1、使用异步API

src/mini_agent/agent/async_llm_client.py

@@ -0,0 +1,39 @@
+import os
+
+from openai import AsyncOpenAI
+from openai.types.chat import (
+    ChatCompletion,
+    ChatCompletionMessageParam,
+    ChatCompletionToolChoiceOptionParam,
+    ChatCompletionToolParam,
+)
+
+
+class AsyncLLMClient:
+    def __init__(
+        self,
+        model: str | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: int | None = None,
+    ) -> None:
+        self.model = model or os.getenv("LLM_MODEL_ID")
+        self.base_url = base_url or os.getenv("LLM_BASE_URL")
+        self.api_key = api_key or os.getenv("LLM_API_KEY")
+        self.timeout = timeout if timeout is not None else int(os.getenv("LLM_TIMEOUT", "60"))
+
+        if not all([self.model, self.base_url, self.api_key]):
+            raise ValueError("模型ID、API密钥和服务地址必须被提供或在.env文件中定义。")
+        self.client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key, timeout=self.timeout)
+
+    async def think(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        tools: list[ChatCompletionToolParam],
+        tool_choice: ChatCompletionToolChoiceOptionParam = "auto",
+        **kwargs,
+    ) -> ChatCompletion:
+        assert self.model is not None
+        return await self.client.chat.completions.create(
+            model=self.model, messages=messages, tools=tools, tool_choice=tool_choice, **kwargs
+        )