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fix: resolve markdown linting errors and optimize V2 workflow performance

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Nubenetes Bot
2026-05-20 17:57:00 +02:00
parent 4f67b351dc
commit c31bcb23ea
4 changed files with 46 additions and 27 deletions
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10
src/gemini_utils.py
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@@ -310,11 +310,16 @@ async def call_gemini_with_retry(prompt: str, response_format: str = "json", max
for key_offset in range(total_keys):
current_idx = (CURRENT_KEY_INDEX + key_offset) % total_keys
api_key = GEMINI_API_KEYS[current_idx]
key_label = GEMINI_API_KEYS_DATA[current_idx]["label"]
async with httpx.AsyncClient() as client:
for model in models:
# Limit the number of models to try per key to avoid excessive timeouts
for model in models[:5]:
if THROTTLED_MODELS.get(f"{current_idx}_{model}", 0) > time.time():
continue
# Mandate 13: Detailed tracing for long-running workflows
# log_event(f" [AI] Attempt: Key {current_idx+1} ({key_label}) | Model: {model}")
full_model_name = f"models/{model}"
api_url = f"https://generativelanguage.googleapis.com/{GEMINI_API_VERSION}/{full_model_name}:generateContent?key={api_key}"
@@ -325,7 +330,7 @@ async def call_gemini_with_retry(prompt: str, response_format: str = "json", max
"contents": [{"parts": [{"text": prompt}]}],
"tools": [{"google_search_retrieval": {}}] if use_grounding else []
}
response = await client.post(api_url, json=payload, timeout=50)
response = await client.post(api_url, json=payload, timeout=60)
resp_json = {}
try: resp_json = response.json()
@@ -335,6 +340,7 @@ async def call_gemini_with_retry(prompt: str, response_format: str = "json", max
SESSION_TRACKER.track_call(current_idx, model, response.status_code, usage, role=role)
if response.status_code == 200:
log_event(f" [AI] Success: Key {current_idx+1} | Model: {model} | Role: {role}")
CURRENT_KEY_INDEX = current_idx
if 'candidates' in resp_json and resp_json['candidates']:
text_resp = resp_json['candidates'][0]['content']['parts'][0]['text']

48
src/v2_optimizer.py
View File

@@ -292,25 +292,32 @@ class V2VisionEngine:
# 1. Has AI summary (previous run)
# 2. Is GitHub and has stars (metadata present)
# 3. Has decent manual description (> 40 chars)
# 4. Is already in inventory (we have title/category context)
has_ai_summary = l.get("ai_summary") is not None and len(l.get("ai_summary")) > 50
has_stars = l.get("gh_stars") is not None
has_desc = len(l.get("description", "")) > 40
if has_ai_summary or has_stars or has_desc:
is_known = nu in self.inventory
if has_ai_summary or has_stars or has_desc or is_known:
fast_track.append(l)
else:
# Grounded-Track is ONLY for "Unknown" resources with zero context
grounded_track.append(l)
log_event(f"[*] Agent Phase 1: Analyst Evaluation ({len(to_evaluate)} resources)...")
log_event(f" [>] Fast-Track: {len(fast_track)} | Grounded-Track: {len(grounded_track)}")
analyst_results = []
# 1.1 Fast-Track: Large Batches, NO GROUNDING (Fast)
BATCH_SIZE_FAST = 25
for i in range(0, len(fast_track), BATCH_SIZE_FAST):
BATCH_SIZE_FAST = 40 # Increased from 25
total_fast = len(fast_track)
for i in range(0, total_fast, BATCH_SIZE_FAST):
batch = fast_track[i:i+BATCH_SIZE_FAST]
batch_num = (i // BATCH_SIZE_FAST) + 1
total_batches = (total_fast + BATCH_SIZE_FAST - 1) // BATCH_SIZE_FAST
log_event(f" [>] Fast-Track: Processing Batch {batch_num}/{total_batches}...")
prompt = (
f"You are the Nubenetes Technical Analyst (2026).\n"
f"{dynamic_mandates}\n"
@@ -328,7 +335,7 @@ class V2VisionEngine:
item = batch[idx].copy()
eval_data = {
"year": str(res.get("year", "N/A")), "stars": min(max(int(res.get("stars", 0)), 0), 5),
"ai_summary": res.get("summary", item.get("ai_summary", "")),
"ai_summary": res.get("summary", item.get("ai_summary", "")),
"language": res.get("language", "English"),
"resource_type": res.get("type", "Reference"), "complexity": res.get("complexity", "Intermediate"),
"hierarchy": res.get("hierarchy", ["General"]), "tags": res.get("tags", []),
@@ -337,14 +344,19 @@ class V2VisionEngine:
}
item.update(eval_data)
analyst_results.append(item)
except:
except:
for l in batch: analyst_results.append(l)
await asyncio.sleep(0.5)
await asyncio.sleep(0.5)
# 1.2 Grounded-Track: Small Batches, WITH GROUNDING (Slower but precise)
BATCH_SIZE_GROUNDED = 5 # Reduced batch for grounding to avoid 429
for i in range(0, len(grounded_track), BATCH_SIZE_GROUNDED):
BATCH_SIZE_GROUNDED = 15 # Increased from 5
total_grounded = len(grounded_track)
for i in range(0, total_grounded, BATCH_SIZE_GROUNDED):
batch = grounded_track[i:i+BATCH_SIZE_GROUNDED]
batch_num = (i // BATCH_SIZE_GROUNDED) + 1
total_batches = (total_grounded + BATCH_SIZE_GROUNDED - 1) // BATCH_SIZE_GROUNDED
log_event(f" [🌟] Grounded-Track: Processing Batch {batch_num}/{total_batches} (Grounding active)...")
prompt = (
f"You are the Nubenetes Technical Analyst (2026).\n"
f"{dynamic_mandates}\n"
@@ -370,11 +382,9 @@ class V2VisionEngine:
}
item.update(eval_data)
analyst_results.append(item)
except:
except:
for l in batch: analyst_results.append(l)
await asyncio.sleep(5.0) # Grounding is very expensive in terms of quota
# --- AGENT PHASE 2: SELECTIVE AUDIT (MCP-Grounded) ---
await asyncio.sleep(2.0) # Reduced from 5.0 to improve throughput # --- AGENT PHASE 2: SELECTIVE AUDIT (MCP-Grounded) ---
# Identify candidates for high-trust verification
audit_candidates = [l for l in analyst_results if "[DE FACTO STANDARD]" in l.get("tags", []) or "[ENTERPRISE-STABLE]" in l.get("tags", [])]
@@ -562,7 +572,7 @@ class V2VisionEngine:
async def _write_premium_files(self, data: Dict[str, Dict], mosaic_html: str, videos_html: str):
# 1. Update Index with Pulse
trending_pool = sorted([dict(meta, url=url) for url, meta in self.inventory.items() if isinstance(meta, dict) and meta.get("stars", 0) >= 4], key=lambda x: (x.get("pub_date", "0000"), -x.get("stars", 0)), reverse=True)
pulse_md = "## ⚡ The Agentic Pulse\n" + "\n".join([f"- **({l.get('pub_date', 'N/A')[:10]})** [**=={l['title']}==**]({l['url']}) {'🌟'*l.get('stars',3)}" for l in trending_pool[:5]])
pulse_md = "## The Agentic Pulse\n" + "\n".join([f"- **({l.get('pub_date', 'N/A')[:10]})** [**=={l['title']}==**]({l['url']}) {'🌟'*l.get('stars',3)}" for l in trending_pool[:5]])
index_md = (
"# Nubenetes Elite Portal (V2) | Nubenetes: Awesome Kubernetes & Cloud [![Awesome](https://cdn.jsdelivr.net/gh/sindresorhus/awesome@d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n\n"
@@ -594,7 +604,7 @@ class V2VisionEngine:
index_md += (
"\n***\n\n"
"## 💎 The Maturity Taxonomy\n\n"
"## The Maturity Taxonomy\n\n"
"To ensure industrial-grade precision, every resource in V2 is classified using our proprietary 5-tier maturity system:\n\n"
"| Tag | Description | Engineering Context |\n"
"| :--- | :--- | :--- |\n"
@@ -603,7 +613,7 @@ class V2VisionEngine:
"| **`[EMERGING]`** | The cutting edge. | High-potential tools and patterns (e.g., AI Agents, MCP) shaping the future. |\n"
"| **`[GUIDE]`** | Strategic knowledge. | High-quality tutorials, architectural deep-dives, and decision matrices. |\n"
"| **`[LEGACY]`** | Historical context. | Established tools that are being replaced or are primarily for maintaining older stacks. |\n\n"
"## 🌟 Technical Impact (Relevance Score)\n\n"
"## Technical Impact (Relevance Score)\n\n"
"The stars accompanying each resource represent its **Technical Impact** and **Architectural Relevance** for a 2026 Senior Architect:\n\n"
"| Impact | Level | Meaning | Visual Code |\n"
"| :---: | :--- | :--- | :--- |\n"