feat(agent): 增加 LLM 路由与诊断日志

review_agent/llm.py

@@ -53,6 +53,47 @@ def generate_reply(conversation, user_message: str) -> str:
         raise LLMRequestError("模型接口返回格式不符合预期。") from exc
 
 
+def generate_completion(messages: list[dict[str, str]], *, temperature: float = 0.0) -> str:
+    """Calls the configured chat endpoint with explicit messages and returns assistant text."""
+
+    if not settings.LLM_API_KEY:
+        raise LLMConfigurationError("缺少 LLM_API_KEY 配置。")
+    if not settings.LLM_MODEL:
+        raise LLMConfigurationError("缺少 LLM_MODEL 配置。")
+
+    payload = {
+        "model": settings.LLM_MODEL,
+        "messages": messages,
+        "temperature": temperature,
+    }
+    body = json.dumps(payload).encode("utf-8")
+    endpoint = f"{settings.LLM_BASE_URL.rstrip('/')}/chat/completions"
+
+    http_request = request.Request(
+        endpoint,
+        data=body,
+        headers={
+            "Authorization": f"Bearer {settings.LLM_API_KEY}",
+            "Content-Type": "application/json",
+        },
+        method="POST",
+    )
+
+    try:
+        with request.urlopen(http_request, timeout=60) as response:
+            data = json.loads(response.read().decode("utf-8"))
+    except error.HTTPError as exc:
+        details = exc.read().decode("utf-8", errors="ignore")
+        raise LLMRequestError(f"模型接口调用失败：HTTP {exc.code} {details}") from exc
+    except error.URLError as exc:
+        raise LLMRequestError(f"模型接口调用失败：{exc.reason}") from exc
+
+    try:
+        return data["choices"][0]["message"]["content"].strip()
+    except (KeyError, IndexError, TypeError) as exc:
+        raise LLMRequestError("模型接口返回格式不符合预期。") from exc
+
+
 def stream_reply(conversation, user_message: str):
     """Streams incremental assistant text from the SiliconFlow chat endpoint."""