add ollama bagcround run and cache

This commit is contained in:
larssand
2026-06-18 23:13:16 +02:00
parent 6117013c6e
commit 5e257b7d47
7 changed files with 120 additions and 1 deletions

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@@ -27,6 +27,14 @@ Or use the helper script, which creates/uses `.venv` automatically and runs `pip
- Continuous monitor writing `state/fgai-status.json` - Continuous monitor writing `state/fgai-status.json`
- Local dashboard at `http://127.0.0.1:8088` - Local dashboard at `http://127.0.0.1:8088`
Enable cached Ollama analyst notes in the dashboard:
```bash
FGAI_LLM=1 OLLAMA_MODEL=llama3.1 ./start.sh restart
```
The monitor refreshes deterministic detections every `FGAI_MONITOR_INTERVAL` seconds and refreshes the LLM note every `FGAI_LLM_INTERVAL` seconds, default `300`.
The script activates `.venv` inside the script process. If you also want your current shell prompt to show the venv, run: The script activates `.venv` inside the script process. If you also want your current shell prompt to show the venv, run:
```bash ```bash
@@ -182,6 +190,8 @@ end
- `OLLAMA_HOST`: defaults to `http://127.0.0.1:11434`. - `OLLAMA_HOST`: defaults to `http://127.0.0.1:11434`.
- `OLLAMA_MODEL`: defaults to `llama3.3`. - `OLLAMA_MODEL`: defaults to `llama3.3`.
- `OLLAMA_TIMEOUT`: Ollama request timeout in seconds, defaults to `180`. - `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`.
- `FGAI_THREAT_INTEL`: set to `1` to enable external threat intelligence lookups. - `FGAI_THREAT_INTEL`: set to `1` to enable external threat intelligence lookups.
- `ABUSEIPDB_API_KEY`: AbuseIPDB API key for public IP reputation enrichment. - `ABUSEIPDB_API_KEY`: AbuseIPDB API key for public IP reputation enrichment.
- `ABUSEIPDB_MAX_AGE_DAYS`: report age window for AbuseIPDB, defaults to `90`. - `ABUSEIPDB_MAX_AGE_DAYS`: report age window for AbuseIPDB, defaults to `90`.

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@@ -192,6 +192,10 @@ def run_monitor(args: argparse.Namespace) -> int:
policy_path=args.policies, policy_path=args.policies,
interval=args.interval, interval=args.interval,
anomaly_limit=args.anomaly_limit, anomaly_limit=args.anomaly_limit,
llm=args.llm,
llm_interval=args.llm_interval,
llm_model=args.model,
llm_timeout=args.llm_timeout,
) )
return 0 return 0
@@ -272,6 +276,10 @@ def build_parser() -> argparse.ArgumentParser:
monitor.add_argument("--policies", default=None, help="Optional FortiGate policy JSON/config file to audit continuously") monitor.add_argument("--policies", default=None, help="Optional FortiGate policy JSON/config file to audit continuously")
monitor.add_argument("--interval", type=int, default=10, help="Seconds between analysis runs") monitor.add_argument("--interval", type=int, default=10, help="Seconds between analysis runs")
monitor.add_argument("--anomaly-limit", type=int, default=20, help="Maximum anomaly findings to include") monitor.add_argument("--anomaly-limit", type=int, default=20, help="Maximum anomaly findings to include")
monitor.add_argument("--llm", action="store_true", help="Generate cached Ollama analyst note for dashboard")
monitor.add_argument("--llm-interval", type=int, default=300, help="Seconds between Ollama dashboard assessments")
monitor.add_argument("--model", default=None, help="Ollama model name")
monitor.add_argument("--llm-timeout", type=int, default=None, help="Ollama request timeout in seconds")
monitor.set_defaults(func=run_monitor) monitor.set_defaults(func=run_monitor)
dashboard = subparsers.add_parser("dashboard", help="Serve local fgAI dashboard") dashboard = subparsers.add_parser("dashboard", help="Serve local fgAI dashboard")

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@@ -46,6 +46,7 @@ HTML = """<!doctype html>
<section class="panel"><h2>Live Status</h2><div id="liveStatus" class="muted">Waiting for monitor data.</div></section> <section class="panel"><h2>Live Status</h2><div id="liveStatus" class="muted">Waiting for monitor data.</div></section>
</div> </div>
</section> </section>
<section class="panel"><h2>AI Assessment</h2><div id="llmAssessment" class="muted">LLM assessment disabled.</div></section>
<section class="panel"><h2>Anomalies</h2><div id="anomalies"></div></section> <section class="panel"><h2>Anomalies</h2><div id="anomalies"></div></section>
<section class="panel"><h2>Recommendations</h2><div id="recommendations"></div></section> <section class="panel"><h2>Recommendations</h2><div id="recommendations"></div></section>
<section class="panel"><h2>Block Candidates</h2><div id="blocks"></div></section> <section class="panel"><h2>Block Candidates</h2><div id="blocks"></div></section>
@@ -84,6 +85,9 @@ async function refresh() {
`Critical anomalies: ${esc((a.critical || 0))}`, `Critical anomalies: ${esc((a.critical || 0))}`,
`High anomalies: ${esc((a.high || 0))}` `High anomalies: ${esc((a.high || 0))}`
].join('<br>'); ].join('<br>');
const llm = data.llm_assessment || {};
const llmText = llm.text ? esc(llm.text).replace(/\\n/g, '<br>') : esc(llm.error || 'LLM assessment disabled or waiting for first run.');
document.getElementById('llmAssessment').innerHTML = `<div>Status: <code>${esc(llm.status || 'unknown')}</code></div><p>${llmText}</p>`;
document.getElementById('anomalies').innerHTML = table(data.anomalies || [], [ document.getElementById('anomalies').innerHTML = table(data.anomalies || [], [
{label:'Source', key:'subject'}, {label:'Source', key:'subject'},
{label:'Score', key:'score'}, {label:'Score', key:'score'},

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@@ -46,3 +46,28 @@ def ollama_summary(
with request.urlopen(req, timeout=selected_timeout) as response: with request.urlopen(req, timeout=selected_timeout) as response:
data = json.loads(response.read().decode("utf-8")) data = json.loads(response.read().decode("utf-8"))
return str(data.get("response", "")).strip() return str(data.get("response", "")).strip()
def ollama_dashboard_assessment(analysis: dict[str, object], model: str | None = None, timeout: int | None = None) -> str:
compact = {
"summary": analysis.get("summary", {}),
"anomaly_summary": analysis.get("anomaly_summary", {}),
"top_anomalies": analysis.get("anomalies", [])[:5],
"top_recommendations": analysis.get("recommendations", [])[:5],
"block_candidates": analysis.get("block_candidates", [])[:5],
"policy_findings": analysis.get("policy_findings", [])[:5],
}
return ollama_summary(
[],
[],
model,
analysis={
"task": (
"Write a concise dashboard analyst note for a FortiGate admin. "
"Explain likely cause, whether this looks malicious or noisy, and the next action. "
"Mention policyid=0 as implicit deny/drop, not an editable policy."
),
"data": compact,
},
timeout=timeout,
)

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@@ -5,6 +5,7 @@ import time
from pathlib import Path from pathlib import Path
from .anomaly import anomaly_summary, detect_source_anomalies from .anomaly import anomaly_summary, detect_source_anomalies
from .llm import ollama_dashboard_assessment
from .logs import local_in_failures, read_events, summarize_events, top_field_values from .logs import local_in_failures, read_events, summarize_events, top_field_values
from .mitigation import parse_allowlist, suggest_block_candidates from .mitigation import parse_allowlist, suggest_block_candidates
from .policies import audit_policies, read_policies from .policies import audit_policies, read_policies
@@ -94,6 +95,24 @@ def build_status(
} }
def add_llm_assessment(status: dict[str, object], *, previous: str | None = None, model: str | None = None, timeout: int | None = None) -> None:
try:
status["llm_assessment"] = {
"enabled": True,
"status": "ok",
"generated_at": int(time.time()),
"text": ollama_dashboard_assessment(status, model=model, timeout=timeout),
}
except Exception as exc:
status["llm_assessment"] = {
"enabled": True,
"status": "error",
"generated_at": int(time.time()),
"error": str(exc),
"text": previous or "",
}
def write_status(status: dict[str, object], output: str) -> None: def write_status(status: dict[str, object], output: str) -> None:
output_path = Path(output) output_path = Path(output)
output_path.parent.mkdir(parents=True, exist_ok=True) output_path.parent.mkdir(parents=True, exist_ok=True)
@@ -109,10 +128,33 @@ def monitor_loop(
policy_path: str | None = None, policy_path: str | None = None,
interval: int = 10, interval: int = 10,
anomaly_limit: int = 20, anomaly_limit: int = 20,
llm: bool = False,
llm_interval: int = 300,
llm_model: str | None = None,
llm_timeout: int | None = None,
) -> None: ) -> None:
print(f"Monitoring {log_path}") print(f"Monitoring {log_path}")
print(f"Writing status to {output}") print(f"Writing status to {output}")
last_llm_at = 0
last_llm_text: str | None = None
while True: while True:
status = build_status(log_path, policy_path=policy_path, anomaly_limit=anomaly_limit) status = build_status(log_path, policy_path=policy_path, anomaly_limit=anomaly_limit)
if llm:
now = int(time.time())
if now - last_llm_at >= llm_interval:
add_llm_assessment(status, previous=last_llm_text, model=llm_model, timeout=llm_timeout)
assessment = status.get("llm_assessment", {})
if isinstance(assessment, dict):
last_llm_text = str(assessment.get("text", "") or last_llm_text or "")
last_llm_at = now
else:
status["llm_assessment"] = {
"enabled": True,
"status": "cached",
"generated_at": last_llm_at,
"text": last_llm_text or "",
}
else:
status["llm_assessment"] = {"enabled": False, "status": "disabled", "text": ""}
write_status(status, output) write_status(status, output)
time.sleep(interval) time.sleep(interval)

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@@ -10,6 +10,10 @@ LISTENER_LOG="${FGAI_LISTENER_LOG:-$ROOT_DIR/logs/fgai-listener.log}"
POLICY_FILE="${FGAI_POLICY_FILE:-$ROOT_DIR/exports/policies.json}" POLICY_FILE="${FGAI_POLICY_FILE:-$ROOT_DIR/exports/policies.json}"
STATE_FILE="${FGAI_STATE_FILE:-$ROOT_DIR/state/fgai-status.json}" STATE_FILE="${FGAI_STATE_FILE:-$ROOT_DIR/state/fgai-status.json}"
MONITOR_INTERVAL="${FGAI_MONITOR_INTERVAL:-10}" MONITOR_INTERVAL="${FGAI_MONITOR_INTERVAL:-10}"
LLM_ENABLED="${FGAI_LLM:-0}"
LLM_INTERVAL="${FGAI_LLM_INTERVAL:-300}"
LLM_TIMEOUT="${OLLAMA_TIMEOUT:-180}"
LLM_MODEL="${OLLAMA_MODEL:-}"
DASHBOARD_HOST="${FGAI_DASHBOARD_HOST:-127.0.0.1}" DASHBOARD_HOST="${FGAI_DASHBOARD_HOST:-127.0.0.1}"
DASHBOARD_PORT="${FGAI_DASHBOARD_PORT:-8088}" DASHBOARD_PORT="${FGAI_DASHBOARD_PORT:-8088}"
LISTENER_PID_FILE="${FGAI_LISTENER_PID_FILE:-$ROOT_DIR/run/fgai-listener.pid}" LISTENER_PID_FILE="${FGAI_LISTENER_PID_FILE:-$ROOT_DIR/run/fgai-listener.pid}"
@@ -27,6 +31,7 @@ usage() {
printf ' FGAI_SYSLOG_FILE=%s\n' "$LOG_FILE" printf ' FGAI_SYSLOG_FILE=%s\n' "$LOG_FILE"
printf ' FGAI_LISTENER_LOG=%s\n' "$LISTENER_LOG" printf ' FGAI_LISTENER_LOG=%s\n' "$LISTENER_LOG"
printf ' FGAI_DASHBOARD_PORT=%s\n' "$DASHBOARD_PORT" printf ' FGAI_DASHBOARD_PORT=%s\n' "$DASHBOARD_PORT"
printf ' FGAI_LLM=%s\n' "$LLM_ENABLED"
} }
activate_venv_for_script() { activate_venv_for_script() {
@@ -109,6 +114,12 @@ start_monitor() {
if [ -f "$POLICY_FILE" ]; then if [ -f "$POLICY_FILE" ]; then
monitor_args+=(--policies "$POLICY_FILE") monitor_args+=(--policies "$POLICY_FILE")
fi fi
if [ "$LLM_ENABLED" = "1" ] || [ "$LLM_ENABLED" = "true" ]; then
monitor_args+=(--llm --llm-interval "$LLM_INTERVAL" --llm-timeout "$LLM_TIMEOUT")
if [ -n "$LLM_MODEL" ]; then
monitor_args+=(--model "$LLM_MODEL")
fi
fi
nohup "$VENV_DIR/bin/fgai" "${monitor_args[@]}" > "$MONITOR_LOG" 2>&1 & nohup "$VENV_DIR/bin/fgai" "${monitor_args[@]}" > "$MONITOR_LOG" 2>&1 &
printf '%s\n' "$!" > "$MONITOR_PID_FILE" printf '%s\n' "$!" > "$MONITOR_PID_FILE"

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@@ -1,8 +1,9 @@
import tempfile import tempfile
import unittest import unittest
from pathlib import Path from pathlib import Path
from unittest.mock import patch
from fgai.monitor import build_status, write_status from fgai.monitor import add_llm_assessment, build_status, write_status
class MonitorTests(unittest.TestCase): class MonitorTests(unittest.TestCase):
@@ -33,6 +34,24 @@ class MonitorTests(unittest.TestCase):
self.assertTrue(output.exists()) self.assertTrue(output.exists())
def test_add_llm_assessment_records_error_without_ollama(self):
status = {"summary": {}, "anomalies": []}
with patch("fgai.monitor.ollama_dashboard_assessment", side_effect=TimeoutError("timeout")):
add_llm_assessment(status, previous="old text")
self.assertEqual(status["llm_assessment"]["status"], "error")
self.assertEqual(status["llm_assessment"]["text"], "old text")
def test_add_llm_assessment_records_text(self):
status = {"summary": {}, "anomalies": []}
with patch("fgai.monitor.ollama_dashboard_assessment", return_value="looks noisy"):
add_llm_assessment(status)
self.assertEqual(status["llm_assessment"]["status"], "ok")
self.assertEqual(status["llm_assessment"]["text"], "looks noisy")
if __name__ == "__main__": if __name__ == "__main__":
unittest.main() unittest.main()