != 200:
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return {
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"success": False,
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"message": "LLM Studio服务未运行或不可访问",
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"latency_ms": self._calculate_latency(start_time)
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}
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data = response.json()
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models = [m.get("id", "") for m in data.get("data", [])]
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if not models:
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return {
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"success": False,
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"message": "LLM Studio中没有加载的模型",
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"latency_ms": self._calculate_latency(start_time)
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}
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# 发送测试消息
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test_messages = [
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ChatMessage(role="user", content="Hello, respond with 'OK'")
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]
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response = await self.chat(
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messages=test_messages,
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temperature=0,
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max_tokens=10
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)
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if response.success:
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return {
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"success": True,
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"message": "连接成功",
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"model": response.model,
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"latency_ms": response.latency_ms
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}
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else:
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return {
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"success": False,
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"message": response.error,
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"latency_ms": response.latency_ms
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}
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except Exception as e:
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return {
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"success": False,
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"message": str(e),
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"latency_ms": self._calculate_latency(start_time)
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}
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async def list_models(self) -> List[Dict[str, Any]]:
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"""
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列出LLM Studio中加载的模型
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Returns:
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模型信息列表
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"""
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try:
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async with get_http_client(
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use_proxy=self.use_proxy,
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proxy_config=self.proxy_config,
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timeout=10
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) as client:
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response = await client.get(
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f"{self.base_url}/models",
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headers={"Authorization": f"Bearer {self.api_key}"}
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)
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if response.status_code == 200:
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data = response.json()
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return data.get("data", [])
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except Exception as e:
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logger.error(f"获取LLM Studio模型列表失败: {e}")
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return []
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