github/awesome-kubernetes
1
0
Fork 0
mirror of https://github.com/nubenetes/awesome-kubernetes.git synced 2026-08-19 04:16:26 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: add AI re-evaluation staleness detection, cross-dimension See Also, and last_ai_eval tracking

Browse Source 
- Add last_ai_eval timestamp to eval_data in both fast-track and grounded-track
  evaluation paths, plus SQL schema and curator
- Entries enriched >6 months ago are automatically flagged for re-evaluation
  instead of being skipped (stale content detection)
- Enhance "See Also" links with cross-dimension references based on shared
  tags between pages, in addition to same-dimension related links
- Add _collect_tags_from_tree() helper for recursive tag extraction

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Nubenetes Bot
2026-06-19 00:45:57 +02:00
co-authored by Claude Sonnet 4.6
parent 1149a9c8f7
commit 782dc1bd06
3 changed files with 53 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/agentic_curator.py
+1
View File
@@ -210,6 +210,7 @@ async def evaluate_extracted_assets(raw_assets: List[Dict]) -> Dict[str, Dict]:
"source_provenance": d["asset"].get("source_type", "Social"), "social_preview_url": d["rich_meta"].get("og_image", ""),
"category": primary_cat, "status": "online", "last_checked": datetime.now().timestamp(),
"discovered_at": datetime.now(MADRID_TZ).isoformat(),
"last_ai_eval": datetime.now(MADRID_TZ).isoformat(),
"suggested_new_category": data.get("suggested_new_category", ""),
"addition_method": {
"rss": "rss_ingestion", "GitHub Trending": "github_trending",
src/inventory_manager.py
+2 -1
View File
@@ -116,6 +116,7 @@ def save_inventory(inv: Dict, shard_file: str = None):
last_checked REAL,
needs_ai_refresh BOOLEAN,
discovered_at TEXT,
last_ai_eval TEXT,
company TEXT,
geo_region TEXT,
hierarchy TEXT,
@@ -131,7 +132,7 @@ def save_inventory(inv: Dict, shard_file: str = None):
"url", "title", "description", "year", "stars", "ai_summary", "language",
"resource_type", "complexity", "is_microservice", "status", "addition_method",
"content_hash", "health_score", "last_checked", "needs_ai_refresh",
"discovered_at", "company", "geo_region",
"discovered_at", "last_ai_eval", "company", "geo_region",
"hierarchy", "tags", "v1_locations", "v2_locations", "youtube_mosaic", "extra_metadata"
]
src/v2_optimizer.py
+50 -7
View File
@@ -373,7 +373,15 @@ class V2VisionEngine:
if is_special: item["is_special"] = True
# Mandate 30: Hierarchy and AI Summaries are mandatory for ELITE AI curation.
# Optimized Skip Logic: Only skip if we already have BOTH hierarchy and a summary.
if ((cached.get("hierarchy") and cached.get("ai_summary")) or self.render_only) and not force_eval:
last_eval = cached.get("last_ai_eval", "")
eval_stale = False
if last_eval and isinstance(last_eval, str) and len(last_eval) >= 10:
try:
eval_age = (datetime.now(MADRID_TZ) - datetime.fromisoformat(last_eval)).days
eval_stale = eval_age > 180
except Exception:
pass
if ((cached.get("hierarchy") and cached.get("ai_summary") and not eval_stale) or self.render_only) and not force_eval:
if project_id not in project_registry or item.get("stars", 0) > project_registry[project_id].get("stars", 0):
if project_id in project_registry and project_registry[project_id].get("is_special"): item["is_special"] = True
project_registry[project_id] = item
@@ -442,7 +450,8 @@ class V2VisionEngine:
"resource_type": res.get("type", "Reference"), "complexity": res.get("complexity", "Intermediate"),
"hierarchy": res.get("hierarchy", ["General"]), "tags": res.get("tags", []),
"is_microservice": bool(res.get("is_microservice", False)),
"status": "online", "is_special": item.get("is_special", False)
"status": "online", "is_special": item.get("is_special", False),
"last_ai_eval": datetime.now(MADRID_TZ).isoformat()
}
existing_entry = self.inventory.get(normalize_url(item["url"]), {})
if existing_entry.get("discovered_at"):
@@ -531,7 +540,8 @@ class V2VisionEngine:
"resource_type": res.get("type", "Reference"), "complexity": res.get("complexity", "Intermediate"),
"hierarchy": res.get("hierarchy", ["General"]), "tags": res.get("tags", []),
"is_microservice": bool(res.get("is_microservice", False)),
"status": "online", "is_special": item.get("is_special", False)
"status": "online", "is_special": item.get("is_special", False),
"last_ai_eval": datetime.now(MADRID_TZ).isoformat()
}
existing_entry = self.inventory.get(normalize_url(item["url"]), {})
if existing_entry.get("discovered_at"):
@@ -752,6 +762,19 @@ class V2VisionEngine:
return v2_structure
def _collect_tags_from_tree(self, node: Dict) -> List[Set]:
"""Recursively collect maturity/tech tags from a content tree for cross-referencing."""
results = []
if "__links__" in node:
for link in node["__links__"]:
tags = set(link.get("tags", []))
if tags:
results.append(tags)
for key, val in node.items():
if key != "__links__" and isinstance(val, dict):
results.extend(self._collect_tags_from_tree(val))
return results
async def _generate_comparison_table(self, links: List[Dict]) -> str:
standard_tools = [l for l in links if l.get("stars", 0) >= 3]
if len(standard_tools) < 8: return ""
@@ -1294,10 +1317,30 @@ class V2VisionEngine:
md += await render_node(info["content"], -1, f_name.replace(".md", ""), used_headers, is_intro=(f_name=="introduction.md" or f_name=="about.md"))
# Add Semantic "See Also" ONLY ONCE at the end of the page
related = [f"[{data[f]['title']}](./{f})" for f in data if f != f_name and data[f]["dim"] == info["dim"]]
if related:
md += f"\n---\n💡 **Explore Related:** {' | '.join(related[:3])}\n\n"
# Add Semantic "See Also" — same dimension + cross-dimension by shared tags
same_dim = [f for f in data if f != f_name and data[f]["dim"] == info["dim"]]
cross_dim = []
if info.get("content") and isinstance(info["content"], dict):
page_tags = set()
for node_links in self._collect_tags_from_tree(info["content"]):
page_tags.update(node_links)
if page_tags:
for f in data:
if f != f_name and data[f]["dim"] != info["dim"]:
other_tags = set()
if isinstance(data[f].get("content"), dict):
for t in self._collect_tags_from_tree(data[f]["content"]):
other_tags.update(t)
if page_tags & other_tags:
cross_dim.append(f)
related = [f"[{data[f]['title']}](./{f})" for f in same_dim[:3]]
cross = [f"[{data[f]['title']}](./{f})" for f in cross_dim[:2]]
if related or cross:
md += "\n---\n"
if related:
md += f"💡 **Explore Related:** {' | '.join(related)}\n\n"
if cross:
md += f"🔗 **See Also:** {' | '.join(cross)}\n\n"
# Smart Write: Only update disk if content changed
target_path = os.path.join(V2_DIR, f_name)