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feat: implement 'Double-Evidence Synthesis' for grounded technical summaries

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Nubenetes Bot
2026-05-19 21:14:07 +02:00
parent 54b09838b5
commit 44e1340e7a
1 changed files with 18 additions and 13 deletions
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31
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: HIGH-DENSITY TECHNICAL SUMMARIES (Mandate 4)\n"
"PHASE 4: HIGH-DENSITY TECHNICAL SUMMARIES (Double-Evidence Synthesis)\n"
"- Generate professional, neutral, and advanced technical summaries. Style: O'Reilly technical.\n"
"- PROTOCOL: Contrast 'Curator Insight' (from source) with 'Live Grounding' (from search).\n"
"- If discrepancies are found (e.g. project is archived but source says it's new), PRIORITIZE live engineering truth.\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"
@@ -254,15 +256,15 @@ class V2VisionEngine:
for i in range(0, len(to_evaluate), BATCH_SIZE):
batch = to_evaluate[i:i+BATCH_SIZE]
# Analyst Prompt (Focus: Classification & High-Density Summary)
# Analyst Prompt (Focus: Classification & High-Density Synthesis)
prompt = (
f"You are the Nubenetes Technical Analyst (2026).\n"
f"{dynamic_mandates}\n"
f"{self.library_criteria}\n"
"PHASE 5: INITIAL TAGGING & RICH SUMMARY\n"
"- Assign 1 to 3 preliminary tags.\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"
"PHASE 5: DOUBLE-EVIDENCE SYNTHESIS & RICH SUMMARY\n"
"- Cross-reference the provided title/desc with your internal knowledge and search grounding.\n"
"- Provide a 'summary' that is technical and dense. Identify the core architectural 'WHY'.\n"
"Respond ONLY JSON: {{\"results\": [{{ \"idx\": int, \"year\": \"YYYY\", \"stars\": 0-5, \"hierarchy\": [\"Area\", \"Topic\", ...], \"tags\": [\"...\"], \"summary\": \"Synthesis of evidence...\", \"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:
@@ -297,12 +299,15 @@ class V2VisionEngine:
audit_prompt = (
f"You are the Nubenetes Auditor (2026).\n"
f"{dynamic_mandates}\n"
"MISSION: Verify the 'Elite' status of these resources using your GOOGLE_SEARCH tool.\n"
"MISSION: Perform 'Double-Evidence' verification using your GOOGLE_SEARCH tool.\n"
"PROTOCOL:\n"
"1. SEARCH: Look for community reputation (Reddit, HN) and repo status (GitHub).\n"
"2. CONTRAST: Compare findings with the proposed Analyst summary.\n"
"3. REFINE: Correct any 'vaporware' or 'hype' claims. Ensure technical accuracy.\n"
"CRITERIA:\n"
"- [DE FACTO STANDARD]: Industry baseline, used by everyone (e.g. K8s, Terraform).\n"
"- [ENTERPRISE-STABLE]: Proven, high-trust, supported (e.g. ArgoCD, Linkerd).\n"
"- REPUTATION: Check Reddit/Hacker News for stability or abandonment reports.\n"
"Respond ONLY JSON: {{\"audits\": [{{ \"idx\": int, \"verified_tags\": [\"...\"], \"reputation_summary\": \"...\", \"reputation_penalty\": bool }}, ...]}}\n\n"
"- [DE FACTO STANDARD]: Industry baseline, used by everyone.\n"
"- [ENTERPRISE-STABLE]: Proven, high-trust, supported.\n"
"Respond ONLY JSON: {{\"audits\": [{{ \"idx\": int, \"verified_tags\": [\"...\"], \"refined_summary\": \"Synthesized and verified technical summary...\", \"reputation_summary\": \"...\", \"reputation_penalty\": bool }}, ...]}}\n\n"
"RESOURCES TO AUDIT:\n" + "\n".join([f"{idx}. {l['title']} ({l['url']}) - Proposed: {l.get('tags')}" for idx, l in enumerate(batch)])
)
try:
@@ -311,9 +316,9 @@ class V2VisionEngine:
for aud in audit_data.get("audits", []):
idx = int(aud["idx"])
if idx < len(batch):
url = batch[idx]["url"]
# Update tags and add reputation metadata (Mandate 32/33)
# Update tags, summary and add reputation metadata (Mandate 32/33)
batch[idx]["tags"] = aud.get("verified_tags", batch[idx]["tags"])
if aud.get("refined_summary"): batch[idx]["ai_summary"] = aud["refined_summary"]
batch[idx]["reputation_summary"] = aud.get("reputation_summary", "")
if aud.get("reputation_penalty"):
batch[idx]["stars"] = max(batch[idx].get("stars", 1) - 1, 1)