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feat: refine V2 description engine with V1 priority and deep link analysis

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Nubenetes Bot
2026-05-15 18:38:48 +02:00
parent fc2d67fb32
commit 4a7d68eff8
3 changed files with 23 additions and 9 deletions
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1
GEMINI.md
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@@ -126,4 +126,5 @@ The bot must rotate between profiles to avoid detection:
- **Automatic Detection**: The `agentic_v2_builder.yml` workflow now triggers automatically whenever `docs/` changes or after a curation run.
- **Cost Efficiency**: Only NEW links from V1 are sent to Gemini. Existing links use cached AI evaluations but are locally "upgraded" with real-time GitHub metadata (stars/dates) and dynamic maturity tagging.
- **Maturity Taxonomy**: Replaced generic labels with a professional 5-tier system (`[DE FACTO STANDARD]`, `[ENTERPRISE-STABLE]`, `[EMERGING]`, `[LEGACY]`, `[GUIDE]`) explained in the V2 Index.
- **Mandatory Descriptions**: Every resource in V2 MUST have a description. If the V1 source is missing one, the Optimizer uses Gemini to generate a professional 1-2 sentence summary and caches it.
- **Manual Control**: The workflow supports a `force_reevaluate` flag for full architectural refreshes.

1
README.md
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@@ -200,6 +200,7 @@ To maintain the high-density quality of V2 without redundant AI costs, the `V2Vi
2. **Dynamic "Upgrading"**: Even for cached links, the engine performs real-time local updates:
- **GitHub Metadata**: Fetches live star counts and last-commit dates via the GitHub API to ensure chronological accuracy.
- **Maturity Tagging**: Applies a sophisticated 5-tier taxonomy (De Facto Standard, Enterprise Stable, Emerging, Legacy, Guide) based on live data.
- **Mandatory AI Descriptions**: Ensures 100% description coverage. If a link in V1 lacks a description, the engine automatically generates a professional summary using Gemini.
3. **Flat Routing**: Both versions use `use_directory_urls: false` to ensure relative asset paths (`images/`) remain stable across all sub-pages.
### Comparison Matrix

30
src/v2_optimizer.py
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@@ -36,10 +36,14 @@ class V2VisionEngine:
"PHASE 1: TECHNICAL PRESERVATION (HIGH INCLUSIVITY)\n"
"- KEEP >90% of technical resources.\n"
"PHASE 2: SOPHISTICATED SYNTHESIS & DATING\n"
"- Extract precise PUBLICATION YEAR: Look for dates in the URL (e.g., /2023/05/ post dates), Twitter/X post dates, or text context. Return 'N/A' if truly unknown, do NOT guess '2024'.\n"
"- Extract precise PUBLICATION YEAR: Look for dates in the URL, Twitter/X post dates, or text context. Return 'N/A' if truly unknown.\n"
"- Assign QUALITY level (1-3 stars).\n"
"- Assign a MATURITY TAG based on content type/status: '[ENTERPRISE-STABLE]', '[EMERGING / INNOVATION]', '[ARCHITECTURE-GUIDE]', '[TOOLING]', '[CASE-STUDY]', or '[CHEATSHEET]'.\n"
" * Note: We will override tags for GitHub repos using real API data (Stars/Commits), so focus on classifying blogs and articles correctly.\n"
"- Assign a MATURITY TAG based on content type/status.\n"
"PHASE 3: MANDATORY DESCRIPTIONS (V1 PRIORITY)\n"
"- If 'Current Desc' is already provided and descriptive, DO NOT CHANGE IT.\n"
"- If 'Current Desc' is empty, too short, or non-descriptive, generate a professional 1-2 sentence summary.\n"
"- To generate the summary: Analyze the URL and title. If you recognize the technical resource, describe its core value proposition for a Cloud Architect in 2026.\n"
"- Style: Technical, neutral, and informative. Language: English only.\n"
)
self.cache = self._load_cache()
@@ -219,9 +223,14 @@ class V2VisionEngine:
item = l.copy()
if not force_eval and url in self.cache and "stars" in self.cache[url]:
item.update(self.cache[url])
else:
# If cache has a generated description and item is missing one, use it
if "ai_summary" in self.cache[url] and not item["description"]:
item["description"] = self.cache[url]["ai_summary"]
# Re-evaluate if description is still missing even after cache check
if not item.get("description"):
to_evaluate.append(item)
continue # process later via API
continue
# Re-apply GitHub metadata and mature tagging for cached items
if "github.com" in url:
@@ -239,12 +248,12 @@ class V2VisionEngine:
for i in range(0, len(to_evaluate), BATCH_SIZE):
batch = to_evaluate[i:i+BATCH_SIZE]
batch_num = i//BATCH_SIZE + 1
log_event(f" [>] Processing Batch {batch_num} with AI...")
log_event(f" [>] Processing Batch {batch_num} with AI (Mandatory Descriptions)...")
prompt = (
f"{self.library_criteria}\n"
"Respond ONLY with a JSON object: {\"results\": [{\"idx\": int, \"year\": \"YYYY\", \"stars\": int, \"is_video\": bool, \"tag\": \"[TAG]\"}, ...]}\n\n"
"LINKS:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']})" for idx, l in enumerate(batch)])
"Respond ONLY with a JSON object: {\"results\": [{\"idx\": int, \"year\": \"YYYY\", \"stars\": int, \"is_video\": bool, \"tag\": \"[TAG]\", \"summary\": \"1-2 sentences description\"}, ...]}\n\n"
"LINKS:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']}) - Current Desc: {l['description'][:50]}" for idx, l in enumerate(batch)])
)
try:
@@ -260,9 +269,12 @@ class V2VisionEngine:
"year": str(res.get("year", "N/A")),
"stars": min(max(int(res.get("stars", 1)), 1), 3),
"is_video": res.get("is_video", False),
"tag": res.get("tag", "[ENTERPRISE-STABLE]")
"tag": res.get("tag", "[ENTERPRISE-STABLE]"),
"ai_summary": res.get("summary", "")
}
item.update(eval_data)
if not item["description"] and item["ai_summary"]:
item["description"] = item["ai_summary"]
# GitHub overrides
if "github.com" in item["url"]: