reactor.py: fix Ollama silent-empty-return bug (no error checking on API response), bump Ollama timeout 30s->90s (cold model loads took 15s+ alone), add exception type to fallback log lines

deploy/reactor.py

@@ -144,12 +144,18 @@ async def llm_call(messages: list, provider: str = "claude", system: str = "") -
     for p in order:
         try:
             if p == "claude" and CLAUDE_API_KEY:
-                return await _claude_call(messages, system)
+                result = await _claude_call(messages, system)
             elif p == "groq" and GROQ_API_KEY:
-                return await _groq_call(messages, system)
+                result = await _groq_call(messages, system)
             elif p == "ollama":
-                return await _ollama_call(messages, system)
+                result = await _ollama_call(messages, system)
+            else:
+                continue
+            if not result or not result.strip():
+                raise RuntimeError(f"{p} returned empty content")
+            return result
         except Exception as e:
+            log.warning(f"[LLM] Provider {p} failed: {type(e).__name__}: {e}")
             last_err = e
             continue
     raise RuntimeError(f"All LLM providers failed (last error: {last_err})")
@@ -183,8 +189,14 @@ async def _ollama_call(messages: list, system: str = "") -> str:
     prompt = (system + "\n\n" if system else "") + "\n".join(f"{m['role'].upper()}: {m['content']}" for m in messages)
     async with aiohttp.ClientSession() as session:
         async with session.post(f"{OLLAMA_HOST}/api/generate", json={"model": OLLAMA_MODEL, "prompt": prompt, "stream": False},
-                                timeout=aiohttp.ClientTimeout(total=30)) as resp:
+                                timeout=aiohttp.ClientTimeout(total=90)) as resp:  # bumped from 30s 2026-07-07: cold model loads on this CPU-only host alone took 15s+ in testing, leaving no room for actual generation
             data = await resp.json()
+            # Fixed 2026-07-07: this had no error checking at all — if the model wasn't
+            # pulled (or any other Ollama-side error), the API returns {"error": "..."}
+            # with no "response" key, and this silently returned "" instead of raising,
+            # which fooled llm_call()'s fallback into treating it as a real success.
+            if "error" in data:
+                raise RuntimeError(f"Ollama error: {data['error']}")
             return data.get("response", "")