feat: 一系列新功能

src/heurams/providers/audio/protocol.py

@@ -1,13 +0,0 @@
-import pathlib
-from typing import Protocol
-
-from heurams.services.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-class PlayFunctionProtocol(Protocol):
-    def __call__(self, path: pathlib.Path) -> None: ...
-
-
-logger.debug("音频协议模块已加载")

src/heurams/providers/llm/__init__.py

@@ -1,6 +1,19 @@
 # 大语言模型
 from heurams.services.logger import get_logger
 
+from .base import BaseLLM
+from .openai import OpenAILLM
+
 logger = get_logger(__name__)
 
-logger.debug("LLM providers 模块已加载")
+__all__ = [
+    "BaseLLM",
+    "OpenAILLM",
+]
+
+providers = {
+    "base": BaseLLM,
+    "openai": OpenAILLM,
+}
+
+logger.debug("LLM providers 已注册: %s", list(providers.keys()))

src/heurams/providers/llm/base.py

@@ -1,5 +1,55 @@
+"""LLM 提供者基类"""
+
+import asyncio
+from typing import Any, Dict, List, Optional
+
 from heurams.services.logger import get_logger
 
 logger = get_logger(__name__)
 
-logger.debug("LLM 基类模块已加载")
+
+class BaseLLM:
+    """LLM 提供者基类"""
+
+    name = "BaseLLM"
+
+    def __init__(self, config: Dict[str, Any]):
+        """初始化 LLM 提供者
+
+        Args:
+            config: 提供者配置字典
+        """
+        self.config = config
+        logger.debug("BaseLLM 初始化完成")
+
+    async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
+        """发送聊天消息并获取响应
+
+        Args:
+            messages: 消息列表，每个消息为 {"role": "user"|"assistant"|"system", "content": "消息内容"}
+            **kwargs: 其他参数，如 temperature, max_tokens 等
+
+        Returns:
+            模型返回的文本响应
+        """
+        logger.debug("BaseLLM.chat: messages=%d, kwargs=%s", len(messages), kwargs)
+        logger.warning("BaseLLM.chat 是基类方法，未实现具体功能")
+        await asyncio.sleep(0)  # 避免未使用异步的警告
+        return "BaseLLM 未实现具体功能"
+
+    async def chat_stream(self, messages: List[Dict[str, str]], **kwargs):
+        """流式聊天（可选实现）
+
+        Args:
+            messages: 消息列表
+            **kwargs: 其他参数
+
+        Yields:
+            流式响应的文本块
+        """
+        logger.debug(
+            "BaseLLM.chat_stream: messages=%d, kwargs=%s", len(messages), kwargs
+        )
+        logger.warning("BaseLLM.chat_stream 是基类方法，未实现具体功能")
+        await asyncio.sleep(0)
+        yield "BaseLLM 未实现流式功能"

src/heurams/providers/llm/openai.py

@@ -1,5 +1,96 @@
+"""OpenAI 兼容 LLM 提供者"""
+
+import asyncio
+from typing import Any, AsyncGenerator, Dict, List, Optional
+
 from heurams.services.logger import get_logger
 
+from .base import BaseLLM
+
 logger = get_logger(__name__)
 
-logger.debug("OpenAI provider 模块已加载(未实现)")
+
+class OpenAILLM(BaseLLM):
+    """OpenAI 兼容 LLM 提供者"""
+
+    name = "OpenAI"
+
+    def __init__(self, config: Dict[str, Any]):
+        super().__init__(config)
+        self.api_key = config.get("key", "")
+        self.base_url = config.get("url", "https://api.openai.com/v1")
+        self._client = None
+        logger.debug("OpenAILLM 初始化完成: base_url=%s", self.base_url)
+
+    def _get_client(self):
+        """获取 OpenAI 客户端（延迟导入）"""
+        if self._client is None:
+            try:
+                from openai import AsyncOpenAI
+            except ImportError:
+                logger.error("未安装 openai 库，请运行: pip install openai")
+                raise ImportError("未安装 openai 库，请运行: pip install openai")
+
+            self._client = AsyncOpenAI(
+                api_key=self.api_key if self.api_key else None,
+                base_url=self.base_url if self.base_url else None,
+            )
+        return self._client
+
+    async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
+        """发送聊天消息并获取响应"""
+        logger.debug("OpenAILLM.chat: messages=%d", len(messages))
+
+        client = self._get_client()
+
+        # 默认参数
+        default_kwargs = {
+            "model": kwargs.get("model", "gpt-3.5-turbo"),
+            "temperature": kwargs.get("temperature", 0.7),
+            "max_tokens": kwargs.get("max_tokens", 1000),
+        }
+
+        # 合并参数，优先使用传入的 kwargs
+        request_kwargs = {**default_kwargs, **kwargs}
+        request_kwargs["messages"] = messages
+
+        try:
+            response = await client.chat.completions.create(**request_kwargs)
+            content = response.choices[0].message.content
+            logger.debug(
+                "OpenAILLM.chat 成功: response length=%d",
+                len(content) if content else 0,
+            )
+            return content or ""
+        except Exception as e:
+            logger.error("OpenAILLM.chat 失败: %s", e)
+            raise
+
+    async def chat_stream(
+        self, messages: List[Dict[str, str]], **kwargs
+    ) -> AsyncGenerator[str, None]:
+        """流式聊天"""
+        logger.debug("OpenAILLM.chat_stream: messages=%d", len(messages))
+
+        client = self._get_client()
+
+        # 默认参数
+        default_kwargs = {
+            "model": kwargs.get("model", "gpt-3.5-turbo"),
+            "temperature": kwargs.get("temperature", 0.7),
+            "max_tokens": kwargs.get("max_tokens", 1000),
+            "stream": True,
+        }
+
+        # 合并参数
+        request_kwargs = {**default_kwargs, **kwargs}
+        request_kwargs["messages"] = messages
+
+        try:
+            stream = await client.chat.completions.create(**request_kwargs)
+            async for chunk in stream:
+                if chunk.choices[0].delta.content:
+                    yield chunk.choices[0].delta.content
+        except Exception as e:
+            logger.error("OpenAILLM.chat_stream 失败: %s", e)
+            raise