fix: cap MI50 summary length + fast-fail cloud fallback

The dream cycle's summarize_all ran uncapped against the MI50: no max_tokens
and no timeout, so the OpenAI SDK's 600s x2-retry default meant ~30 min per
call. Combined with summary.py's own retry loop, one unsummarizable session
pegged the GPU for hours (observed 2026-07-04: stuck since 23:02, nothing saved
since 00:56, 7-8k-token runaway generations, all 4 llama.cpp slots busy). Not
context overflow (0 shifts/truncations) - purely unbounded length on a slow
backend timing out and retrying.

- llm.complete(): add optional max_tokens (caps generation; num_predict for
  Ollama) and timeout (bounds the request and sets max_retries=0 so the caller
  owns retry policy). Both default None -> unchanged for every existing caller.
- summary.py: cap gists at 768 tokens, 150s/call fast-fail, 2 MI50 attempts
  then one cloud fallback (when primary isn't already cloud and a key exists).

Known limitation (scoped out per decision): the fallback triggers on
timeouts/exceptions, not on a degraded backend returning garbage as a 200.

Tests: fallback fires after 2 MI50 failures; no fallback when primary is cloud
or no key; cap+timeout threaded into every complete() call; llm bounds tests.
172 pass, ruff clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015yrEb5qpPGv2FjyxrB7LLk

lyra/llm.py

@@ -37,30 +37,45 @@ def _resolved_model(cfg, backend: Backend, model: str | None) -> str:
     return model or cfg.local_model
 
 
-def complete(messages: list[Message], backend: Backend = "local", model: str | None = None) -> str:
+def complete(messages: list[Message], backend: Backend = "local", model: str | None = None,
+             max_tokens: int | None = None, timeout: float | None = None) -> str:
     """Generate a completion. `model` overrides the backend's default model
-    (used so live chat can run a stronger cloud model than bulk consolidation)."""
+    (used so live chat can run a stronger cloud model than bulk consolidation).
+
+    `max_tokens` caps the generation length (guards a slow local model against
+    rambling for thousands of tokens). `timeout`, when set, bounds each request
+    and disables the SDK's own retries so the caller owns retry/fallback policy.
+    Both default to None → unchanged behavior for every existing caller."""
     cfg = load()
     mdl = _resolved_model(cfg, backend, model)
     logbus.log("info", "llm call", kind="complete", backend=backend, model=mdl, tok=_approx_tok(messages))
     t0 = time.monotonic()
 
-    if backend == "cloud":
-        if not cfg.openai_api_key:
-            raise RuntimeError("OPENAI_API_KEY is not set")
-        client = OpenAI(api_key=cfg.openai_api_key)
-        resp = client.chat.completions.create(model=mdl, messages=messages)
-        out = resp.choices[0].message.content or ""
-    elif backend == "mi50":
-        # MI50 box runs an OpenAI-compatible llama.cpp server; key is unused.
-        client = OpenAI(api_key="not-needed", base_url=cfg.mi50_base_url)
-        resp = client.chat.completions.create(model=mdl, messages=messages)
+    if backend in ("cloud", "mi50"):
+        if backend == "cloud":
+            if not cfg.openai_api_key:
+                raise RuntimeError("OPENAI_API_KEY is not set")
+            client_kwargs: dict = {"api_key": cfg.openai_api_key}
+        else:
+            # MI50 box runs an OpenAI-compatible llama.cpp server; key is unused.
+            client_kwargs = {"api_key": "not-needed", "base_url": cfg.mi50_base_url}
+        if timeout is not None:
+            client_kwargs["timeout"] = timeout
+            client_kwargs["max_retries"] = 0  # caller owns retries (see summary.py)
+        client = OpenAI(**client_kwargs)
+        create_kwargs: dict = {"model": mdl, "messages": messages}
+        if max_tokens is not None:
+            create_kwargs["max_tokens"] = max_tokens
+        resp = client.chat.completions.create(**create_kwargs)
         out = resp.choices[0].message.content or ""
     else:
+        payload: dict = {"model": mdl, "messages": messages, "stream": False}
+        if max_tokens is not None:
+            payload["options"] = {"num_predict": max_tokens}
         resp = httpx.post(
             f"{cfg.local_base_url}/api/chat",
-            json={"model": mdl, "messages": messages, "stream": False},
-            timeout=120,
+            json=payload,
+            timeout=timeout or 120,
         )
         resp.raise_for_status()
         out = resp.json()["message"]["content"]