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feat: implement high-density multi-line summaries for V2 elite portal

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Nubenetes Bot
2026-05-19 21:05:45 +02:00
parent 6e55af1c9c
commit 5264ba4dff
2 changed files with 19 additions and 8 deletions
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8
src/agentic_curator.py
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@@ -121,9 +121,13 @@ async def evaluate_extracted_assets(raw_assets: List[Dict]) -> Dict[str, Dict]:
"- If the community (Reddit, Hacker News) reports the tool as 'unstable', 'abandoned', or 'vaporware', set reputation_penalty: true.\n"
"PHASE 2: LINGUISTIC DIVERSITY & CLASSIFICATION\n"
"- Identify TECHNICAL_HIERARCHY: List (max 10 strings) Area > Topic > Subtopics.\n"
"PHASE 3: MULTI-DIMENSIONAL TAGGING\n"
"PHASE 3: HIGH-DENSITY TECHNICAL SUMMARIES (Mandate 4)\n"
"- Provide an 'en_summary' that is technical, professional and dense.\n"
"- Include architectural value, key features, and technical significance. Style: O'Reilly technical.\n"
"- Format: Use paragraphs and bullet points if necessary. Aim for 2-5 sentences of depth.\n"
"PHASE 4: MULTI-DIMENSIONAL TAGGING\n"
"- Assign 1 to 3 tags from: [DE FACTO STANDARD], [ENTERPRISE-STABLE], [EMERGING], [GUIDE], [CASE STUDY], [COMMUNITY-TOOL], [LEGACY].\n"
"Respond ONLY JSON list: [{\"url\": \"...\", \"impact_score\": int, \"reputation_penalty\": bool, \"reputation_summary\": \"...\", \"pub_date\": \"YYYY-MM-DD\", \"primary_category\": \"...\", \"title\": \"...\", \"desc\": \"...\", \"en_summary\": \"...\", \"language\": \"...\", \"type\": \"...\", \"level\": \"...\", \"technical_hierarchy\": [...], \"tags\": [...], \"is_microservice\": bool}, ...]\n\n"
"Respond ONLY JSON list: [{\"url\": \"...\", \"impact_score\": int, \"reputation_penalty\": bool, \"reputation_summary\": \"...\", \"pub_date\": \"YYYY-MM-DD\", \"primary_category\": \"...\", \"title\": \"...\", \"desc\": \"...\", \"en_summary\": \"High-density summary...\", \"language\": \"...\", \"type\": \"...\", \"level\": \"...\", \"technical_hierarchy\": [...], \"tags\": [...], \"is_microservice\": bool}, ...]\n\n"
"RESOURCES:\n" + "\n".join([f"- {d['asset']['url']}: (MVQ Penalty: {d['mvq_penalty']}) {d['content']}" for d in batch_data])
)

19
src/v2_optimizer.py
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@@ -45,8 +45,10 @@ class V2VisionEngine:
"- For 'introduction.md', identify links related to MICROSERVICES for extraction.\n"
"PHASE 3: KNOWLEDGE ASSIMILATION FLOW\n"
"- Order hierarchy to facilitate a structured learning journey.\n"
"PHASE 4: MANDATORY DESCRIPTIONS\n"
"- If 'Current Desc' is empty, generate a professional summary. Style: O'Reilly technical.\n"
"PHASE 4: HIGH-DENSITY TECHNICAL SUMMARIES (Mandate 4)\n"
"- Generate professional, neutral, and advanced technical summaries. Style: O'Reilly technical.\n"
"- Summaries MUST be high-density: Include architectural value, key features, and technical significance.\n"
"- Format: Use paragraphs and bullet points for complex tools. Aim for 2-5 sentences of depth.\n"
"PHASE 5: ADVANCED MATURITY TAGGING\n"
"- Assign 1 to 3 tags from: [DE FACTO STANDARD], [ENTERPRISE-STABLE], [EMERGING], [GUIDE], [CASE STUDY], [COMMUNITY-TOOL], [LEGACY].\n"
)
@@ -252,14 +254,15 @@ class V2VisionEngine:
for i in range(0, len(to_evaluate), BATCH_SIZE):
batch = to_evaluate[i:i+BATCH_SIZE]
# Analyst Prompt (Focus: Classification & Summary)
# Analyst Prompt (Focus: Classification & High-Density Summary)
prompt = (
f"You are the Nubenetes Technical Analyst (2026).\n"
f"{dynamic_mandates}\n"
f"{self.library_criteria}\n"
"PHASE 5: INITIAL TAGGING\n"
"PHASE 5: INITIAL TAGGING & RICH SUMMARY\n"
"- Assign 1 to 3 preliminary tags.\n"
"Respond ONLY JSON: {{\"results\": [{{ \"idx\": int, \"year\": \"YYYY\", \"stars\": 0-5, \"hierarchy\": [\"Area\", \"Topic\", ...], \"tags\": [\"...\"], \"summary\": \"...\", \"language\": \"...\", \"type\": \"...\", \"complexity\": \"...\", \"is_microservice\": bool }}, ...]}}\n\n"
"- Provide a 'summary' that is technical and dense. Use Markdown (bullet points, bolding) if necessary.\n"
"Respond ONLY JSON: {{\"results\": [{{ \"idx\": int, \"year\": \"YYYY\", \"stars\": 0-5, \"hierarchy\": [\"Area\", \"Topic\", ...], \"tags\": [\"...\"], \"summary\": \"High-density multi-line summary...\", \"language\": \"...\", \"type\": \"...\", \"complexity\": \"...\", \"is_microservice\": bool }}, ...]}}\n\n"
"LINKS:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']})" for idx, l in enumerate(batch)])
)
try:
@@ -566,7 +569,11 @@ class V2VisionEngine:
link_text = f"**{title}**"
md += f" - {year_prefix}[{link_text}]({l['url']}){icon}{gh_info}{lang_tag}{level_tag}{type_tag}{rich} {'🌟'*raw_stars}{tag_html}\n"
if l.get('ai_summary'): md += f"\n {l['ai_summary']}\n\n"
# Mandate 4: Render High-Density Summary with proper indentation (6 spaces for Markdown list nesting)
summary = l.get('ai_summary', l.get('description', ''))
if summary:
indented_summary = "\n".join([f" {line}" if line.strip() else "" for line in summary.strip().split("\n")])
md += f"\n{indented_summary}\n\n"
# Add Semantic "See Also" for related categories within the same Dimension
related = [f"[{data[f]['title']}](./{f})" for f in data if f != f_name and data[f]["dim"] == info["dim"]]