完善前端

backend/scripts/mock_model.py

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+"""开发档 Mock 模型服务：OpenAI 兼容 /chat/completions SSE 流式回声。
+
+仅供本地联调 Run 流水线（事件序列、芽体渲染、对账），不模拟任何真实模型能力。
+生产部署不启动本服务（与 /v1/webhooks/v1/payments/mock 同一开发档惯例）。
+
+启动：uv run python scripts/mock_model.py （127.0.0.1:8001）
+鉴权：任意 Bearer 均接受（联调端点，只监听回环地址）。
+"""
+
+import asyncio
+import json
+
+from fastapi import FastAPI, Request
+from fastapi.responses import StreamingResponse
+
+app = FastAPI(title="Hunjing Dev Mock Model", docs_url=None)
+
+REPLY = (
+    "收到了。按当前镜域的状态，我先把这件事拆成两步：先核对读书会的偏好记忆，"
+    "再给出一份可归真的结果。这个回复由开发档 Mock 模型逐字流出，"
+    "用于验证 content.block.delta 事件序列、临时芽体渲染与 message.completed 对账。"
+)
+
+
+@app.get("/healthz")
+async def healthz() -> dict[str, str]:
+    return {"status": "ok", "env": "development", "mock": "true"}
+
+
+@app.post("/v1/chat/completions")
+async def chat_completions(request: Request) -> StreamingResponse:
+    body = await request.json()
+    user_text = ""
+    for m in reversed(body.get("messages", [])):
+        if m.get("role") == "user" and m.get("content"):
+            user_text = str(m["content"])[-60:]
+            break
+
+    text = f"（mock 回应 · 你说：「{user_text}」）\n{REPLY}"
+
+    async def gen():
+        # 逐字流出，模拟真实 TTFT 与节奏
+        for ch in text:
+            await asyncio.sleep(0.02)
+            yield (
+                "data: "
+                + json.dumps(
+                    {"choices": [{"index": 0, "delta": {"content": ch}, "finish_reason": None}]},
+                    ensure_ascii=False,
+                )
+                + "\n\n"
+            )
+        yield (
+            "data: "
+            + json.dumps(
+                {"choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]},
+                ensure_ascii=False,
+            )
+            + "\n\n"
+        )
+        yield (
+            "data: "
+            + json.dumps(
+                {
+                    "choices": [],
+                    "usage": {
+                        "prompt_tokens": 32,
+                        "completion_tokens": len(text),
+                        "total_tokens": 32 + len(text),
+                    },
+                },
+                ensure_ascii=False,
+            )
+            + "\n\n"
+        )
+        yield "data: [DONE]\n\n"
+
+    return StreamingResponse(gen(), media_type="text/event-stream")