add timoute och export av fg config

src/fgai/cli.py

@@ -24,25 +24,24 @@ def analyze_logs(args: argparse.Namespace) -> int:
         min_score=args.min_score,
         allowlist=parse_allowlist(args.allowlist),
     )
-    _print_json(
-        {
-            "summary": summarize_events(events),
-            "diagnostics": {
-                "top_source_ips": top_field_values(events, "srcip", limit=10),
-                "top_services": top_field_values(events, "service", limit=10),
-                "top_actions": top_field_values(events, "action", limit=10),
-                "top_subtypes": top_field_values(events, "subtype", limit=10),
-                "local_in_failures": local_in_failures(events, limit=10),
-            },
-            "block_candidates": [
-                {"src_ip": candidate.src_ip, "score": candidate.score, "reasons": candidate.reasons}
-                for candidate in candidates
-            ],
-        }
-    )
+    analysis = {
+        "summary": summarize_events(events),
+        "diagnostics": {
+            "top_source_ips": top_field_values(events, "srcip", limit=10),
+            "top_services": top_field_values(events, "service", limit=10),
+            "top_actions": top_field_values(events, "action", limit=10),
+            "top_subtypes": top_field_values(events, "subtype", limit=10),
+            "local_in_failures": local_in_failures(events, limit=10),
+        },
+        "block_candidates": [
+            {"src_ip": candidate.src_ip, "score": candidate.score, "reasons": candidate.reasons}
+            for candidate in candidates
+        ],
+    }
+    _print_json(analysis)
     if args.llm:
         print("\nLLM summary:")
-        print(ollama_summary([], candidates, args.model))
+        print(ollama_summary([], candidates, args.model, analysis=analysis, timeout=args.llm_timeout))
     return 0
 
 
@@ -51,7 +50,7 @@ def audit_policy_file(args: argparse.Namespace) -> int:
     _print_json([finding.__dict__ for finding in findings])
     if args.llm:
         print("\nLLM summary:")
-        print(ollama_summary(findings, [], args.model))
+        print(ollama_summary(findings, [], args.model, timeout=args.llm_timeout))
     return 0
 
 
@@ -117,12 +116,14 @@ def build_parser() -> argparse.ArgumentParser:
     logs.add_argument("--allowlist", default=None, help="Comma-separated IPs/CIDRs never to block")
     logs.add_argument("--llm", action="store_true", help="Ask local Ollama to summarize results")
     logs.add_argument("--model", default=None, help="Ollama model name")
+    logs.add_argument("--llm-timeout", type=int, default=None, help="Ollama request timeout in seconds")
     logs.set_defaults(func=analyze_logs)
 
     policies = subparsers.add_parser("audit-policies", help="Audit FortiOS firewall policy config")
     policies.add_argument("--config", required=True, help="Path to FortiOS config backup")
     policies.add_argument("--llm", action="store_true", help="Ask local Ollama to summarize results")
     policies.add_argument("--model", default=None, help="Ollama model name")
+    policies.add_argument("--llm-timeout", type=int, default=None, help="Ollama request timeout in seconds")
     policies.set_defaults(func=audit_policy_file)
 
     blocks = subparsers.add_parser("suggest-blocks", help="Suggest or execute guarded source IP blocks")

src/fgai/llm.py

@@ -7,28 +7,42 @@ from urllib import request
 from .models import BlockCandidate, Finding
 
 
-def ollama_summary(findings: list[Finding], candidates: list[BlockCandidate], model: str | None = None) -> str:
+def ollama_summary(
+    findings: list[Finding],
+    candidates: list[BlockCandidate],
+    model: str | None = None,
+    *,
+    analysis: dict[str, object] | None = None,
+    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", "llama3.3")
+    selected_timeout = timeout or int(os.getenv("OLLAMA_TIMEOUT", "180"))
     prompt = {
+        "analysis": analysis or {},
         "findings": [finding.__dict__ for finding in findings],
         "block_candidates": [
             {"src_ip": candidate.src_ip, "score": candidate.score, "reasons": candidate.reasons}
-            for candidate in candidates
+            for candidate in candidates[:20]
         ],
     }
     body = json.dumps(
         {
             "model": selected_model,
             "stream": False,
+            "options": {
+                "num_predict": 350,
+                "temperature": 0.2,
+            },
             "prompt": (
                 "You are a local FortiGate security analyst. Summarize these policy findings "
-                "and UTM block candidates. Be concise, include risk, likely cause, and next action. "
+                "and log diagnostics. Be concise. Include risk, likely cause, and next action. "
+                "Do not recommend blocking private/internal client IPs unless the data explicitly proves compromise. "
                 f"Data: {json.dumps(prompt)}"
             ),
         }
     ).encode("utf-8")
     req = request.Request(f"{host}/api/generate", data=body, method="POST", headers={"Content-Type": "application/json"})
-    with request.urlopen(req, timeout=60) as response:
+    with request.urlopen(req, timeout=selected_timeout) as response:
         data = json.loads(response.read().decode("utf-8"))
     return str(data.get("response", "")).strip()

src/fgai/policies.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import json
 import shlex
 from pathlib import Path
 
@@ -56,8 +57,60 @@ def parse_policy_config(text: str) -> list[PolicyRule]:
     return policies
 
 
+def _normalize_policy_value(value: object) -> list[str]:
+    if value is None:
+        return []
+    if isinstance(value, list):
+        normalized: list[str] = []
+        for item in value:
+            if isinstance(item, dict):
+                if "name" in item:
+                    normalized.append(str(item["name"]))
+                elif "q_origin_key" in item:
+                    normalized.append(str(item["q_origin_key"]))
+                else:
+                    normalized.append(json.dumps(item, sort_keys=True))
+            else:
+                normalized.append(str(item))
+        return normalized
+    if isinstance(value, dict):
+        if "name" in value:
+            return [str(value["name"])]
+        if "q_origin_key" in value:
+            return [str(value["q_origin_key"])]
+        return [json.dumps(value, sort_keys=True)]
+    return [str(value)]
+
+
+def parse_policy_json(text: str) -> list[PolicyRule]:
+    data = json.loads(text)
+    raw_policies = data.get("results", data) if isinstance(data, dict) else data
+    if not isinstance(raw_policies, list):
+        raise ValueError("policy JSON must be a list or contain a results list")
+
+    policies: list[PolicyRule] = []
+    for index, raw_policy in enumerate(raw_policies, start=1):
+        if not isinstance(raw_policy, dict):
+            continue
+        policy_id = str(raw_policy.get("policyid", raw_policy.get("policy_id", raw_policy.get("id", index))))
+        settings = {str(key): _normalize_policy_value(value) for key, value in raw_policy.items()}
+        policies.append(PolicyRule(policy_id, settings))
+    return policies
+
+
 def read_policies(path: str | Path) -> list[PolicyRule]:
-    return parse_policy_config(Path(path).read_text(encoding="utf-8", errors="replace"))
+    policy_path = Path(path)
+    if not policy_path.exists():
+        raise FileNotFoundError(
+            f"{policy_path} does not exist. Fetch policies first with: "
+            f"fgai fetch-policies --output exports/policies.json"
+        )
+
+    text = policy_path.read_text(encoding="utf-8", errors="replace")
+    stripped = text.lstrip()
+    if stripped.startswith("{") or stripped.startswith("["):
+        return parse_policy_json(text)
+    return parse_policy_config(text)
 
 
 def audit_policies(policies: list[PolicyRule]) -> list[Finding]: