{"repo": "allen-xxa/comfynexus", "files": {"source-code/backend/src/core/ai/providers/factory.py": {"632eb35e73cd32d57b9708f8e48b9efb884f2f7e": {"t": 1785674273.5365443, "n": 4728, "h": "3303ff30a4139200", "c": "\"\"\"\nProviderFactory 工厂类\n\n负责根据配置创建对应的 AI Provider 实例。\n使用工厂模式实现 Provider 的动态创建和注册。\n\"\"\"\n\nfrom typing import Dict, Any, Type\nfrom .base_provider import BaseProvider\nfrom .openai_provider import OpenAIProvider\nfrom .spark_provider import SparkProvider\nfrom .volcengine_provider import VolcengineProvider\nfrom .zhipu_provider import ZhipuProvider\nfrom .ollama_provider import OllamaProvider\n\n\nclass ProviderFactory:\n    \"\"\"AI Provider 工厂类\n    \n    负责创建和管理不同类型的 AI Provider 实例。\n    支持动态注册新的 Provider 类型。\n    \"\"\"\n    \n    # Provider 注册表：provider_name -> Provider 类\n    _providers: Dict[str, Type[BaseProvider]] = {\n        'openai': OpenAIProvider,\n        'xflow': OpenAIProvider,  # XFlow 兼容 OpenAI API\n        'iflow': OpenAIProvider,  # iFlow 兼容 OpenAI API\n        'custom-openai': OpenAIProvider,  # 通用 OpenAI API\n        'spark': SparkProvider,\n        'volcengine': VolcengineProvider,\n        'zhipu': ZhipuProvider,\n        'ollama': OllamaProvider,\n    }\n    \n    # Provider 默认 Base URL 配置\n    _default_base_urls: Dict[str, str] = {\n        'openai': 'https://api.openai.com/v1',\n        'xflow': 'https://api.xflow.cc/v1',\n        'iflow': 'https://apis.iflow.cn/v1',\n        'volcengine': 'https://ark.cn-beijing.volces.com/api/v3',\n        'zhipu': 'https://open.bigmodel.cn/api/paas/v4',\n        'ollama': 'http://localhost:11434',\n    }\n    \n    @classmethod\n    def create(cls, provider_name: str, config: Dict[str, Any], use_proxy: bool = False) -> BaseProvider:\n        \"\"\"创建 Provider 实例\n        \n        Args:\n            provider_name: Provider 名称（openai/spark/volcengine/zhipu/ollama）\n            config: Provider 配置字典\n                {\n                    \"api_key\": str,\n                    \"base_url\": str,  # 可选\n                    \"model\": str,\n                    \"temperature\": float,\n                    \"max_tokens\": int,\n                    ...\n                }\n            use_proxy: 是否使用系统代理（默认 False）\n        \n        Returns:\n            BaseProvider: Provider 实例\n        \n        Raises:\n            ValueError: 当 provider_name 不存在时抛出\n        \n        Example:\n            config = {\n                \"api_key\": \"sk-xxx\",\n                \"model\": \"gpt-4o\",\n                \"temperature\": 0.7\n            }\n            provider = ProviderFactory.create(\"openai\", config, use_proxy=True)\n        \"\"\"\n        # 检查 provider_name 是否有效\n        if provider_name not in cls._providers:\n            available = ', '.join(cls._providers.keys())\n            raise ValueError(\n                f\"未知的 Provider: '{provider_name}'. \"\n                f\"可用的 Provider: {available}\"\n            )\n        \n        # 复制配置，避免修改原始配置\n        config = config.copy()\n        \n        # 如果 base_url 为空且该 provider 有默认 URL，则设置默认值\n        if not config.get('base_url') and provider_name in cls._default_base_urls:\n            config['base_url'] = cls._default_base_urls[provider_name]\n        \n        # 获取 Provider 类\n        provider_class = cls._providers[provider_name]\n        \n        # 创建并返回实例（传递 use_proxy 参数）\n        return provider_class(config, use_proxy=use_proxy)\n    \n    @classmethod\n    def register(cls, provider_name: str, provider_class: Type[BaseProvider]) -> None:\n        \"\"\"注册新的 Provider 类型\n        \n        Args:\n            provider_name: Provider 名称\n            provider_class: Provider 类（必须继承自 BaseProvider）\n        \n        Raises:\n            TypeError: 当 provider_class 不是 BaseProvider 的子类时抛出\n        \n        Example:\n            class CustomProvider(BaseProvider):\n                ...\n            \n            ProviderFactory.register(\"custom\", CustomProvider)\n        \"\"\"\n        # 验证 provider_class 是 BaseProvider 的子类\n        if not issubclass(provider_class, BaseProvider):\n            raise TypeError(\n                f\"Provider 类必须继承自 BaseProvider, \"\n                f\"但得到: {provider_class.__name__}\"\n            )\n        \n        # 注册到注册表\n        cls._providers[provider_name] = provider_class\n    \n    @classmethod\n    def get_available_providers(cls) -> list[str]:\n        \"\"\"获取所有已注册的 Provider 名称列表\n        \n        Returns:\n            Provider 名称列表\n        \n        Example:\n            providers = ProviderFactory.get_available_providers()\n            # ['openai', 'spark', 'volcengine', ...]\n        \"\"\"\n        return list(cls._providers.keys())\n    \n    @classmethod\n    def is_provider_available(cls, provider_name: str) -> bool:\n        \"\"\"检查指定的 Provider 是否可用\n        \n        Args:\n            provider_name: Provider 名称\n        \n        Returns:\n            是否可用\n        \n        Example:\n            if ProviderFactory.is_provider_available(\"openai\"):\n                provider = ProviderFactory.create(\"openai\", config)\n        \"\"\"\n        return provider_name in cls._providers\n"}, "98c7f1ce95484824f2c8d92ea2e4b94403ea2bf0": {"t": 1785846887.4427633, "n": 4728, "h": "3303ff30a4139200", "c": "\"\"\"\nProviderFactory 工厂类\n\n负责根据配置创建对应的 AI Provider 实例。\n使用工厂模式实现 Provider 的动态创建和注册。\n\"\"\"\n\nfrom typing import Dict, Any, Type\nfrom .base_provider import BaseProvider\nfrom .openai_provider import OpenAIProvider\nfrom .spark_provider import SparkProvider\nfrom .volcengine_provider import VolcengineProvider\nfrom .zhipu_provider import ZhipuProvider\nfrom .ollama_provider import OllamaProvider\n\n\nclass ProviderFactory:\n    \"\"\"AI Provider 工厂类\n    \n    负责创建和管理不同类型的 AI Provider 实例。\n    支持动态注册新的 Provider 类型。\n    \"\"\"\n    \n    # Provider 注册表：provider_name -> Provider 类\n    _providers: Dict[str, Type[BaseProvider]] = {\n        'openai': OpenAIProvider,\n        'xflow': OpenAIProvider,  # XFlow 兼容 OpenAI API\n        'iflow': OpenAIProvider,  # iFlow 兼容 OpenAI API\n        'custom-openai': OpenAIProvider,  # 通用 OpenAI API\n        'spark': SparkProvider,\n        'volcengine': VolcengineProvider,\n        'zhipu': ZhipuProvider,\n        'ollama': OllamaProvider,\n    }\n    \n    # Provider 默认 Base URL 配置\n    _default_base_urls: Dict[str, str] = {\n        'openai': 'https://api.openai.com/v1',\n        'xflow': 'https://api.xflow.cc/v1',\n        'iflow': 'https://apis.iflow.cn/v1',\n        'volcengine': 'https://ark.cn-beijing.volces.com/api/v3',\n        'zhipu': 'https://open.bigmodel.cn/api/paas/v4',\n        'ollama': 'http://localhost:11434',\n    }\n    \n    @classmethod\n    def create(cls, provider_name: str, config: Dict[str, Any], use_proxy: bool = False) -> BaseProvider:\n        \"\"\"创建 Provider 实例\n        \n        Args:\n            provider_name: Provider 名称（openai/spark/volcengine/zhipu/ollama）\n            config: Provider 配置字典\n                {\n                    \"api_key\": str,\n                    \"base_url\": str,  # 可选\n                    \"model\": str,\n                    \"temperature\": float,\n                    \"max_tokens\": int,\n                    ...\n                }\n            use_proxy: 是否使用系统代理（默认 False）\n        \n        Returns:\n            BaseProvider: Provider 实例\n        \n        Raises:\n            ValueError: 当 provider_name 不存在时抛出\n        \n        Example:\n            config = {\n                \"api_key\": \"sk-xxx\",\n                \"model\": \"gpt-4o\",\n                \"temperature\": 0.7\n            }\n            provider = ProviderFactory.create(\"openai\", config, use_proxy=True)\n        \"\"\"\n        # 检查 provider_name 是否有效\n        if provider_name not in cls._providers:\n            available = ', '.join(cls._providers.keys())\n            raise ValueError(\n                f\"未知的 Provider: '{provider_name}'. \"\n                f\"可用的 Provider: {available}\"\n            )\n        \n        # 复制配置，避免修改原始配置\n        config = config.copy()\n        \n        # 如果 base_url 为空且该 provider 有默认 URL，则设置默认值\n        if not config.get('base_url') and provider_name in cls._default_base_urls:\n            config['base_url'] = cls._default_base_urls[provider_name]\n        \n        # 获取 Provider 类\n        provider_class = cls._providers[provider_name]\n        \n        # 创建并返回实例（传递 use_proxy 参数）\n        return provider_class(config, use_proxy=use_proxy)\n    \n    @classmethod\n    def register(cls, provider_name: str, provider_class: Type[BaseProvider]) -> None:\n        \"\"\"注册新的 Provider 类型\n        \n        Args:\n            provider_name: Provider 名称\n            provider_class: Provider 类（必须继承自 BaseProvider）\n        \n        Raises:\n            TypeError: 当 provider_class 不是 BaseProvider 的子类时抛出\n        \n        Example:\n            class CustomProvider(BaseProvider):\n                ...\n            \n            ProviderFactory.register(\"custom\", CustomProvider)\n        \"\"\"\n        # 验证 provider_class 是 BaseProvider 的子类\n        if not issubclass(provider_class, BaseProvider):\n            raise TypeError(\n                f\"Provider 类必须继承自 BaseProvider, \"\n                f\"但得到: {provider_class.__name__}\"\n            )\n        \n        # 注册到注册表\n        cls._providers[provider_name] = provider_class\n    \n    @classmethod\n    def get_available_providers(cls) -> list[str]:\n        \"\"\"获取所有已注册的 Provider 名称列表\n        \n        Returns:\n            Provider 名称列表\n        \n        Example:\n            providers = ProviderFactory.get_available_providers()\n            # ['openai', 'spark', 'volcengine', ...]\n        \"\"\"\n        return list(cls._providers.keys())\n    \n    @classmethod\n    def is_provider_available(cls, provider_name: str) -> bool:\n        \"\"\"检查指定的 Provider 是否可用\n        \n        Args:\n            provider_name: Provider 名称\n        \n        Returns:\n            是否可用\n        \n        Example:\n            if ProviderFactory.is_provider_available(\"openai\"):\n                provider = ProviderFactory.create(\"openai\", config)\n        \"\"\"\n        return provider_name in cls._providers\n"}}, "source-code/backend/src/core/ai/providers/spark_provider.py": {"3decbadd64cf6ac14dbc3bd7c48bd14042aaac05": {"t": 1785809854.275647, "n": 11701, "h": "29d4e02bc0120df6", "c": "\"\"\"\nSparkProvider 实现（讯飞星火）\n\n提供讯飞星火 API 的流式聊天功能，支持：\n- WebSocket 流式响应\n- 连接测试\n- 模型列表获取\n- 错误处理和超时控制\n\n讯飞星火 API 文档：https://www.xfyun.cn/doc/spark/Web.html\n\"\"\"\n\nimport asyncio\nimport time\nimport json\nimport hmac\nimport hashlib\nimport base64\nfrom typing import AsyncIterator, Dict, Any, List, Tuple\nfrom urllib.parse import urlencode, urlparse\nfrom datetime import datetime, timezone\nimport logging\n\ntry:\n    import websockets\nexcept ImportError:\n    websockets = None\n\nfrom .base_provider import BaseProvider, StreamChunk\nfrom .network_utils import get_websocket_proxy\nfrom ._deep_thinking_prompt import DEEP_THINKING_PROMPT\n\nlogger = logging.getLogger(__name__)\n\n\nclass SparkProvider(BaseProvider):\n    \"\"\"讯飞星火 Provider 实现类\n    \n    讯飞星火使用 WebSocket 协议进行通信，需要特殊的鉴权方式。\n    \"\"\"\n    \n    # 模型版本对应的 API 地址\n    MODEL_URLS = {\n        'v1.5': 'wss://spark-api.xf-yun.com/v1.1/chat',\n        'v2.0': 'wss://spark-api.xf-yun.com/v2.1/chat',\n        'v3.0': 'wss://spark-api.xf-yun.com/v3.1/chat',\n        'v3.5': 'wss://spark-api.xf-yun.com/v3.5/chat',\n        'v4.0': 'wss://spark-api.xf-yun.com/v4.0/chat',\n    }\n    \n    # 模型版本对应的 domain\n    MODEL_DOMAINS = {\n        'v1.5': 'general',\n        'v2.0': 'generalv2',\n        'v3.0': 'generalv3',\n        'v3.5': 'generalv3.5',\n        'v4.0': 'generalv4',\n    }\n    \n    def __init__(self, config: Dict[str, Any], use_proxy: bool = False):\n        \"\"\"\n        初始化讯飞星火 Provider\n        \n        Args:\n            config: 配置字典\n                {\n                    \"app_id\": str,           # 应用 ID（必需）\n                    \"api_key\": str,          # API Key（必需）\n                    \"api_secret\": str,       # API Secret（必需）\n                    \"model\": str,            # 模型版本（必需，如 v3.5）\n                    \"temperature\": float,    # 温度参数（可选，默认 0.5）\n                    \"max_tokens\": int,       # 最大 Token 数（可选，默认 2048）\n                    \"timeout\": int,          # 超时时间（秒，可选，默认 30）\n                }\n            use_proxy: 是否使用系统代理\n        \"\"\"\n        super().__init__(config, use_proxy=use_proxy)\n        \n        # 检查 websockets 库是否可用\n        if websockets is None:\n            raise ImportError(\n                \"SparkProvider 需要 websockets 库。\"\n                \"请运行: pip install websockets\"\n            )\n        \n        self.app_id = config.get('app_id', '')\n        self.api_key = config.get('api_key', '')\n        self.api_secret = config.get('api_secret', '')\n        self.timeout = config.get('timeout', 300)  # 默认 5 分钟\n        \n        # 验证必需参数\n        if not self.app_id:\n            raise ValueError(\"讯飞星火 Provider 需要提供 app_id\")\n        if not self.api_key:\n            raise ValueError(\"讯飞星火 Provider 需要提供 api_key\")\n        if not self.api_secret:\n            raise ValueError(\"讯飞星火 Provider 需要提供 api_secret\")\n        \n        # 验证模型版本\n        model = self.get_model()\n        if model not in self.MODEL_URLS:\n            available = ', '.join(self.MODEL_URLS.keys())\n            raise ValueError(\n                f\"不支持的讯飞星火模型: '{model}'. \"\n                f\"可用模型: {available}\"\n            )\n    \n    def _generate_auth_url(self, base_url: str) -> str:\n        \"\"\"\n        生成带鉴权参数的 WebSocket URL\n        \n        讯飞星火使用 HMAC-SHA256 签名进行鉴权。\n        \n        Args:\n            base_url: 基础 WebSocket URL\n        \n        Returns:\n            带鉴权参数的完整 URL\n        \"\"\"\n        # 解析 URL\n        parsed = urlparse(base_url)\n        host = parsed.netloc\n        path = parsed.path\n        \n        # 生成 RFC1123 格式的时间戳\n        now = datetime.now(timezone.utc)\n        date = now.strftime('%a, %d %b %Y %H:%M:%S GMT')\n        \n        # 构建签名原文\n        signature_origin = f\"host: {host}\\ndate: {date}\\nGET {path} HTTP/1.1\"\n        \n        # 使用 HMAC-SHA256 生成签名\n        signature = hmac.new(\n            self.api_secret.encode('utf-8'),\n            signature_origin.encode('utf-8'),\n            hashlib.sha256\n        ).digest()\n        \n        # Base64 编码签名\n        signature_base64 = base64.b64encode(signature).decode('utf-8')\n        \n        # 构建 authorization 字符串\n        authorization_origin = (\n            f'api_key=\"{self.api_key}\", '\n            f'algorithm=\"hmac-sha256\", '\n            f'headers=\"host date request-line\", '\n            f'signature=\"{signature_base64}\"'\n        )\n        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode('utf-8')\n        \n        # 构建完整 URL\n        auth_params = {\n            'authorization': authorization,\n            'date': date,\n            'host': host\n        }\n        \n        return f\"{base_url}?{urlencode(auth_params)}\"\n    \n    def _build_request_body(\n        self, \n        messages: List[Dict[str, str]], \n        system_prompt: str = None,\n        deep_thinking: bool = False\n    ) -> dict:\n        \"\"\"\n        构建请求体\n        \n        Args:\n            messages: 消息列表\n            system_prompt: 系统提示词（可选）\n            deep_thinking: 是否启用深度思考（可选）\n        \n        Returns:\n            请求体字典\n        \"\"\"\n        # 获取模型对应的 domain\n        model = self.get_model()\n        domain = self.MODEL_DOMAINS.get(model, 'general')\n        \n        # 构建消息列表\n        request_messages = []\n        \n        # 讯飞星火不支持 system role，需要将 system prompt 转换为 user 消息\n        if system_prompt:\n            request_messages.append({\n                \"role\": \"user\",\n                \"content\": system_prompt\n            })\n        \n        # 如果启用深度思考，添加深度思考提示词\n        if deep_thinking:\n            request_messages.append({\n                \"role\": \"user\",\n                \"content\": DEEP_THINKING_PROMPT\n            })\n        \n        # 添加对话消息\n        request_messages.extend(messages)\n        \n        # 构建请求体\n        request_body = {\n            \"header\": {\n                \"app_id\": self.app_id,\n                \"uid\": \"user\"\n            },\n            \"parameter\": {\n                \"chat\": {\n                    \"domain\": domain,\n                    \"temperature\": self.get_temperature(),\n                }\n            },\n            \"payload\": {\n                \"message\": {\n                    \"text\": request_messages\n                }\n            }\n        }\n        \n        # 只有在设置了 max_tokens 时才添加到请求体\n        max_tokens = self.get_max_tokens()\n        if max_tokens is not None:\n            request_body[\"parameter\"][\"chat\"][\"max_tokens\"] = max_tokens\n        \n        return request_body\n    \n    async def chat_stream(\n        self, \n        messages: List[Dict[str, str]], \n        system_prompt: str = None,\n        deep_thinking: bool = False\n    ) -> AsyncIterator[StreamChunk]:\n        \"\"\"\n        流式聊天\n        \n        Args:\n            messages: 消息列表\n                [\n                    {\"role\": \"user\", \"content\": \"你好\"},\n                    {\"role\": \"assistant\", \"content\": \"你好！有什么可以帮助你的吗？\"},\n                    ...\n                ]\n            system_prompt: 系统提示词（可选）\n            deep_thinking: 是否启用深度思考（可选，默认 False）\n        \n        Yields:\n            StreamChunk: 流式响应数据结构，包含 content 和 finish_reason\n        \n        Raises:\n            Exception: WebSocket 连接或通信错误\n        \"\"\"\n        # 获取模型对应的 WebSocket URL\n        model = self.get_model()\n        base_url = self.MODEL_URLS[model]\n        \n        # 生成鉴权 URL\n        auth_url = self._generate_auth_url(base_url)\n        \n        # 构建请求体\n        request_body = self._build_request_body(messages, system_prompt, deep_thinking)\n        \n        # 获取代理配置\n        proxy = get_websocket_proxy()\n        \n        try:\n            # 建立 WebSocket 连接\n            connect_kwargs = {\n                'ping_interval': None,\n                'close_timeout': self.timeout\n            }\n            \n            # 如果有代理配置，添加代理参数\n            if proxy:\n                logger.debug(f\"[讯飞星火] 使用代理: {proxy}\")\n                # 注意：websockets 库的代理支持可能有限\n                # 某些版本可能不支持 proxy 参数\n                try:\n                    connect_kwargs['proxy'] = proxy\n                except TypeError:\n                    logger.warning(\"[讯飞星火] 当前 websockets 版本不支持代理参数\")\n            \n            async with websockets.connect(auth_url, **connect_kwargs) as websocket:\n                # 发送请求\n                await websocket.send(json.dumps(request_body, ensure_ascii=False))\n                \n                # 接收响应\n                while True:\n                    try:\n                        # 设置接收超时\n                        response_text = await asyncio.wait_for(\n                            websocket.recv(),\n                            timeout=self.timeout\n                        )\n                        \n                        # 解析响应\n                        response = json.loads(response_text)\n                        \n                        # 检查错误\n                        header = response.get('header', {})\n                        code = header.get('code', 0)\n                        \n                        if code != 0:\n                            message = header.get('message', '未知错误')\n                            raise ValueError(f\"讯飞星火 API 错误 ({code}): {message}\")\n                        \n                        # 提取内容\n                        payload = response.get('payload', {})\n                        choices = payload.get('choices', {})\n                        text_list = choices.get('text', [])\n                        \n                        for text_item in text_list:\n                            content = text_item.get('content', '')\n                            if content:\n                                yield StreamChunk(content=content)\n                        \n                        # 检查是否结束\n                        status = choices.get('status', 0)\n                        if status == 2:  # 2 表示结束\n                            yield StreamChunk(content=\"\", finish_reason=\"stop\")\n                            break\n                    \n                    except asyncio.TimeoutError:\n                        raise asyncio.TimeoutError(f\"讯飞星火 API 接收超时（{self.timeout}秒）\")\n        \n        except websockets.exceptions.WebSocketException as e:\n            raise Exception(f\"讯飞星火 WebSocket 错误: {str(e)}\")\n        \n        except Exception as e:\n            if isinstance(e, (asyncio.TimeoutError, ValueError)):\n                raise\n            raise Exception(f\"讯飞星火 API 错误: {str(e)}\")\n    \n    async def test_connection(self) -> Tuple[bool, str, float]:\n        \"\"\"\n        测试连接\n        \n        Returns:\n            (success, message, latency)\n            - success: 是否成功\n            - message: 结果消息\n            - latency: 延迟（毫秒）\n        \"\"\"\n        start_time = time.time()\n        \n        try:\n            # 发送一个简单的测试消息\n            test_messages = [\n                {\"role\": \"user\", \"content\": \"你好\"}\n            ]\n            \n            # 尝试获取第一个响应片段\n            response_received = False\n            async for chunk in self.chat_stream(test_messages):\n                if chunk:\n                    response_received = True\n                    break\n            \n            latency = int((time.time() - start_time) * 1000)  # 转换为毫秒（整数）\n            \n            if response_received:\n                return True, \"连接成功\", latency\n            else:\n                return False, \"未收到响应\", latency\n        \n        except asyncio.TimeoutError:\n            latency = int((time.time() - start_time) * 1000)\n            return False, \"连接超时\", latency\n        \n        except Exception as e:\n            latency = int((time.time() - start_time) * 1000)\n            return False, f\"连接错误: {str(e)}\", latency\n    \n\n    async def get_available_models_async(self) -> List[str]:\n        \"\"\"\n        异步获取可用模型列表\n        \n        Returns:\n            模型名称列表\n        \n        Note:\n            讯飞星火的模型列表是固定的，由 MODEL_URLS 定义。\n        \"\"\"\n        # 讯飞星火的模型列表是固定的，返回固定列表\n        return list(self.MODEL_URLS.keys())\n"}, "98c7f1ce95484824f2c8d92ea2e4b94403ea2bf0": {"t": 1785809896.8773384, "n": 11701, "h": "29d4e02bc0120df6", "c": "\"\"\"\nSparkProvider 实现（讯飞星火）\n\n提供讯飞星火 API 的流式聊天功能，支持：\n- WebSocket 流式响应\n- 连接测试\n- 模型列表获取\n- 错误处理和超时控制\n\n讯飞星火 API 文档：https://www.xfyun.cn/doc/spark/Web.html\n\"\"\"\n\nimport asyncio\nimport time\nimport json\nimport hmac\nimport hashlib\nimport base64\nfrom typing import AsyncIterator, Dict, Any, List, Tuple\nfrom urllib.parse import urlencode, urlparse\nfrom datetime import datetime, timezone\nimport logging\n\ntry:\n    import websockets\nexcept ImportError:\n    websockets = None\n\nfrom .base_provider import BaseProvider, StreamChunk\nfrom .network_utils import get_websocket_proxy\nfrom ._deep_thinking_prompt import DEEP_THINKING_PROMPT\n\nlogger = logging.getLogger(__name__)\n\n\nclass SparkProvider(BaseProvider):\n    \"\"\"讯飞星火 Provider 实现类\n    \n    讯飞星火使用 WebSocket 协议进行通信，需要特殊的鉴权方式。\n    \"\"\"\n    \n    # 模型版本对应的 API 地址\n    MODEL_URLS = {\n        'v1.5': 'wss://spark-api.xf-yun.com/v1.1/chat',\n        'v2.0': 'wss://spark-api.xf-yun.com/v2.1/chat',\n        'v3.0': 'wss://spark-api.xf-yun.com/v3.1/chat',\n        'v3.5': 'wss://spark-api.xf-yun.com/v3.5/chat',\n        'v4.0': 'wss://spark-api.xf-yun.com/v4.0/chat',\n    }\n    \n    # 模型版本对应的 domain\n    MODEL_DOMAINS = {\n        'v1.5': 'general',\n        'v2.0': 'generalv2',\n        'v3.0': 'generalv3',\n        'v3.5': 'generalv3.5',\n        'v4.0': 'generalv4',\n    }\n    \n    def __init__(self, config: Dict[str, Any], use_proxy: bool = False):\n        \"\"\"\n        初始化讯飞星火 Provider\n        \n        Args:\n            config: 配置字典\n                {\n                    \"app_id\": str,           # 应用 ID（必需）\n                    \"api_key\": str,          # API Key（必需）\n                    \"api_secret\": str,       # API Secret（必需）\n                    \"model\": str,            # 模型版本（必需，如 v3.5）\n                    \"temperature\": float,    # 温度参数（可选，默认 0.5）\n                    \"max_tokens\": int,       # 最大 Token 数（可选，默认 2048）\n                    \"timeout\": int,          # 超时时间（秒，可选，默认 30）\n                }\n            use_proxy: 是否使用系统代理\n        \"\"\"\n        super().__init__(config, use_proxy=use_proxy)\n        \n        # 检查 websockets 库是否可用\n        if websockets is None:\n            raise ImportError(\n                \"SparkProvider 需要 websockets 库。\"\n                \"请运行: pip install websockets\"\n            )\n        \n        self.app_id = config.get('app_id', '')\n        self.api_key = config.get('api_key', '')\n        self.api_secret = config.get('api_secret', '')\n        self.timeout = config.get('timeout', 300)  # 默认 5 分钟\n        \n        # 验证必需参数\n        if not self.app_id:\n            raise ValueError(\"讯飞星火 Provider 需要提供 app_id\")\n        if not self.api_key:\n            raise ValueError(\"讯飞星火 Provider 需要提供 api_key\")\n        if not self.api_secret:\n            raise ValueError(\"讯飞星火 Provider 需要提供 api_secret\")\n        \n        # 验证模型版本\n        model = self.get_model()\n        if model not in self.MODEL_URLS:\n            available = ', '.join(self.MODEL_URLS.keys())\n            raise ValueError(\n                f\"不支持的讯飞星火模型: '{model}'. \"\n                f\"可用模型: {available}\"\n            )\n    \n    def _generate_auth_url(self, base_url: str) -> str:\n        \"\"\"\n        生成带鉴权参数的 WebSocket URL\n        \n        讯飞星火使用 HMAC-SHA256 签名进行鉴权。\n        \n        Args:\n            base_url: 基础 WebSocket URL\n        \n        Returns:\n            带鉴权参数的完整 URL\n        \"\"\"\n        # 解析 URL\n        parsed = urlparse(base_url)\n        host = parsed.netloc\n        path = parsed.path\n        \n        # 生成 RFC1123 格式的时间戳\n        now = datetime.now(timezone.utc)\n        date = now.strftime('%a, %d %b %Y %H:%M:%S GMT')\n        \n        # 构建签名原文\n        signature_origin = f\"host: {host}\\ndate: {date}\\nGET {path} HTTP/1.1\"\n        \n        # 使用 HMAC-SHA256 生成签名\n        signature = hmac.new(\n            self.api_secret.encode('utf-8'),\n            signature_origin.encode('utf-8'),\n            hashlib.sha256\n        ).digest()\n        \n        # Base64 编码签名\n        signature_base64 = base64.b64encode(signature).decode('utf-8')\n        \n        # 构建 authorization 字符串\n        authorization_origin = (\n            f'api_key=\"{self.api_key}\", '\n            f'algorithm=\"hmac-sha256\", '\n            f'headers=\"host date request-line\", '\n            f'signature=\"{signature_base64}\"'\n        )\n        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode('utf-8')\n        \n        # 构建完整 URL\n        auth_params = {\n            'authorization': authorization,\n            'date': date,\n            'host': host\n        }\n        \n        return f\"{base_url}?{urlencode(auth_params)}\"\n    \n    def _build_request_body(\n        self, \n        messages: List[Dict[str, str]], \n        system_prompt: str = None,\n        deep_thinking: bool = False\n    ) -> dict:\n        \"\"\"\n        构建请求体\n        \n        Args:\n            messages: 消息列表\n            system_prompt: 系统提示词（可选）\n            deep_thinking: 是否启用深度思考（可选）\n        \n        Returns:\n            请求体字典\n        \"\"\"\n        # 获取模型对应的 domain\n        model = self.get_model()\n        domain = self.MODEL_DOMAINS.get(model, 'general')\n        \n        # 构建消息列表\n        request_messages = []\n        \n        # 讯飞星火不支持 system role，需要将 system prompt 转换为 user 消息\n        if system_prompt:\n            request_messages.append({\n                \"role\": \"user\",\n                \"content\": system_prompt\n            })\n        \n        # 如果启用深度思考，添加深度思考提示词\n        if deep_thinking:\n            request_messages.append({\n                \"role\": \"user\",\n                \"content\": DEEP_THINKING_PROMPT\n            })\n        \n        # 添加对话消息\n        request_messages.extend(messages)\n        \n        # 构建请求体\n        request_body = {\n            \"header\": {\n                \"app_id\": self.app_id,\n                \"uid\": \"user\"\n            },\n            \"parameter\": {\n                \"chat\": {\n                    \"domain\": domain,\n                    \"temperature\": self.get_temperature(),\n                }\n            },\n            \"payload\": {\n                \"message\": {\n                    \"text\": request_messages\n                }\n            }\n        }\n        \n        # 只有在设置了 max_tokens 时才添加到请求体\n        max_tokens = self.get_max_tokens()\n        if max_tokens is not None:\n            request_body[\"parameter\"][\"chat\"][\"max_tokens\"] = max_tokens\n        \n        return request_body\n    \n    async def chat_stream(\n        self, \n        messages: List[Dict[str, str]], \n        system_prompt: str = None,\n        deep_thinking: bool = False\n    ) -> AsyncIterator[StreamChunk]:\n        \"\"\"\n        流式聊天\n        \n        Args:\n            messages: 消息列表\n                [\n                    {\"role\": \"user\", \"content\": \"你好\"},\n                    {\"role\": \"assistant\", \"content\": \"你好！有什么可以帮助你的吗？\"},\n                    ...\n                ]\n            system_prompt: 系统提示词（可选）\n            deep_thinking: 是否启用深度思考（可选，默认 False）\n        \n        Yields:\n            StreamChunk: 流式响应数据结构，包含 content 和 finish_reason\n        \n        Raises:\n            Exception: WebSocket 连接或通信错误\n        \"\"\"\n        # 获取模型对应的 WebSocket URL\n        model = self.get_model()\n        base_url = self.MODEL_URLS[model]\n        \n        # 生成鉴权 URL\n        auth_url = self._generate_auth_url(base_url)\n        \n        # 构建请求体\n        request_body = self._build_request_body(messages, system_prompt, deep_thinking)\n        \n        # 获取代理配置\n        proxy = get_websocket_proxy()\n        \n        try:\n            # 建立 WebSocket 连接\n            connect_kwargs = {\n                'ping_interval': None,\n                'close_timeout': self.timeout\n            }\n            \n            # 如果有代理配置，添加代理参数\n            if proxy:\n                logger.debug(f\"[讯飞星火] 使用代理: {proxy}\")\n                # 注意：websockets 库的代理支持可能有限\n                # 某些版本可能不支持 proxy 参数\n                try:\n                    connect_kwargs['proxy'] = proxy\n                except TypeError:\n                    logger.warning(\"[讯飞星火] 当前 websockets 版本不支持代理参数\")\n            \n            async with websockets.connect(auth_url, **connect_kwargs) as websocket:\n                # 发送请求\n                await websocket.send(json.dumps(request_body, ensure_ascii=False))\n                \n                # 接收响应\n                while True:\n                    try:\n                        # 设置接收超时\n                        response_text = await asyncio.wait_for(\n                            websocket.recv(),\n                            timeout=self.timeout\n                        )\n                        \n                        # 解析响应\n                        response = json.loads(response_text)\n                        \n                        # 检查错误\n                        header = response.get('header', {})\n                        code = header.get('code', 0)\n                        \n                        if code != 0:\n                            message = header.get('message', '未知错误')\n                            raise ValueError(f\"讯飞星火 API 错误 ({code}): {message}\")\n                        \n                        # 提取内容\n                        payload = response.get('payload', {})\n                        choices = payload.get('choices', {})\n                        text_list = choices.get('text', [])\n                        \n                        for text_item in text_list:\n                            content = text_item.get('content', '')\n                            if content:\n                                yield StreamChunk(content=content)\n                        \n                        # 检查是否结束\n                        status = choices.get('status', 0)\n                        if status == 2:  # 2 表示结束\n                            yield StreamChunk(content=\"\", finish_reason=\"stop\")\n                            break\n                    \n                    except asyncio.TimeoutError:\n                        raise asyncio.TimeoutError(f\"讯飞星火 API 接收超时（{self.timeout}秒）\")\n        \n        except websockets.exceptions.WebSocketException as e:\n            raise Exception(f\"讯飞星火 WebSocket 错误: {str(e)}\")\n        \n        except Exception as e:\n            if isinstance(e, (asyncio.TimeoutError, ValueError)):\n                raise\n            raise Exception(f\"讯飞星火 API 错误: {str(e)}\")\n    \n    async def test_connection(self) -> Tuple[bool, str, float]:\n        \"\"\"\n        测试连接\n        \n        Returns:\n            (success, message, latency)\n            - success: 是否成功\n            - message: 结果消息\n            - latency: 延迟（毫秒）\n        \"\"\"\n        start_time = time.time()\n        \n        try:\n            # 发送一个简单的测试消息\n            test_messages = [\n                {\"role\": \"user\", \"content\": \"你好\"}\n            ]\n            \n            # 尝试获取第一个响应片段\n            response_received = False\n            async for chunk in self.chat_stream(test_messages):\n                if chunk:\n                    response_received = True\n                    break\n            \n            latency = int((time.time() - start_time) * 1000)  # 转换为毫秒（整数）\n            \n            if response_received:\n                return True, \"连接成功\", latency\n            else:\n                return False, \"未收到响应\", latency\n        \n        except asyncio.TimeoutError:\n            latency = int((time.time() - start_time) * 1000)\n            return False, \"连接超时\", latency\n        \n        except Exception as e:\n            latency = int((time.time() - start_time) * 1000)\n            return False, f\"连接错误: {str(e)}\", latency\n    \n\n    async def get_available_models_async(self) -> List[str]:\n        \"\"\"\n        异步获取可用模型列表\n        \n        Returns:\n            模型名称列表\n        \n        Note:\n            讯飞星火的模型列表是固定的，由 MODEL_URLS 定义。\n        \"\"\"\n        # 讯飞星火的模型列表是固定的，返回固定列表\n        return list(self.MODEL_URLS.keys())\n"}}}}