Initial commit: V1

text_ai.py

@@ -1,43 +1,44 @@
 """
-text_ai.py - LLM 文本生成
-用于场景划分等 AI 推理任务
-支持多 LLM 提供商切换
+text_ai.py - LLM text generation client.
+
+Supports multiple providers defined in config.py.
 """
 
 from openai import OpenAI
-from config import LLM_PROVIDERS, DEFAULT_LLM, LLM_API_KEY, LLM_API_BASE, LLM_MODEL
+from config import LLM_PROVIDERS
 
 
 def text_ai(in_put: str, system_prompt: str = "You are a helpful assistant.",
             provider: str = None) -> str:
-    """
-    调用 LLM 生成文本
+    """Call LLM to generate text.
 
     Args:
-        in_put: 用户输入内容
-        system_prompt: 系统提示词
-        provider: LLM 提供商名称（对应 LLM_PROVIDERS 的 key），None 则用默认
-
+        in_put: user message
+        system_prompt: system prompt
+        provider: provider name (key in LLM_PROVIDERS), None = first in dict
     Returns:
-        AI 生成的文本
+        generated text
     """
     if provider and provider in LLM_PROVIDERS:
         cfg = LLM_PROVIDERS[provider]
-        api_key = cfg["api_key"]
-        api_base = cfg["api_base"]
-        model = cfg["model"]
     else:
-        api_key = LLM_API_KEY
-        api_base = LLM_API_BASE
-        model = LLM_MODEL
+        # Default to first provider in dict
+        cfg = next(iter(LLM_PROVIDERS.values()))
+        provider = next(iter(LLM_PROVIDERS))
 
-    client = OpenAI(
-        api_key=api_key,
-        base_url=api_base,
-    )
+    api_key = cfg["api_key"]
+    api_base = cfg["api_base"]
+    model = cfg["model"]
 
-    # ModelScope 的 Qwen3 系列和 GLM 系列默认开启 thinking，需要关掉
-    # 注意：MiniMax 系列不是 Qwen/GLM，不需要也不能传 enable_thinking
+    if not api_key:
+        raise ValueError(
+            f"API key not configured for '{provider}'. "
+            f"Edit config.py and fill in the api_key field."
+        )
+
+    client = OpenAI(api_key=api_key, base_url=api_base)
+
+    # ModelScope Qwen3/GLM default to thinking mode, disable it
     extra_body = {}
     is_modelscope = "modelscope" in api_base.lower()
     is_qwen = "qwen" in model.lower()
@@ -49,38 +50,31 @@ def text_ai(in_put: str, system_prompt: str = "You are a helpful assistant.",
         model=model,
         messages=[
             {"role": "system", "content": system_prompt},
-            {"role": "user", "content": in_put}
+            {"role": "user", "content": in_put},
         ],
         max_tokens=16384,
         stream=False,
-        extra_body=extra_body if extra_body else None,
+        extra_body=extra_body or None,
     )
 
-    # 防御：choices 为空或 None
     if not response.choices:
-        # 尝试从 response 对象提取有用信息
         resp_dict = response.model_dump() if hasattr(response, "model_dump") else {}
         error_msg = resp_dict.get("error", {})
-        if isinstance(error_msg, dict):
-            err_text = error_msg.get("message", str(error_msg))
-        else:
-            err_text = str(resp_dict)
+        err_text = error_msg.get("message", str(error_msg)) if isinstance(error_msg, dict) else str(resp_dict)
         raise ValueError(
-            f"模型 '{model}' 返回了空的 choices。\n"
-            f"响应内容: {err_text}\n"
-            f"可能是模型暂时不可用或请求被拒绝。"
+            f"Model '{model}' returned empty choices.\n"
+            f"Response: {err_text}\n"
+            f"Model may be unavailable or request was rejected."
         )
 
     msg = response.choices[0].message
     content = msg.content
 
-    # 检测输出是否被截断
-    finish = response.choices[0].finish_reason
-    if finish == "length":
-        print(f"[WARN] LLM output truncated (finish_reason=length), max_tokens may be too small")
+    if response.choices[0].finish_reason == "length":
+        print(f"[WARN] LLM output truncated (finish_reason=length)")
 
     if content is None:
-        # fallback：尝试多种字段名（不同 API 叫法不同）
+        # Fallback: try alternate field names
         for attr in ("thinking_content", "reasoning_content", "text", "output"):
             fallback = getattr(msg, attr, None)
             if fallback:
@@ -88,7 +82,6 @@ def text_ai(in_put: str, system_prompt: str = "You are a helpful assistant.",
                 break
 
     if content is None:
-        # 最后一搏：尝试把 message 对象当 dict 看
         try:
             msg_dict = msg.model_dump() if hasattr(msg, "model_dump") else vars(msg)
             for v in msg_dict.values():
@@ -99,11 +92,8 @@ def text_ai(in_put: str, system_prompt: str = "You are a helpful assistant.",
             pass
 
     if content is None:
-        finish = response.choices[0].finish_reason
         raise ValueError(
-            f"模型 '{model}' 返回内容为空（content=None），"
-            f"finish_reason={finish}。\n"
-            f"如果使用 MiniMax 系列，请改用 Qwen3.5-35B (ModelScope 免费) 或其他 Qwen 模型。"
+            f"Model '{model}' returned None content (finish_reason={response.choices[0].finish_reason})."
         )
     return content