vornith stability: auto-retry+reload, k=2 prompt budget, budget-sized warm ping, web auto-retry; revert keep_alive
This commit is contained in:
16
app.py
16
app.py
@@ -68,10 +68,12 @@ def api_page():
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@app.route("/health")
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def health():
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# No generation probe here: a tiny probe prompt on the shared resident instance
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# is the vornith corruption trigger. Model presence via /api/tags (no GPU work).
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try:
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r = http.post(f"{CFG['ollama_url']}/api/generate", stream=True, timeout=4,
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json={"model": CFG["model"], "prompt": "ping", "stream": True, "options": {"num_predict": 1}})
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llm = r.status_code == 200
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r = http.get(f"{CFG['ollama_url']}/api/tags", timeout=4)
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models = [m.get("name") for m in r.json().get("models", [])]
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llm = CFG["model"] in models
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except Exception:
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llm = False
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return jsonify(status="ok", llm=llm, model=CFG["model"], **core.get_stats())
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@@ -122,6 +124,14 @@ def chat():
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return Response(generate(), mimetype="text/event-stream",
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
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@app.route("/api/warm")
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def api_warm():
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"""Heartbeat target for the systemd timer; also a manual health-probe for the model lane."""
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healthy, sample = core.warm_model()
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if not healthy and "CUDA" not in sample:
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core.unload_model() # corrupt instance: drop it; next warm reloads clean
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return jsonify(healthy=healthy, sample=sample, model=CFG["model"])
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@app.route("/api/remaining")
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def remaining():
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ip = core.client_ip(request)
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@@ -1,8 +1,9 @@
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{
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"model": "ornith-1.5:9b-64k",
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"model": "vornith:latest",
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"ollama_url": "http://10.30.20.29:11434",
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"num_ctx": 8192,
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"rag_k": 6,
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"rag_k": 2,
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"num_predict": 1100,
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"port": 8012,
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"mcp_port": 8012,
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"base_url": "https://draco.thetempleofdoom.com",
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6
deploy/draco-warm.service
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6
deploy/draco-warm.service
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@@ -0,0 +1,6 @@
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[Unit]
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Description=DRACO model keep-warm ping
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[Service]
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Type=oneshot
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ExecStart=/usr/bin/curl -s -m 130 http://127.0.0.1:8012/api/warm
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9
deploy/draco-warm.timer
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9
deploy/draco-warm.timer
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@@ -0,0 +1,9 @@
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[Unit]
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Description=DRACO keep model warm every 4 minutes
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[Timer]
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OnBootSec=60
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OnUnitActiveSec=240
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[Install]
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WantedBy=timers.target
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@@ -5,7 +5,7 @@ After=network.target
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[Service]
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Type=simple
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WorkingDirectory=/opt/draco
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ExecStart=/opt/draco/venv/bin/gunicorn -w 2 --threads 8 -b 127.0.0.1:8012 --timeout 300 app:app
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ExecStart=/opt/draco/venv/bin/gunicorn -w 2 --threads 8 -b 127.0.0.1:8012 --timeout 300 --graceful-timeout 10 app:app
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Restart=always
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RestartSec=5
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Environment=PYTHONUNBUFFERED=1
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@@ -20,6 +20,7 @@ CREATE TABLE IF NOT EXISTS users (
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api_key VARCHAR(64) UNIQUE NOT NULL,
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credits INTEGER DEFAULT 0,
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free_used INTEGER DEFAULT 0,
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total_calls INTEGER DEFAULT 0,
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is_admin INTEGER DEFAULT 0,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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last_used_at TIMESTAMP
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@@ -58,6 +59,11 @@ def db():
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def init_db():
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c = db()
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c.executescript(SCHEMA)
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try: # in-place migration for DBs created before total_calls existed
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c.execute("SELECT total_calls FROM users LIMIT 1")
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except sqlite3.OperationalError:
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c.execute("ALTER TABLE users ADD COLUMN total_calls INTEGER DEFAULT 0")
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c.commit()
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c.execute("INSERT OR IGNORE INTO users (email, api_key, credits, is_admin) VALUES (?,?,?,1)",
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("admin@draco.local", CFG["admin_key"], 999999))
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c.commit()
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@@ -127,12 +133,17 @@ SYSTEM = (
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"Answer directly with no reasoning preamble and no meta commentary."
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)
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def build_prompt(question, k=6):
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def build_prompt(question, k=None):
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"""RAG prompt. PROMPT BUDGET RULE: all prompts must land in the same size class
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(~600 tok). vornith (linear-attn+MTP hybrid) corrupts when one resident instance
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receives mixed tiny/long prompts; uniform bounded prompts are proven stable."""
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k = k or CFG.get("rag_k", 2)
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k = min(k, 3) # hard cap: 3 x 1200-char excerpts ~= clean-zone prompt
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hits = search_library(question, k=k)
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if hits:
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blocks = []
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for n, h in enumerate(hits, 1):
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blocks.append(f"[{n}] {h['title']} ({h['category']})\n{h['text'][:1400]}")
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blocks.append(f"[{n}] {h['title']} ({h['category']})\n{h['text'][:1200]}")
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ctx = "\n\n".join(blocks)
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prompt = f"{SYSTEM}\n\nBOOK EXCERPTS:\n{ctx}\n\nQUESTION: {question}\n\nANSWER (cite [n]):"
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else:
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@@ -314,6 +325,9 @@ def stream_ollama(prompt):
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"""Yield (channel, piece) tuples: channel 'think' or 'answer'."""
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r = http.post(f"{CFG['ollama_url']}/api/generate",
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json={"model": CFG["model"], "prompt": prompt, "stream": True,
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# RESIDENT model: cold-load prefill >1k tokens CUDA-crashes on this
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# hybrid arch. Corruption (????? output) is handled by detection +
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# auto-reload in the app layer instead.
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"options": {"temperature": 0.4, "num_predict": CFG.get("num_predict", 1100),
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"num_ctx": CFG.get("num_ctx", 8192)}},
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timeout=(5, None), stream=True)
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@@ -328,24 +342,55 @@ def stream_ollama(prompt):
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yield channel, piece
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def ask_ollama(prompt):
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"""Non-streaming RAG answer (reasoning stripped, tagged or not)."""
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r = http.post(f"{CFG['ollama_url']}/api/generate",
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json={"model": CFG["model"], "prompt": prompt, "stream": False,
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"options": {"temperature": 0.4, "num_predict": CFG.get("num_predict", 1100),
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"num_ctx": CFG.get("num_ctx", 8192)}},
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timeout=180)
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r.raise_for_status()
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_, answer = strip_cot(r.json().get("response", ""))
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if degenerate(answer):
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unload_model()
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raise RuntimeError("model returned degenerate output — model unloaded, retry")
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return answer
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"""Non-streaming RAG answer with auto-recovery: on degenerate/CUDA failure,
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unload the model, reload fresh, retry once before surfacing an error."""
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last_err = None
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for attempt in (1, 2):
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try:
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r = http.post(f"{CFG['ollama_url']}/api/generate",
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json={"model": CFG["model"], "prompt": prompt, "stream": False,
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"options": {"temperature": 0.4, "num_predict": CFG.get("num_predict", 1100),
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"num_ctx": CFG.get("num_ctx", 8192)}},
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timeout=180)
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r.raise_for_status()
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raw = r.json().get("response", "")
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if raw and degenerate(raw):
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last_err = RuntimeError("degenerate output")
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else:
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_, answer = strip_cot(raw)
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return answer
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except http.HTTPError as e:
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last_err = e
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except Exception as e:
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last_err = e
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unload_model() # force clean reload for the next attempt
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time.sleep(2)
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raise RuntimeError(f"model unstable after retry: {last_err}")
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def degenerate(s):
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"""vornith VRAM/session corruption signature: run of '?' chars."""
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s = s.strip()
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return len(s) >= 40 and s.count("?") / len(s) > 0.4
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def warm_model():
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"""Budget-sized heartbeat: keeps the model resident AND exercised at the canonical
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prompt size. Tiny prompts (<50 tok) on the shared instance are the corruption
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trigger, so the ping itself must be RAG-sized."""
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try:
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excerpt = ("The utility of a uniform prompt budget is that the model never "
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"encounters a context-length distribution shift between requests. " * 9)
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prompt = (f"{SYSTEM}\n\nBOOK EXCERPTS:\n[1] Warmup Excerpt (maintenance)\n{excerpt}"
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"\n\nQUESTION: Reply with exactly: ok\n\nANSWER (cite [n]):")
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r = http.post(f"{CFG['ollama_url']}/api/generate",
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json={"model": CFG["model"], "prompt": prompt,
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"stream": False, "options": {"num_predict": 4}},
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timeout=120)
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sample = r.json().get("response", "") if r.status_code == 200 else ""
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healthy = r.status_code == 200 and not degenerate(sample)
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return healthy, sample[:120]
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except Exception as e:
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return False, str(e)[:120]
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def unload_model():
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"""Drop the model from VRAM so the next request reloads clean."""
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try:
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59
pages.py
59
pages.py
@@ -140,33 +140,40 @@ async function ask(text){
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add('you','user').textContent=text;
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const body=add('draco','assistant');body.innerHTML='<span class=cursor></span>';
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const srcs=document.createElement('div');srcs.className='srcs';srcs.style.display='none';
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try{
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const r=await fetch('/api/chat',{method:'POST',headers:{'Content-Type':'application/json'},
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body:JSON.stringify({q:text})});
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if(!r.ok){const t=await r.text();let m='HTTP '+r.status;try{m=JSON.parse(t).error||m}catch(e){}
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body.textContent='⚠ '+m;busy=false;go.disabled=false;return;}
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const reader=r.body.getReader(),dec=new TextDecoder();let buf='',acc='',thinkEl=null;
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while(true){const{value,done}=await reader.read();if(done)break;
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buf+=dec.decode(value,{stream:true});const lines=buf.split('\\n');buf=lines.pop()||'';
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for(const line of lines){if(!line.startsWith('data: '))continue;
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const ev=JSON.parse(line.slice(6));
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if(ev.type==='sources'&&ev.sources.length){
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srcs.style.display='block';
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srcs.textContent='📚 '+ev.sources.map(s=>'['+(s.n)+'] '+s.title).join(' · ');
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}else if(ev.type==='token'&&ev.channel==='think'){
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if(!thinkEl){thinkEl=document.createElement('div');thinkEl.className='thinking';
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thinkEl.innerHTML="<span class='who'>◈ thinking </span><span class='tbody'></span>";
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body.parentNode.insertBefore(thinkEl,body);}
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thinkEl.querySelector('.tbody').textContent+=ev.text;
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log.scrollTop=log.scrollHeight;
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}else if(ev.type==='token'){acc+=ev.text;body.innerHTML='';
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body.appendChild(document.createTextNode(acc));body.appendChild(document.createElement('span')).className='cursor';
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log.scrollTop=log.scrollHeight;
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}else if(ev.type==='error'){acc+='\n⚠ '+ev.text;}
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}}
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}catch(e){acc+='\n⚠ connection lost';}
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body.innerHTML='';body.appendChild(document.createTextNode(acc||'(no output)'));
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for(let attempt=1;attempt<=3;attempt++){
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let hadTokens=false,hadError=null,acc='';
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try{
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const r=await fetch('/api/chat',{method:'POST',headers:{'Content-Type':'application/json'},
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body:JSON.stringify({q:text})});
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if(!r.ok){const t=await r.text();let m='HTTP '+r.status;try{m=JSON.parse(t).error||m}catch(e){}
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body.textContent='⚠ '+m;busy=false;go.disabled=false;return;}
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const reader=r.body.getReader(),dec=new TextDecoder();let buf='',thinkEl=null;
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while(true){const{value,done}=await reader.read();if(done)break;
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buf+=dec.decode(value,{stream:true});const lines=buf.split('\\n');buf=lines.pop()||'';
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for(const line of lines){if(!line.startsWith('data: '))continue;
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const ev=JSON.parse(line.slice(6));
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if(ev.type==='sources'&&ev.sources.length){
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srcs.style.display='block';
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srcs.textContent='📚 '+ev.sources.map(s=>'['+(s.n)+'] '+s.title).join(' · ');
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}else if(ev.type==='token'&&ev.channel==='think'){
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if(!thinkEl){thinkEl=document.createElement('div');thinkEl.className='thinking';
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thinkEl.innerHTML="<span class='who'>◈ thinking </span><span class='tbody'></span>";
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body.parentNode.insertBefore(thinkEl,body);}
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thinkEl.querySelector('.tbody').textContent+=ev.text;
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log.scrollTop=log.scrollHeight;
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}else if(ev.type==='token'){acc+=ev.text;hadTokens=true;body.innerHTML='';
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body.appendChild(document.createTextNode(acc));body.appendChild(document.createElement('span')).className='cursor';
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log.scrollTop=log.scrollHeight;
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}else if(ev.type==='error'){hadError=ev.text;}
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}}
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}catch(e){hadError='connection lost';}
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if(hadTokens&&!hadError){break;}
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if(attempt<3){body.innerHTML='<span class=cursor></span>';await new Promise(r=>setTimeout(r,1500));}
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else if(hadError){body.textContent='⚠ '+hadError;}
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else break;
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}
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const cur=body.querySelector('.cursor');if(cur)cur.remove();
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if(!body.textContent.trim())body.textContent='(no output)';
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body.appendChild(srcs);
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const left=document.getElementById('left');
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fetch('/api/remaining').then(r=>r.json()).then(d=>left.textContent=d.remaining+' free questions left today').catch(()=>{});
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