## Plan: Fix Fast Mode 20-Second Delay in `/api/interpret/click/tun`

### Problem
After `{"type": "generating", "source": "fast"}`, the user waits ~20 seconds with no SSE events before any `token` starts streaming. Root causes:
1. `deepseek-v4-pro` has high TTFT (prompt evaluation ~15-30s)
2. Reasoning tokens (`reasoning_content`) silently discarded — only `chunk.content` checked
3. No heartbeat/progress events during LLM wait (unlike review pipeline)

### Changes (all in `api/routes.py`)

**File: `api/routes.py`**

---

### Change 1 — `_get_chat_model()`: Switch fast mode to flash model + add timeout

Modify the function to:
- Accept `fast_mode: bool = True` parameter  
- When `fast_mode=True`, read `SUMMARY_MODEL` env var (`deepseek-v4-flash`) instead of `DEEPAGENT_MODEL` (`deepseek-v4-pro`)
- Add `timeout=180` and `max_retries=2` to `init_chat_model()` call (matching `vent_agent.py` pattern)

**Rationale**: `deepseek-v4-flash` is designed for low latency; fast mode just fills a fixed template and doesn't need the heavyweight reasoning of the pro model. This alone could reduce TTFT from ~20s to ~2-5s.

---

### Change 2 — `_fast_tun_sse_generator()`: Add heartbeat + reasoning_content handling

Replace lines 413-417 (the simple `async for chunk in model.astream(messages)` loop) with:

```python
import asyncio, time

first_token = False
heartbeat_start = time.time()
astream_iter = model.astream(messages)

while True:
    try:
        if first_token:
            chunk = await astream_iter.__anext__()
        else:
            # 3-second timeout for first token — send heartbeat if slow
            chunk = await asyncio.wait_for(astream_iter.__anext__(), timeout=3.0)
            if not first_token:
                first_token = True
    except asyncio.TimeoutError:
        elapsed = int(time.time() - heartbeat_start)
        yield f"data: {json.dumps({'type': 'progress', 'message': f'模型正在生成报告（已等待 {elapsed} 秒）...'}, ensure_ascii=False)}\n\n"
        continue
    except StopAsyncIteration:
        break

    # Handle reasoning content (R1-style models)
    reasoning = chunk.additional_kwargs.get("reasoning_content", "") if hasattr(chunk, "additional_kwargs") else ""
    if reasoning:
        yield f"data: {json.dumps({'type': 'reasoning', 'content': reasoning}, ensure_ascii=False)}\n\n"
        continue  # don't save reasoning to full_response

    content = chunk.content if hasattr(chunk, "content") else ""
    if content and isinstance(content, str):
        full_response += content
        yield f"data: {json.dumps({'type': 'token', 'source': 'fast', 'content': content}, ensure_ascii=False)}\n\n"
```

The `asyncio` and `time` imports are already available in the module.

---

### Change 3 — `_fast_device_sse_generator()`: Same fix

Apply the identical heartbeat + reasoning_content pattern (lines 468-472).

---

### Change 4 — Update callers of `_get_chat_model()`

Both `_fast_tun_sse_generator` (line 397) and `_fast_device_sse_generator` call `_get_chat_model()` — update to `_get_chat_model(fast_mode=True)` to use the flash model.

---

### Summary

| Fix | Impact | Risk |
|-----|--------|------|
| Flash model for fast mode | TTFT: ~20s → ~2-5s | Output quality may differ (fast mode is template-filling, so low risk) |
| Heartbeat during LLM wait | No more dead silence; progress events every 3s | None (pattern already proven in `review_agent.py`) |
| Stream reasoning_content | Immediate feedback if model "thinks" | None (new event type `reasoning`; frontend can ignore) |
| Add timeout to model init | Prevents hung connections | None (matches `vent_agent.py` pattern) |