优化处理

src/llm_models/model_client/gemini_client.py

@@ -362,18 +362,29 @@ class GeminiClient(BaseClient):
                 http_options_kwargs["api_version"] = parts[1]
             else:
                 http_options_kwargs["base_url"] = api_provider.base_url
+                http_options_kwargs["api_version"] = None
         self.client = genai.Client(
             http_options=HttpOptions(**http_options_kwargs),
             api_key=api_provider.api_key,
         )  # 这里和openai不一样，gemini会自己决定自己是否需要retry
 
     @staticmethod
-    def clamp_thinking_budget(tb: int, model_id: str) -> int:
+    def clamp_thinking_budget(extra_params: dict[str, Any] | None, model_id: str) -> int:
         """
         按模型限制思考预算范围，仅支持指定的模型（支持带数字后缀的新版本）
         """
         limits = None
 
+        # 参数传入处理
+        tb = THINKING_BUDGET_AUTO
+        if extra_params and "thinking_budget" in extra_params:
+            try:
+                tb = int(extra_params["thinking_budget"])
+            except (ValueError, TypeError):
+                logger.warning(
+                    f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用模型自动预算模式 {tb}"
+                )
+        
         # 优先尝试精确匹配
         if model_id in THINKING_BUDGET_LIMITS:
             limits = THINKING_BUDGET_LIMITS[model_id]
@@ -456,19 +467,9 @@ class GeminiClient(BaseClient):
         messages = _convert_messages(message_list)
         # 将tool_options转换为Gemini API所需的格式
         tools = _convert_tool_options(tool_options) if tool_options else None
-
-        tb = THINKING_BUDGET_AUTO
-        # 空处理
-        if extra_params and "thinking_budget" in extra_params:
-            try:
-                tb = int(extra_params["thinking_budget"])
-            except (ValueError, TypeError):
-                logger.warning(
-                    f"无效的 thinking_budget 值 {extra_params['thinking_budget']}，将使用模型自动预算模式 {tb}"
-                )
-        # 裁剪到模型支持的范围
-        tb = self.clamp_thinking_budget(tb, model_info.model_identifier)
-
+        # 解析并裁剪 thinking_budget
+        tb = self.clamp_thinking_budget(extra_params, model_info.model_identifier)
+        
         # 将response_format转换为Gemini API所需的格式
         generation_config_dict = {
             "max_output_tokens": max_tokens,
@@ -590,41 +591,51 @@ class GeminiClient(BaseClient):
 
         return response
 
-    def get_audio_transcriptions(
-        self, model_info: ModelInfo, audio_base64: str, extra_params: dict[str, Any] | None = None
+    async def get_audio_transcriptions(
+        self,
+        model_info: ModelInfo,
+        audio_base64: str,
+        max_tokens: int = 2048,
+        extra_params: dict[str, Any] | None = None,
     ) -> APIResponse:
         """
         获取音频转录
         :param model_info: 模型信息
         :param audio_base64: 音频文件的Base64编码字符串
+        :param max_tokens: 最大输出token数（默认2048）
         :param extra_params: 额外参数（可选）
         :return: 转录响应
         """
+        # 解析并裁剪 thinking_budget
+        tb = self.clamp_thinking_budget(extra_params, model_info.model_identifier)
+
+        # 构造 prompt + 音频输入
+        prompt = "Generate a transcript of the speech. The language of the transcript should **match the language of the speech**."
+        contents = [
+            Content(
+                role="user",
+                parts=[
+                    Part.from_text(text=prompt),
+                    Part.from_bytes(data=base64.b64decode(audio_base64), mime_type="audio/wav"),
+                ],
+            )
+        ]
+
         generation_config_dict = {
-            "max_output_tokens": 2048,
+            "max_output_tokens": max_tokens,
             "response_modalities": ["TEXT"],
             "thinking_config": ThinkingConfig(
                 include_thoughts=True,
-                thinking_budget=(
-                    extra_params["thinking_budget"] if extra_params and "thinking_budget" in extra_params else 1024
-                ),
+                thinking_budget=tb,
             ),
             "safety_settings": gemini_safe_settings,
         }
         generate_content_config = GenerateContentConfig(**generation_config_dict)
-        prompt = "Generate a transcript of the speech. The language of the transcript should **match the language of the speech**."
+
         try:
-            raw_response: GenerateContentResponse = self.client.models.generate_content(
+            raw_response: GenerateContentResponse = await self.client.aio.models.generate_content(
                 model=model_info.model_identifier,
-                contents=[
-                    Content(
-                        role="user",
-                        parts=[
-                            Part.from_text(text=prompt),
-                            Part.from_bytes(data=base64.b64decode(audio_base64), mime_type="audio/wav"),
-                        ],
-                    )
-                ],
+                contents=contents,
                 config=generate_content_config,
             )
             resp, usage_record = _default_normal_response_parser(raw_response)