chore: 将后端移动到 api/ 目录，准备合并为单仓库

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-26 16:01:25 +08:00
1 parent e172ffc0b3
commit 0591eda56f
@@ -0,0 +1,171 @@
+import re
+from django.conf import settings
+from openai import AsyncOpenAI
+
+
+SYSTEM_PROMPT = """你是一个网页生成助手。根据用户的需求描述，生成网页代码。
+
+规则：
+1. 使用一个 ```html 代码块返回所有代码
+2. HTML 代码只需要 body 内的内容，不需要完整的 HTML 文档结构
+3. CSS 样式写在 <style> 标签内，JavaScript 写在 <script> 标签内，都放在代码块里
+4. 用中文回复，先简要说明你做了什么，然后给出代码
+5. 在已有代码基础上修改时，返回完整的修改后代码，不要只返回片段
+6. 由于任何外部链接都被屏蔽，使用纯 HTML、CSS 和 JS 实现功能，不要依赖外部库
+
+输出格式示例（必须严格遵守，只用一个代码块）：
+
+好的，我为你创建了一个点击按钮变色的示例。
+
+```html
+<style>
+button { padding: 8px 16px; }
+</style>
+
+<button id="btn">点击我</button>
+
+<script>
+document.getElementById('btn').onclick = function() {
+  this.style.background = 'red';
+};
+</script>
+```"""
+
+GUIDANCE_SYSTEM_PROMPT = """你是一个提示词写作教练，帮助学生写出清晰、具体的网页需求描述。
+
+判断标准——满足以下条件视为"够好"：
+- 有明确主题（例如：登录页、计时器、商品卡片列表）
+- 至少包含以下一项具体描述：颜色/布局/风格、交互行为（按钮点击、动画等）、页面内容（文字、数量、图标）
+
+判断为"不够好"的典型情况：
+- 目标太泛，如"做一个好看的页面"
+- 只有主题，完全没有任何视觉、交互或内容描述
+
+规则：
+1. 如果提示词不够好，用 1-2 个启发性问题引导学生补充细节，不要直接给出答案
+2. 如果提示词已经够好，以 [READY] 开头回复，简短夸奖学生并说明可以生成了
+3. 用中文回复，语气鼓励，简洁明了
+4. 使用 Markdown 语法高亮关键词，优先突出 **主题**、**视觉**、**交互**、**内容**、**可以生成** 等重点
+5. 如果回复以 [READY] 开头，[READY] 不要加粗，必须保持原始文本
+6. 不要生成任何代码"""
+
+DEFAULT_MODEL = "deepseek-v4-flash"
+DEEPSEEK_THINKING_MODEL = "deepseek-v4-flash-thinking"
+MODEL_ALIASES = {
+    DEEPSEEK_THINKING_MODEL: DEFAULT_MODEL,
+}
+NON_THINKING_MODELS = {"deepseek-v4-flash"}
+NON_THINKING_EXTRA_BODY = {"thinking": {"type": "disabled"}}
+
+# Models served by the ARK (Volcengine) endpoint
+ARK_MODELS = {"doubao-seed-2-0-lite-260215"}
+
+
+def _get_client(model: str) -> tuple[AsyncOpenAI, str]:
+    """Return (client, model_id) for the given model name."""
+    requested_model = model or DEFAULT_MODEL
+    resolved_model = MODEL_ALIASES.get(requested_model, requested_model)
+    if resolved_model in ARK_MODELS:
+        return (
+            AsyncOpenAI(
+                api_key=settings.ARK_API_KEY,
+                base_url=settings.ARK_BASE_URL,
+                timeout=120.0,
+            ),
+            resolved_model,
+        )
+    return (
+        AsyncOpenAI(
+            api_key=settings.LLM_API_KEY,
+            base_url=settings.LLM_BASE_URL,
+            timeout=120.0,
+        ),
+        resolved_model,
+    )
+
+
+def _should_disable_thinking(requested_model: str, resolved_model: str) -> bool:
+    return (
+        resolved_model in NON_THINKING_MODELS
+        and requested_model not in MODEL_ALIASES
+    )
+
+
+def _chat_completion_kwargs(
+    requested_model: str,
+    resolved_model: str,
+    messages: list[dict],
+    stream: bool,
+) -> dict:
+    kwargs = {
+        "model": resolved_model,
+        "messages": messages,
+        "stream": stream,
+    }
+    if _should_disable_thinking(requested_model, resolved_model):
+        kwargs["extra_body"] = NON_THINKING_EXTRA_BODY
+    return kwargs
+
+
+async def _stream_completion(messages: list[dict], model: str = ""):
+    client, resolved_model = _get_client(model)
+    requested_model = model or DEFAULT_MODEL
+    async with client as c:
+        stream = await c.chat.completions.create(
+            **_chat_completion_kwargs(requested_model, resolved_model, messages, stream=True),
+        )
+        async for chunk in stream:
+            delta = chunk.choices[0].delta
+            if delta.content:
+                yield delta.content
+
+
+async def stream_chat(history: list[dict], model: str = ""):
+    """Stream chat completion from the LLM. Yields content chunks."""
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}, *history]
+    async for chunk in _stream_completion(messages, model):
+        yield chunk
+
+
+def extract_code(text: str) -> dict:
+    """Extract code from AI response. Supports single HTML block (new) or separate html/css/js blocks (legacy)."""
+    result = {"html": None, "css": None, "js": None}
+    pattern = r"```(html|css|js|javascript|typescript|ts|jsx|tsx)\s*\n(.*?)```"
+    matches = re.findall(pattern, text, re.DOTALL | re.IGNORECASE)
+    for lang, code in matches:
+        lang = lang.lower()
+        if lang in ("javascript", "typescript", "ts", "jsx", "tsx"):
+            lang = "js"
+        if lang in result and result[lang] is None:
+            result[lang] = code.strip()
+
+    # Single HTML block: extract <style>/<script> contents and strip them from html
+    if result["html"] and result["css"] is None and result["js"] is None:
+        html = result["html"]
+
+        style_match = re.search(r"<style[^>]*>(.*?)</style>", html, re.DOTALL | re.IGNORECASE)
+        if style_match:
+            result["css"] = style_match.group(1).strip()
+            html = re.sub(r"<style[^>]*>.*?</style>", "", html, flags=re.DOTALL | re.IGNORECASE)
+
+        script_match = re.search(r"<script[^>]*>(.*?)</script>", html, re.DOTALL | re.IGNORECASE)
+        if script_match:
+            result["js"] = script_match.group(1).strip()
+            html = re.sub(r"<script[^>]*>.*?</script>", "", html, flags=re.DOTALL | re.IGNORECASE)
+
+        result["html"] = html.strip()
+
+    return result
+
+
+def parse_guidance_response(full_response: str) -> tuple[str, bool]:
+    if full_response.startswith("[READY]"):
+        return full_response[len("[READY]"):].lstrip("\n"), True
+    return full_response, False
+
+
+async def stream_guidance(history: list[dict]):
+    """Stream guidance coaching response. Yields content chunks."""
+    messages = [{"role": "system", "content": GUIDANCE_SYSTEM_PROMPT}, *history]
+    async for chunk in _stream_completion(messages):
+        yield chunk