[Feature]integrate LazyLLM framework

backend/app/core/config.py

@@ -64,6 +64,10 @@ def assemble_db_connection(cls, v: str | None, values: dict[str, any]) -> str:
     MINIMAX_API_KEY: Optional[str] = None
     DOUBAO_API_KEY: Optional[str] = None
     OLLAMA_BASE_URL: Optional[str] = "http://localhost:11434/v1"
+    SILICONFLOW_API_KEY: Optional[str] = None
+    SENSENOVA_API_KEY: Optional[str] = None
+    AIPING_API_KEY: Optional[str] = None
+    PPIO_API_KEY: Optional[str] = None
 
     # GitHub配置
     GITHUB_TOKEN: Optional[str] = None

backend/app/services/llm/adapters/__init__.py

@@ -13,10 +13,12 @@
 from .baidu_adapter import BaiduAdapter
 from .minimax_adapter import MinimaxAdapter
 from .doubao_adapter import DoubaoAdapter
+from .lazyllm_adapter import LazyLLMAdapter
 
 __all__ = [
     "LiteLLMAdapter",
     "BaiduAdapter",
     "MinimaxAdapter",
     "DoubaoAdapter",
+    "LazyLLMAdapter",
 ]

backend/app/services/llm/adapters/lazyllm_adapter.py

@@ -0,0 +1,236 @@
+"""
+LazyLLM 适配器
+通过 LazyLLM 的 OnlineChatModule 提供统一的大模型接入服务
+
+支持的提供商:
+- 通过 LazyLLM 支持的所有在线模型提供商
+- 包括: OpenAI, GLM, Qwen, Kimi, SenseNova, DeepSeek, Doubao, Siliconflow, PPIO, AIPing 等
+"""
+
+import os
+import logging
+import lazyllm
+import asyncio
+from ..base_adapter import BaseLLMAdapter
+from ..types import (
+    LLMConfig,
+    LLMRequest,
+    LLMResponse,
+    LLMProvider,
+    LLMError,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class LazyLLMAdapter(BaseLLMAdapter):
+    """LazyLLM 适配器
+
+    利用 LazyLLM 的 OnlineChatModule 提供统一的大模型接口
+    支持多种在线 LLM 提供商的无缝接入
+    """
+
+    # DeepAudit Provider 到 LazyLLM source 的映射
+    PROVIDER_SOURCE_MAP = {
+        LLMProvider.OPENAI: "openai",
+        LLMProvider.GEMINI: "gemini",
+        LLMProvider.QWEN: "qwen",
+        LLMProvider.DEEPSEEK: "deepseek",
+        LLMProvider.ZHIPU: "glm",
+        LLMProvider.MOONSHOT: "kimi",
+        LLMProvider.DOUBAO: "doubao",
+        LLMProvider.SILICONFLOW: "siliconflow",
+        LLMProvider.MINIMAX: "minimax",
+        LLMProvider.SENSENOVA: "sensenova",
+        LLMProvider.PPIO: "ppio",
+        LLMProvider.AIPING: "aiping",
+    }
+
+    # API_KEY的格式转换
+    PROVIDER_ENV_MAP = {
+        LLMProvider.OPENAI: "OPENAI_API_KEY",
+        LLMProvider.GEMINI: "GEMINI_API_KEY",
+        LLMProvider.QWEN: "QWEN_API_KEY",
+        LLMProvider.DEEPSEEK: "DEEPSEEK_API_KEY",
+        LLMProvider.ZHIPU: "ZHIPU_API_KEY",
+        LLMProvider.MOONSHOT: "MOONSHOT_API_KEY",
+        LLMProvider.DOUBAO: "DOUBAO_API_KEY",
+        LLMProvider.SILICONFLOW: "SILICONFLOW_API_KEY",
+        LLMProvider.MINIMAX: "MINIMAX_API_KEY",
+        LLMProvider.SENSENOVA: "SENSENOVA_API_KEY",
+        LLMProvider.PPIO: "PPIO_API_KEY",
+        LLMProvider.AIPING: "AIPING_API_KEY",
+    }
+
+    def __init__(self, config: LLMConfig):
+        super().__init__(config)
+        self._lazyllm_module = None
+        self._source = self._get_lazyllm_source()
+
+    def _get_lazyllm_source(self) -> str:
+        """获取 LazyLLM 的 source 名称"""
+        source = self.PROVIDER_SOURCE_MAP.get(self.config.provider)
+        if not source:
+            raise LLMError(
+                f"LazyLLM 不支持提供商: {self.config.provider}",
+                self.config.provider
+            )
+        return source
+
+    def _setup_environment(self):
+        """设置 LazyLLM 所需的环境变量"""
+        env_key = f"LAZYLLM_{self._source.upper()}_API_KEY"
+        provider_env_key = self.PROVIDER_ENV_MAP.get(self.config.provider)
+        candidate_key = os.getenv(provider_env_key) if provider_env_key else None
+
+        if not candidate_key:
+            candidate_key = self.config.api_key
+
+        if candidate_key:
+            os.environ[env_key] = candidate_key
+
+        # SenseNova 额外 secret_key
+        if self.config.provider == LLMProvider.SENSENOVA:
+            headers = self.config.custom_headers or {}
+            secret_key = headers.get("secret_key") or os.getenv("SENSENOVA_SECRET_KEY")
+            if secret_key:
+                os.environ["LAZYLLM_SENSENOVA_SECRET_KEY"] = secret_key
+
+    def _get_lazyllm_module(self):
+        """创建 LazyLLM OnlineChatModule 实例"""
+        if self._lazyllm_module is None:
+            try:
+                self._setup_environment()
+                # 创建 OnlineChatModule
+                kwargs = {
+                    "source": self._source,
+                    "stream": False,  # 非流式输出
+                }
+
+                if self.config.base_url:
+                    kwargs["base_url"] = self.config.base_url
+
+                if self.config.model:
+                    kwargs["model"] = self.config.model
+                self._lazyllm_module = lazyllm.OnlineChatModule(**kwargs)
+                logger.info(f"LazyLLM 模块初始化成功: source={self._source}, model={self.config.model}")
+
+            except Exception as e:
+                raise LLMError(
+                    f"LazyLLM 模块初始化失败: {str(e)}",
+                    self.config.provider,
+                    original_error=e
+                )
+
+        return self._lazyllm_module
+
+    async def complete(self, request: LLMRequest) -> LLMResponse:
+        """执行 LLM 推理"""
+        try:
+            await self.validate_config()
+            return await self.retry(lambda: self._send_request(request))
+        except Exception as error:
+            api_response = getattr(error, 'api_response', None)
+            self.handle_error(error, "LazyLLM API 调用失败", api_response=api_response)
+
+    async def _send_request(self, request: LLMRequest) -> LLMResponse:
+        """发送请求到 LazyLLM"""
+
+        module = self._get_lazyllm_module()
+
+        # 构建消息历史
+        # LazyLLM 期望的格式: 最后一条消息是 input，之前的是 history
+        messages = request.messages
+        if not messages:
+            raise LLMError("消息列表为空", self.config.provider)
+
+        # 分离最后一条用户消息和历史
+        user_input = messages[-1].content if messages[-1].role == "user" else ""
+
+        # 构建历史记录 (格式: [[user, assistant], [user, assistant], ...])
+        history = []
+        i = 0
+        while i < len(messages) - 1:
+            if messages[i].role == "user" and i + 1 < len(messages) and messages[i + 1].role == "assistant":
+                history.append([messages[i].content, messages[i + 1].content])
+                i += 2
+            else:
+                i += 1
+
+        # 准备调用参数
+        call_kwargs = {}
+
+        # 添加运行时参数（覆盖静态配置）
+        if request.temperature is not None:
+            call_kwargs["temperature"] = request.temperature
+        elif self.config.temperature is not None:
+            call_kwargs["temperature"] = self.config.temperature
+
+        if request.max_tokens is not None:
+            call_kwargs["max_tokens"] = request.max_tokens
+        elif self.config.max_tokens is not None:
+            call_kwargs["max_tokens"] = self.config.max_tokens
+
+        if request.top_p is not None:
+            call_kwargs["top_p"] = request.top_p
+        elif self.config.top_p is not None:
+            call_kwargs["top_p"] = self.config.top_p
+
+        # LazyLLM 的同步调用需要在 executor 中运行
+        def _call_lazyllm():
+            try:
+                result = module(
+                    user_input,
+                    llm_chat_history=history if history else None,
+                    **call_kwargs
+                )
+                return result
+            except Exception as e:
+                logger.error(f"LazyLLM 调用失败: {e}")
+                raise
+
+        # 在线程池中执行同步调用
+        loop = asyncio.get_running_loop()
+        result = await loop.run_in_executor(None, _call_lazyllm)
+
+        # 解析响应
+        if isinstance(result, str):
+            content = result
+        elif isinstance(result, dict):
+            content = result.get("content", str(result))
+        else:
+            content = str(result)
+
+        # 返回基本响应（不包含 usage 信息）
+        return LLMResponse(
+            content=content,
+            model=self.config.model,
+            usage=None,  # LazyLLM 的 usage 记录在其内部系统中
+            finish_reason="stop"
+        )
+
+    async def validate_config(self) -> bool:
+        """验证配置是否有效"""
+        await super().validate_config()
+
+        # 检查 LazyLLM 是否支持该提供商
+        if self.config.provider not in self.PROVIDER_SOURCE_MAP:
+            raise LLMError(
+                f"LazyLLM 适配器不支持提供商: {self.config.provider}",
+                self.config.provider
+            )
+
+        return True
+
+    @classmethod
+    def supports_provider(cls, provider: LLMProvider) -> bool:
+        """检查是否支持指定的提供商"""
+        return provider in cls.PROVIDER_SOURCE_MAP
+
+    async def close(self):
+        """关闭适配器"""
+        await super().close()
+        # LazyLLM 模块的清理（如果需要）
+        self._lazyllm_module = None
+
+

backend/app/services/llm/factory.py

@@ -13,6 +13,7 @@
     BaiduAdapter,
     MinimaxAdapter,
     DoubaoAdapter,
+    LazyLLMAdapter,
 )
 
 
@@ -48,7 +49,14 @@ def create_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
 
     @classmethod
     def _instantiate_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
-        """根据提供商类型实例化适配器"""
+        """根据提供商类型实例化适配器
+
+        优先级策略:
+        1. 必须使用原生适配器的提供商（API 格式特殊）
+        2. LiteLLM 支持的提供商
+        3. LazyLLM 支持的提供商
+        4. 不支持的提供商
+        """
         # 如果未指定模型，使用默认模型
         if not config.model:
             config.model = DEFAULT_MODELS.get(config.provider, "gpt-4o-mini")
@@ -57,9 +65,13 @@ def _instantiate_adapter(cls, config: LLMConfig) -> BaseLLMAdapter:
         if config.provider in NATIVE_ONLY_PROVIDERS:
             return cls._create_native_adapter(config)
 
-        # 其他提供商使用 LiteLLM
+        # 其他提供商优先使用 LiteLLM
         if LiteLLMAdapter.supports_provider(config.provider):
             return LiteLLMAdapter(config)
+
+        # 使用 LazyLLM 适配器
+        if LazyLLMAdapter.supports_provider(config.provider):
+            return LazyLLMAdapter(config)
 
         # 不支持的提供商
         raise ValueError(f"不支持的LLM提供商: {config.provider}")

backend/app/services/llm/service.py

@@ -130,6 +130,10 @@ def _get_provider_api_key_from_user_config(self, provider: LLMProvider, user_llm
             LLMProvider.BAIDU: 'baiduApiKey',
             LLMProvider.MINIMAX: 'minimaxApiKey',
             LLMProvider.DOUBAO: 'doubaoApiKey',
+            LLMProvider.SILICONFLOW: 'siliconflowApiKey',
+            LLMProvider.SENSENOVA: 'sensenovaApiKey',
+            LLMProvider.AIPING: 'aipingApiKey',
+            LLMProvider.PPIO: 'ppioApiKey',
         }
         key_name = provider_key_map.get(provider)
         if key_name:
@@ -149,6 +153,10 @@ def _get_provider_api_key(self, provider: LLMProvider) -> str:
             LLMProvider.BAIDU: 'BAIDU_API_KEY',
             LLMProvider.MINIMAX: 'MINIMAX_API_KEY',
             LLMProvider.DOUBAO: 'DOUBAO_API_KEY',
+            LLMProvider.SILICONFLOW: 'SILICONFLOW_API_KEY',
+            LLMProvider.SENSENOVA: 'SENSENOVA_API_KEY',
+            LLMProvider.AIPING: 'AIPING_API_KEY',
+            LLMProvider.PPIO: 'PPIO_API_KEY',
             LLMProvider.OLLAMA: None,  # Ollama 不需要 API Key
         }
         key_name = provider_key_map.get(provider)
@@ -178,6 +186,10 @@ def _parse_provider(self, provider_str: str) -> LLMProvider:
             'minimax': LLMProvider.MINIMAX,
             'doubao': LLMProvider.DOUBAO,
             'ollama': LLMProvider.OLLAMA,
+            'siliconflow': LLMProvider.SILICONFLOW,
+            'sensenova': LLMProvider.SENSENOVA,
+            'aiping': LLMProvider.AIPING,
+            'ppio': LLMProvider.PPIO,
         }
         return provider_map.get(provider_str.lower(), LLMProvider.OPENAI)
 
@@ -387,6 +399,7 @@ async def analyze_code(self, code: str, language: str) -> Dict[str, Any]:
             )
 
             response = await adapter.complete(request)
+
             content = response.content
 
             # 记录 LLM 原始响应（用于调试）

backend/app/services/llm/types.py

@@ -20,6 +20,10 @@ class LLMProvider(str, Enum):
     MINIMAX = "minimax"      # MiniMax
     DOUBAO = "doubao"        # 字节豆包
     OLLAMA = "ollama"        # Ollama 本地大模型
+    SILICONFLOW = "siliconflow"  # SiliconFlow
+    SENSENOVA = "sensenova"  # SenseNova
+    AIPING = "aiping"        # AI PING
+    PPIO = "ppio"            # PPIO AI
 
 
 @dataclass
@@ -102,6 +106,10 @@ def __init__(
     LLMProvider.MINIMAX: "minimax-m2",
     LLMProvider.DOUBAO: "doubao-1.6-pro",
     LLMProvider.OLLAMA: "llama3.3-70b",
+    LLMProvider.SILICONFLOW: "deepseek-ai/DeepSeek-V3.2",
+    LLMProvider.SENSENOVA: "SenseChat-5",
+    LLMProvider.AIPING: "DeepSeek-V3.2",
+    LLMProvider.PPIO: "deepseek/deepseek-v3.2",
 }
 
 
@@ -118,6 +126,10 @@ def __init__(
     LLMProvider.OLLAMA: "http://localhost:11434/v1",
     LLMProvider.GEMINI: "https://generativelanguage.googleapis.com/v1beta",
     LLMProvider.CLAUDE: "https://api.anthropic.com/v1",
+    LLMProvider.SENSENOVA: "https://api.sensenova.com/v1",
+    LLMProvider.SILICONFLOW: "https://api.siliconflow.cn/v1",
+    LLMProvider.AIPING: "https://api.aiping.cloud/v1",
+    LLMProvider.PPIO: "https://api.ppio.ai/v1",
 }
 
 

backend/requirements.txt

@@ -27,6 +27,7 @@ httpx>=0.25.0
 # ============ LLM Integration ============
 litellm>=1.0.0
 tiktoken>=0.5.2
+lazyllm>=0.7.3
 
 # ============ Report Generation ============
 reportlab>=4.0.0