fix ollama

README.md

@@ -186,7 +186,7 @@ stored under `graylog_stream_profiles` in `state/fgai-config.json`.
 The Settings page also shows recommended stream profiles built from observed
 field coverage and cardinality. These recommendations use deterministic
 discovery first, then can optionally be refined by a local Ollama profile advisor
-model such as `qwen2.5:3b-instruct` (default) or a larger model on more capable
+model such as `qwen3:8b` (default) or a smaller model on constrained
 hardware. Enable `Ollama profile advisor` and set
 `Profile advisor model` in Settings. Advisor output must be valid JSON and is
 validated against fields actually seen in the stream before it can be applied.
@@ -672,27 +672,37 @@ fgai suggest-blocks --logs logs/fg_syslog.jsonl --execute --expiry-minutes 60
 Optional local LLM summary through Ollama:
 
 ```bash
-ollama pull qwen2.5:7b-instruct
+ollama pull llama3.1
 fgai analyze-logs --logs logs/fg_syslog.jsonl --llm --llm-timeout 300
 ```
 
-For slower machines, pick a smaller model instead of raising the timeout:
+For slower machines or a memory-constrained GPU, pick a smaller model instead
+of just raising the timeout:
 
 ```bash
+ollama pull qwen2.5:3b-instruct
 OLLAMA_MODEL=qwen2.5:3b-instruct OLLAMA_TIMEOUT=300 fgai analyze-logs --logs logs/fg_syslog.jsonl --llm
 ```
 
-**Sizing the model to your GPU**: the defaults (`qwen2.5:7b-instruct` for the
-dashboard assessment, `qwen2.5:3b-instruct` for the profile advisor) target a
-6-8 GB VRAM card at Q4 quantization with some headroom for context. On a
-memory-constrained card (6 GB or less, e.g. a mobile/low-power GPU), drop both
-to `qwen2.5:3b-instruct` or smaller — tight VRAM causes Ollama to spill onto
-CPU, which shows up as slow or timed-out assessments rather than an obvious
-out-of-memory error. On a workstation-class GPU with 16 GB+ VRAM, a larger
-model such as `qwen3:14b` for the dashboard assessment will give noticeably
-better analysis quality. The profile advisor only ever classifies a handful
-of ambiguous fields per call (see the Graylog MCP section below), so it stays
-small and fast even on constrained hardware without much quality loss.
+**Sizing the model to your GPU**: the defaults (`llama3.1`, 8B, for the
+dashboard assessment; `qwen3:8b` for the profile advisor) are a reasonable
+general-purpose choice, but on a memory-constrained card (6 GB VRAM or less,
+e.g. a mobile/low-power GPU) they can leave little headroom for context,
+which shows up as slow or timed-out assessments rather than an obvious
+out-of-memory error. If that happens, switch to a smaller model such as
+`qwen2.5:3b-instruct` for both settings. On a workstation-class GPU with
+16 GB+ VRAM, a larger model such as `qwen3:14b` for the dashboard assessment
+will give noticeably better analysis quality. The profile advisor only ever
+classifies a handful of ambiguous fields per call (see the Graylog MCP
+section below), so it can stay small even on constrained hardware without
+much quality loss.
+
+**Whichever model you choose, `ollama pull` it first** — Settings just holds
+a model name string, and the dashboard doesn't pull anything on your behalf.
+Pointing `Ollama model`/`Profile advisor model` at a tag you haven't pulled
+locally makes every assessment fail silently rather than an obvious error.
+Check `ollama list` (or the dashboard's own "Installed Ollama Models" panel
+in Settings) before changing either setting.
 
 ## Optional FortiGate Input
 
@@ -736,7 +746,7 @@ end
 - `FORTIGATE_VERIFY_TLS`: `true` or `false`, defaults to `true`.
 - `FGAI_ALLOWLIST`: comma-separated IPs/CIDRs never to block.
 - `OLLAMA_HOST`: defaults to `http://127.0.0.1:11434`.
-- `OLLAMA_MODEL`: defaults to `qwen2.5:7b-instruct`. Size this to your GPU's VRAM — see the sizing note above.
+- `OLLAMA_MODEL`: defaults to `llama3.1`. Size this to your GPU's VRAM — see the sizing note above.
 - `OLLAMA_TIMEOUT`: Ollama request timeout in seconds, defaults to `180`.
 - `FGAI_LLM`: set to `1` to enable dashboard Ollama analyst notes.
 - `FGAI_LLM_INTERVAL`: seconds between dashboard LLM notes, defaults to `300`.

src/fgai/config.py

@@ -28,7 +28,7 @@ DEFAULT_CONFIG: dict[str, object] = {
     "llm_model": "",
     "llm_timeout": 180,
     "profile_advisor_enabled": False,
-    "profile_advisor_model": "qwen2.5:3b-instruct",
+    "profile_advisor_model": "qwen3:8b",
     "profile_advisor_timeout": 240,
     "profile_advisor_interval_seconds": 300,
     "field_confidence_threshold": 60,

src/fgai/llm.py

@@ -41,7 +41,7 @@ def ollama_summary(
     timeout: int | None = None,
 ) -> str:
     host = os.getenv("OLLAMA_HOST", "http://127.0.0.1:11434").rstrip("/")
-    selected_model = model or os.getenv("OLLAMA_MODEL", "qwen2.5:7b-instruct")
+    selected_model = model or os.getenv("OLLAMA_MODEL", "llama3.1")
     selected_timeout = timeout or int(os.getenv("OLLAMA_TIMEOUT", "180"))
     prompt = {
         "analysis": analysis or {},
@@ -378,7 +378,7 @@ def ollama_profile_advice(
     ambiguous_fields_by_stream: dict[str, list[str]] | None = None,
 ) -> list[dict[str, object]]:
     host = os.getenv("OLLAMA_HOST", "http://127.0.0.1:11434").rstrip("/")
-    selected_model = model or os.getenv("FGAI_PROFILE_ADVISOR_MODEL", "qwen2.5:3b-instruct")
+    selected_model = model or os.getenv("FGAI_PROFILE_ADVISOR_MODEL", "qwen3:8b")
     selected_timeout = timeout or int(os.getenv("FGAI_PROFILE_ADVISOR_TIMEOUT", "240"))
     scoped = bool(ambiguous_fields_by_stream)
     compact = [