Agent/agent/llm/qwen.py

"""
Qwen LLM provider using OpenAI SDK.
"""

import os
import logging
from typing import Any, Callable, Dict, List, Optional
from openai import AsyncOpenAI

# 这里的导入根据你的项目结构调整
from .usage import TokenUsage
from .pricing import PricingCalculator

logger = logging.getLogger(__name__)

# 2026 推荐：如果 qwen3.5-plus 报 404，请先用 qwen-plus 测试
# 阿里有时要求兼容模式下的 ID 必须是特定的字符串
DEFAULT_QWEN_MODEL = "qwen-plus" 

def create_qwen_llm_call(
    model: str = DEFAULT_QWEN_MODEL,
    base_url: Optional[str] = None,
    api_key: Optional[str] = None,
) -> Callable:
    """
    Create a Qwen LLM call function using the OpenAI SDK.
    """
    # 获取配置
    # 注意：使用 OpenAI SDK 时，base_url 必须包含到 /v1
    api_key = api_key or os.getenv("QWEN_API_KEY")
    base_url = base_url or os.getenv("QWEN_BASE_URL", "https://dashscope.aliyuncs.com/compatible-mode/v1")

    if not api_key:
        raise ValueError("QWEN_API_KEY is required")

    # 初始化 OpenAI 异步客户端
    # SDK 会自动处理 /chat/completions 的拼接
    client = AsyncOpenAI(
        api_key=api_key,
        base_url=base_url
    )

    pricing_calc = PricingCalculator()

    async def llm_call(
        messages: List[Dict[str, Any]],
        model: str = model,
        tools: Optional[List[Dict]] = None,
        temperature: float = 0.7,
        max_tokens: int = 4096,
        **kwargs
    ) -> Dict[str, Any]:
        
        try:
            response = await client.chat.completions.create(
                model=model,
                messages=messages,
                tools=tools,
                temperature=temperature,
                max_tokens=max_tokens,
                **kwargs
            )

            # 获取内容
            content = response.choices[0].message.content or ""
            
            # --- 关键修正位置 ---
            # 将 Pydantic 对象转换为原始 Dict 列表，这样 runner.py 的 .get() 才不会报错
            tool_calls = None
            if response.choices[0].message.tool_calls:
                tool_calls = [
                    tc.model_dump() for tc in response.choices[0].message.tool_calls
                ]
            # ------------------

            usage = TokenUsage(
                input_tokens=response.usage.prompt_tokens,
                output_tokens=response.usage.completion_tokens,
            )

            cost = pricing_calc.calculate_cost(model=model, usage=usage)

            return {
                "content": content,
                "tool_calls": tool_calls, # 现在这里是 List[Dict] 了
                "prompt_tokens": usage.input_tokens,
                "completion_tokens": usage.output_tokens,
                "reasoning_tokens": getattr(response.usage, "reasoning_tokens", 0),
                "finish_reason": response.choices[0].finish_reason,
                "cost": cost,
                "usage": usage,
            }

        except Exception as e:
            logger.error(f"Qwen SDK Call Failed: {str(e)}")
            raise

    return llm_call