88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
import re
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import time
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import logging
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from uuid import UUID
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from django.conf import settings
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from openai import OpenAI
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logger = logging.getLogger(__name__)
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CLASSIFY_SYSTEM_PROMPT = """你是一个教育评估专家。根据布鲁姆认知分类学，分析以下学生在前端学习中发送给AI助手的一条提示词，判断该提示词所体现的认知层级。
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层级定义：
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- L1 记忆：能背诵HTML标签语法（例："帮我写一个按钮"）
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- L2 理解：能解释flex布局原理（例："为什么这里不居中？"）
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- L3 应用：能独立搭建页面结构（例："用flex做导航栏，间距16px"）
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- L4 分析：能定位跨浏览器兼容性bug（例："Safari中margin失效，原因？"）
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- L5 评价：能对比并选择方案（例："对比Grid与Flex方案优劣"）
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- L6 创造：能设计并实现原创交互作品（例："设计夜间/日间切换效果"）
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只返回一个数字（1-6），不要解释。"""
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def _call_llm(content: str) -> int | None:
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"""Call LLM to classify a single message content. Returns level 1-6 or None."""
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try:
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client = OpenAI(
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api_key=settings.LLM_API_KEY,
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base_url=settings.LLM_BASE_URL,
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timeout=30.0,
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)
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response = client.chat.completions.create(
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model=settings.LLM_MODEL,
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messages=[
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{"role": "system", "content": CLASSIFY_SYSTEM_PROMPT},
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{"role": "user", "content": content},
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],
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max_tokens=10,
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stream=False,
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extra_body={"thinking": {"type": "disabled"}},
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)
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text = response.choices[0].message.content or ""
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match = re.search(r"[1-6]", text)
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if not match:
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logger.warning("classify: unexpected LLM response '%s'", text)
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return None
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return int(match.group())
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except Exception as e:
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logger.error("classify LLM call failed: %s", e)
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return None
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def classify_message(message_id: int) -> int | None:
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"""Classify a single user Message by ID. Returns level or None."""
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from prompt.models import Message
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try:
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msg = Message.objects.get(id=message_id, role="user")
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except Message.DoesNotExist:
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return None
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level = _call_llm(msg.content)
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if level is not None:
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Message.objects.filter(id=message_id).update(prompt_level=level)
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return level
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def classify_conversation_messages(conversation_id: UUID, force: bool = False) -> None:
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"""Classify all user messages in a conversation."""
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from prompt.models import Message
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qs = Message.objects.filter(conversation_id=conversation_id, role="user")
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if not force:
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qs = qs.filter(prompt_level__isnull=True)
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for msg in qs.order_by("created"):
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level = _call_llm(msg.content)
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if level is not None:
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Message.objects.filter(id=msg.id).update(prompt_level=level)
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time.sleep(0.3)
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def classify_messages_batch(message_ids: list) -> None:
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"""Classify a list of messages by ID."""
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for mid in message_ids:
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classify_message(mid)
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time.sleep(0.5)
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