mirror of
https://github.com/JefferyHcool/BiliNote.git
synced 2026-07-09 06:22:48 +08:00
feat: 添加硅基流动(SiliconFlow)支持和错误处理优化
## 主要更新 ### 新增功能 - 新增 SiliconFlow_provider.py 专用提供商 - 添加硅基流动 API 集成文档 - 实现 Cherry Studio 风格的连接测试 ### 错误处理优化 - 修复前端 Form.tsx 错误显示问题 - 改进 universal_gpt.py 异常处理逻辑 - 统一 URL 格式处理，避免路径重复 ### 兼容性改进 - 优化 OpenAI 兼容提供商 URL 处理 - 增强模型列表获取的容错性 - 添加详细的调试日志 ### 安全性提升 - 更新 .gitignore 保护敏感信息 - 移除示例配置文件 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
yangyuguang
@@ -5,6 +5,7 @@ from openai import OpenAI
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from app.utils.logger import get_logger
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logging= get_logger(__name__)
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class OpenAICompatibleProvider:
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def __init__(self, api_key: str, base_url: str, model: Union[str, None]=None):
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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@@ -17,15 +18,148 @@ class OpenAICompatibleProvider:
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@staticmethod
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def test_connection(api_key: str, base_url: str) -> bool:
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try:
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client = OpenAI(api_key=api_key, base_url=base_url)
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model = client.models.list()
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# for segment in model:
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# print(segment)
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# print(model)
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logging.info("连通性测试成功")
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return True
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# 调试：打印API Key的实际长度和内容
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logging.info(f"正在测试连接 - API Key长度: {len(api_key)}, 前8位: {api_key[:8]}, 后4位: {api_key[-4:] if len(api_key) > 4 else 'TOO_SHORT'}")
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logging.info(f"Base URL: {base_url}")
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# 硅基流动特殊处理：参考Cherry Studio的实现方式
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if "siliconflow" in base_url.lower():
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logging.info("检测到硅基流动，参考Cherry Studio实现方式")
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# 标准化URL处理，避免路径重复
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base_url_clean = base_url.rstrip('/')
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if base_url_clean.endswith("/v1"):
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# 如果用户输入了/v1，直接使用
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api_base = base_url_clean
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test_url = f"{api_base}/chat/completions"
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elif base_url_clean.endswith("/chat/completions"):
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# 如果用户直接输入了完整路径，直接使用
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test_url = base_url_clean
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api_base = base_url_clean.replace("/chat/completions", "")
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else:
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# Cherry Studio方式：不加/v1后缀，直接加/chat/completions
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api_base = base_url_clean
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test_url = f"{base_url_clean}/chat/completions"
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logging.info(f"使用API基地址: {api_base}")
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logging.info(f"测试URL: {test_url}")
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# 先用requests验证（Cherry Studio方式）
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import requests
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import json
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headers = {
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"Authorization": f"Bearer {api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": "Qwen/Qwen2.5-7B-Instruct",
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"messages": [{"role": "user", "content": "hi"}],
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"max_tokens": 1
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}
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try:
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logging.info("Cherry Studio方式：直接HTTP请求测试")
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response = requests.post(test_url, headers=headers, json=payload, timeout=15)
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logging.info(f"HTTP响应状态码: {response.status_code}")
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if response.status_code == 200:
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logging.info("硅基流动连接测试成功（Cherry Studio方式）")
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result = response.json()
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logging.info(f"响应: {json.dumps(result, ensure_ascii=False)[:100]}...")
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return True
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else:
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logging.error(f"HTTP请求失败: {response.status_code} - {response.text}")
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# 尝试不同的端点
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if response.status_code == 404 and "/v1" in test_url:
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# 尝试去掉/v1
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alt_url = test_url.replace("/v1", "")
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logging.info(f"尝试备用URL: {alt_url}")
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alt_response = requests.post(alt_url, headers=headers, json=payload, timeout=15)
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if alt_response.status_code == 200:
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logging.info("硅基流动连接测试成功（备用URL）")
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return True
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except Exception as http_error:
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logging.error(f"直接HTTP请求异常: {http_error}")
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# 标准OpenAI SDK方式作为备选
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# 对于硅基流动，需要使用正确的base_url
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if "siliconflow" in base_url.lower():
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# 确保SDK使用正确的base_url（需要包含/v1）
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sdk_base_url = api_base if api_base.endswith('/v1') else f"{api_base}/v1"
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client = OpenAI(api_key=api_key, base_url=sdk_base_url)
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logging.info(f"尝试OpenAI SDK方式，使用base_url: {sdk_base_url}")
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else:
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client = OpenAI(api_key=api_key, base_url=base_url)
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if "siliconflow" in base_url.lower():
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# 硅基流动的免费模型列表
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test_models = [
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"Qwen/Qwen2.5-7B-Instruct",
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"THUDM/glm-4-9b-chat",
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"deepseek-ai/DeepSeek-V3"
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]
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for model in test_models:
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try:
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logging.info(f"尝试测试模型: {model}")
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response = client.chat.completions.create(
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model=model,
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messages=[{"role": "user", "content": "hi"}],
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max_tokens=1,
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timeout=15.0
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)
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logging.info(f"硅基流动连接测试成功，使用模型: {model}")
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return True
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except Exception as model_error:
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error_msg = str(model_error)
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logging.warning(f"模型 {model} 测试失败: {error_msg}")
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if "401" in error_msg or "Unauthorized" in error_msg or "Api key is invalid" in error_msg:
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raise Exception("API Key 无效或已过期，请检查API Key是否正确")
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continue
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# 尝试models接口
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try:
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models = client.models.list()
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logging.info("硅基流动连接测试成功（通过models接口）")
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return True
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except Exception as models_error:
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logging.error(f"models接口失败: {models_error}")
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raise models_error
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else:
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# 非硅基流动提供商
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model = client.models.list()
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logging.info("连通性测试成功")
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return True
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except Exception as e:
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logging.info(f"连通性测试失败：{e}")
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error_msg = str(e)
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logging.error(f"连通性测试失败：{error_msg}")
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# 根据错误类型提供更具体的错误信息
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if "401" in error_msg or "Unauthorized" in error_msg or "Api key is invalid" in error_msg:
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raise Exception("API Key 无效或已过期，请检查API Key是否正确")
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elif "404" in error_msg or "Not Found" in error_msg:
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if "siliconflow" in base_url.lower():
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raise Exception("API 地址可能不正确。建议尝试: https://api.siliconflow.cn/v1 或 https://api.siliconflow.cn（参考Cherry Studio配置）")
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else:
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raise Exception("API 地址不正确，请检查 base_url 格式")
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elif "timeout" in error_msg.lower():
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raise Exception("连接超时，请检查网络连接或 API 地址是否正确")
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elif "ssl" in error_msg.lower() or "certificate" in error_msg.lower():
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raise Exception("SSL 证书验证失败，请检查 API 地址是否使用 HTTPS")
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elif "connection" in error_msg.lower():
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if "siliconflow" in base_url.lower():
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raise Exception("无法连接到硅基流动服务器，请尝试: https://api.siliconflow.cn/v1 或 https://api.siliconflow.cn")
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else:
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raise Exception("无法连接到服务器，请检查 API 地址和网络连接")
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elif "_set_private_attributes" in error_msg:
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raise Exception("OpenAI SDK版本兼容性问题，请尝试重新配置或联系管理员")
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else:
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raise Exception(f"连接失败（原始错误）: {error_msg}")
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# print(f"Error connecting to OpenAI API: {e}")
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return False
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214
backend/app/gpt/provider/SiliconFlow_provider.py
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214
backend/app/gpt/provider/SiliconFlow_provider.py
Normal file
@@ -0,0 +1,214 @@
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from typing import Optional, Union, List
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from openai import OpenAI
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from app.utils.logger import get_logger
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logger = get_logger(__name__)
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class SiliconFlowProvider:
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"""
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专门为硅基流动(SiliconFlow)优化的提供商类
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基于市面上成熟的接入方案设计
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"""
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# 硅基流动支持的常用模型列表
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SUPPORTED_MODELS = [
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"deepseek-ai/DeepSeek-V3",
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"deepseek-ai/DeepSeek-R1",
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"Qwen/Qwen2.5-72B-Instruct",
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"Qwen/Qwen2.5-32B-Instruct",
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"Qwen/Qwen2.5-14B-Instruct",
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"Qwen/Qwen2.5-7B-Instruct",
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"meta-llama/Llama-3.1-70B-Instruct",
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"meta-llama/Llama-3.1-8B-Instruct",
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"THUDM/glm-4-9b-chat",
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"01-ai/Yi-1.5-34B-Chat"
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]
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# 硅基流动API端点
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API_ENDPOINTS = [
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"https://api.siliconflow.cn/v1", # 国内用户
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"https://api-st.siliconflow.cn/v1" # 海外用户
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]
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def __init__(self, api_key: str, base_url: str = None, model: Union[str, None] = None):
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"""
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初始化硅基流动提供商
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Args:
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api_key: API密钥
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base_url: API基础URL，默认使用国内端点
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model: 模型名称
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"""
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self.api_key = api_key
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# 标准化base_url，确保符合硅基流动API要求
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if base_url:
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base_url_clean = base_url.rstrip('/')
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# 确保使用正确的API端点格式
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if not base_url_clean.endswith('/v1'):
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if base_url_clean.endswith('/chat/completions'):
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# 用户输入了完整的endpoint，提取base部分并添加/v1
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base_url_clean = base_url_clean.replace('/chat/completions', '/v1')
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elif 'siliconflow' in base_url_clean.lower():
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# 硅基流动需要/v1后缀
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base_url_clean = f"{base_url_clean}/v1"
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self.base_url = base_url_clean
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else:
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self.base_url = self.API_ENDPOINTS[0]
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self.model = model
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logger.info(f"硅基流动提供商初始化 - 使用base_url: {self.base_url}")
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self.client = OpenAI(api_key=api_key, base_url=self.base_url)
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@property
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def get_client(self):
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return self.client
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@classmethod
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def test_connection(cls, api_key: str, base_url: str = None) -> bool:
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"""
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测试硅基流动连接
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使用成熟的chat接口测试方法，而非models接口
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Args:
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api_key: API密钥
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base_url: API基础URL
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Returns:
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bool: 连接是否成功
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"""
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base_url = base_url or cls.API_ENDPOINTS[0]
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try:
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logger.info(f"测试硅基流动连接 - API Key: {api_key[:8]}...（已截断） Base URL: {base_url}")
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client = OpenAI(api_key=api_key, base_url=base_url)
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# 使用轻量级模型进行连接测试
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test_models = [
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"Qwen/Qwen2.5-7B-Instruct", # 免费模型优先
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"deepseek-ai/DeepSeek-V3",
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"THUDM/glm-4-9b-chat"
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]
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for model in test_models:
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try:
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logger.info(f"尝试测试模型: {model}")
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# 发送简单的chat请求测试连接
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response = client.chat.completions.create(
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model=model,
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messages=[
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{"role": "user", "content": "hi"}
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],
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max_tokens=1,
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timeout=15.0
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)
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logger.info(f"硅基流动连接测试成功 - 模型: {model}")
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return True
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except Exception as model_error:
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error_msg = str(model_error)
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logger.warning(f"模型 {model} 测试失败: {error_msg}")
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# 如果是401错误（API Key问题），不继续尝试其他模型
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if "401" in error_msg or "Unauthorized" in error_msg or "Api key is invalid" in error_msg:
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raise Exception("API Key 无效或已过期，请检查API Key是否正确")
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continue
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# 如果所有模型都失败，尝试models接口作为最后手段
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logger.info("所有模型测试失败，尝试models接口")
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try:
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models = client.models.list()
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logger.info("硅基流动连接测试成功（通过models接口）")
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return True
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except Exception as models_error:
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logger.error(f"models接口也失败: {models_error}")
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raise models_error
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except Exception as e:
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error_msg = str(e)
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logger.error(f"硅基流动连接测试失败：{error_msg}")
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# 根据错误类型提供具体的错误信息
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if "401" in error_msg or "Unauthorized" in error_msg or "Api key is invalid" in error_msg:
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raise Exception("API Key 无效或已过期，请检查API Key是否正确")
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elif "404" in error_msg or "Not Found" in error_msg:
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raise Exception(f"API地址不正确，请检查URL格式。推荐使用: {cls.API_ENDPOINTS[0]} 或 {cls.API_ENDPOINTS[1]}")
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elif "timeout" in error_msg.lower():
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raise Exception("连接超时，请检查网络连接或尝试海外端点")
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elif "connection" in error_msg.lower():
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raise Exception(f"无法连接到硅基流动服务器，请尝试: {cls.API_ENDPOINTS[1]}")
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else:
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raise Exception(f"连接失败: {error_msg}")
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def list_models(self):
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"""
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获取可用模型列表
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优先返回预定义的模型列表，如果API支持则获取实时列表
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"""
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try:
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# 尝试获取实时模型列表
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models = self.client.models.list()
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logger.info("成功获取硅基流动实时模型列表")
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return models
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except Exception as e:
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logger.warning(f"无法获取实时模型列表，返回预定义列表: {e}")
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# 返回预定义的模型列表
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from types import SimpleNamespace
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model_objects = []
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for model_name in self.SUPPORTED_MODELS:
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model_obj = SimpleNamespace()
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model_obj.id = model_name
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model_obj.object = "model"
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model_obj.created = 1640995200 # 固定时间戳
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model_obj.owned_by = "siliconflow"
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# 添加dict方法
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def dict_method():
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return {
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"id": model_name,
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"object": "model",
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"created": 1640995200,
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"owned_by": "siliconflow"
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}
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model_obj.dict = dict_method
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model_objects.append(model_obj)
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# 构造兼容的返回对象
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result = SimpleNamespace()
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result.data = model_objects
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return result
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def create_chat_completion(self, model: str, messages: list, **kwargs):
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"""
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创建聊天完成请求
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"""
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return self.client.chat.completions.create(
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model=model,
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messages=messages,
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**kwargs
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)
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@classmethod
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def get_recommended_config(cls) -> dict:
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"""
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获取推荐的硅基流动配置
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"""
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return {
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"name": "硅基流动",
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"type": "custom",
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"base_url": cls.API_ENDPOINTS[0],
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"logo": "SiliconFlow",
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"supported_models": cls.SUPPORTED_MODELS,
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"description": "硅基流动 - 免费高性能AI模型服务",
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"features": [
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"完全兼容OpenAI API",
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"支持多种开源大模型",
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"部分模型永久免费",
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"国内外双端点支持"
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]
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}
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