feat(tools): 添加 reasoning.py 工具模块

- 新增 reasoning.py 文件，实现与 OpenAI API 的交互
- 添加 call_openai_api 函数，用于发送请求并处理响应
- 支持可选的 LLMParameters 参数，以定制化请求
- 处理 API 响应中的 tokens 使用情况
- 提供错误处理和缓存 token 字段的处理

tools/reasoning.py

@@ -0,0 +1,82 @@
+import sys
+import asyncio
+import openai
+from pathlib import Path
+from datetime import datetime, timezone
+from typing import Optional
+sys.path.append(str(Path(__file__).resolve().parent.parent))
+from schema import APIProvider, LLMRequest, LLMResponse, TokensUsage, LLMParameters
+
+async def call_openai_api(llm_request: LLMRequest, llm_parameters: Optional[LLMParameters] = None) -> LLMResponse:
+    start_time = datetime.now(timezone.utc)
+    client = openai.AsyncOpenAI(
+        api_key=llm_request.api_provider.api_key,
+        base_url=llm_request.api_provider.base_url
+    )
+    
+    try:
+        messages = [{"role": "user", "content": llm_request.prompt}]
+        response = await client.chat.completions.create(
+            model=llm_request.api_provider.model_id,
+            messages=messages,
+            temperature=llm_parameters.temperature if llm_parameters else None,
+            max_tokens=llm_parameters.max_tokens if llm_parameters else None,
+            top_p=llm_parameters.top_p if llm_parameters else None,
+            frequency_penalty=llm_parameters.frequency_penalty if llm_parameters else None,
+            presence_penalty=llm_parameters.presence_penalty if llm_parameters else None,
+            seed=llm_parameters.seed if llm_parameters else None
+        )
+        
+        end_time = datetime.now(timezone.utc)
+        duration = (end_time - start_time).total_seconds()
+        
+        # 处理可能不存在的缓存token字段
+        usage = response.usage
+        cache_hit = getattr(usage, 'prompt_cache_hit_tokens', None)
+        cache_miss = getattr(usage, 'prompt_cache_miss_tokens', None)
+        
+        tokens_usage = TokensUsage(
+            prompt_tokens=usage.prompt_tokens,
+            completion_tokens=usage.completion_tokens,
+            prompt_cache_hit_tokens=cache_hit,
+            prompt_cache_miss_tokens=cache_miss if cache_miss is not None else usage.prompt_tokens
+        )
+        
+        return LLMResponse(
+            response_id=response.id,
+            tokens_usage=tokens_usage,
+            response_content={"content": response.choices[0].message.content},
+            total_duration=duration,
+            llm_parameters=llm_parameters
+        )
+    except Exception as e:
+        end_time = datetime.now(timezone.utc)
+        duration = (end_time - start_time).total_seconds()
+        return LLMResponse(
+            response_id="error",
+            response_content={"error": str(e)},
+            total_duration=duration
+        )
+
+if __name__ == "__main__":
+    from sqlmodel import Session, select
+    from global_var import get_sql_engine, init_global_var
+    init_global_var("workdir")
+    api_state = "1 deepseek-chat"
+    with Session(get_sql_engine()) as session:
+        api_provider = session.exec(select(APIProvider).where(APIProvider.id == int(api_state.split(" ")[0]))).first()
+    llm_request = LLMRequest(
+        prompt="你好，世界！",
+        api_provider=api_provider
+    )
+    # 不使用LLM参数调用
+    result = asyncio.run(call_openai_api(llm_request))
+    print(f"\n不使用LLM参数调用结果: {result}")
+
+    # 使用LLM参数调用
+    params = LLMParameters(
+        temperature=0.7,
+        max_tokens=100
+    )
+    result = asyncio.run(call_openai_api(llm_request, params))
+    print(f"\nOpenAI API 响应: {result}")