feat(demo): 初始化审核智能体演示基线

review_agent/llm.py

@@ -0,0 +1,79 @@
+import json
+from urllib import error, request
+
+from django.conf import settings
+
+
+class LLMConfigurationError(RuntimeError):
+    """Raised when the project has not been configured with a usable LLM provider."""
+
+
+class LLMRequestError(RuntimeError):
+    """Raised when the remote LLM provider call fails."""
+
+
+def generate_reply(conversation, user_message: str) -> str:
+    """Calls the SiliconFlow OpenAI-compatible chat endpoint 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": build_messages(conversation, user_message),
+        "temperature": 0.3,
+    }
+    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 build_messages(conversation, latest_user_message: str) -> list[dict[str, str]]:
+    """Builds system and conversation history messages for the provider call."""
+
+    messages = [{"role": "system", "content": system_prompt()}]
+
+    for message in conversation.messages.all():
+        messages.append({"role": message.role, "content": message.content})
+
+    if not conversation.messages.filter(role="user", content=latest_user_message.strip()).exists():
+        messages.append({"role": "user", "content": latest_user_message.strip()})
+
+    return messages
+
+
+def system_prompt() -> str:
+    """Defines the initial assistant behavior for the review workspace."""
+
+    return (
+        "你是“审核智能体”，服务于体外诊断试剂临床注册文件准备与审核场景。"
+        "你的回答要专业、简洁、结构清楚，优先帮助用户完成法规核查、说明书审核、"
+        "风险识别、资料补充建议和审评思路梳理。"
+        "当信息不足时，明确指出缺失信息，并给出下一步建议。"
+        "除非用户明确要求英文，否则始终使用中文回答。"
+    )