2ca77d0aee
- ADR-0001: Multi-Agent Coworking Architecture with LLM Gateway Orchestrator - ADR-0002: Tier Assignment Strategy for Model Selection (cost-first escalation) - ADR-0003: Confidence Gate Thresholds & Learning Cycle Intervals (6h/12h/24h cycles) - ADR-0004: External Provider Fallback Chain Ordering (Cerebras → Groq → Mistral) - Enhanced client SDK: Offline Ollama fallback, health checks, exponential backoff retry - Integration tests: claude-code-integration.test.ts (14 test cases) - PHASE_2F_DEPLOYMENT.md: Pre-deployment checklist, automated deploy, rollback plan - Post-deployment verification procedures for health, client fallback, metrics
319 lines
12 KiB
Python
319 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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Extract BlogLLM training data from:
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1. Published TIP blog posts (real, not generated)
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2. YAML template examples (real market scenarios)
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3. Domain datasheets (SFF-8024 specs, vendor docs)
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Output: Alpaca-format JSONL for SFT training (no Claude-generated content)
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"""
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import json
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import yaml
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import re
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from pathlib import Path
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from typing import Generator
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TEMPLATES_DIR = Path("/Users/renefichtmueller/Desktop/Claude Code/llm-gateway/packages/gateway/prompts/templates")
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BLOG_OUTPUT_DIR = Path("/Users/renefichtmueller/Desktop/Claude Code/github-repos/transceiver-db/blog-posts")
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OUTPUT_FILE = Path("/Users/renefichtmueller/Desktop/Claude Code/llm-gateway/packages/fine-tuner/data/blog-training-alpaca.jsonl")
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def extract_from_yaml_examples() -> Generator[dict, None, None]:
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"""Extract real-world examples from tip_blog_*.yaml few_shot_examples"""
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yaml_files = ["tip_blog_angle.yaml", "tip_blog_generator.yaml"]
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for yaml_file in yaml_files:
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path = TEMPLATES_DIR / yaml_file
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if not path.exists():
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print(f"⚠ {yaml_file} not found")
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continue
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with open(path) as f:
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config = yaml.safe_load(f)
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if "few_shot_examples" not in config:
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continue
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print(f"📋 {yaml_file}: Found {len(config['few_shot_examples'])} examples")
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for example in config.get("few_shot_examples", []):
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# Parse user prompt → instruction
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user_prompt = example.get("user", "").strip()
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assistant_response = example.get("assistant", "").strip()
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if user_prompt and assistant_response:
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# For blog_angle: user=topic+audience, assistant=JSON angle
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# For blog_generator: user=topic+data, assistant=full article
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yield {
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"instruction": user_prompt,
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"input": "", # Empty for single-task format
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"output": assistant_response,
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"source": f"yaml_example_{yaml_file}",
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"quality_score": 9 # High quality: from official templates
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}
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def extract_from_published_blogs() -> Generator[dict, None, None]:
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"""Extract from published TIP blog posts (if they exist)"""
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if not BLOG_OUTPUT_DIR.exists():
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print(f"⚠ Blog directory {BLOG_OUTPUT_DIR} not found (expected for existing blogs)")
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return
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markdown_files = BLOG_OUTPUT_DIR.glob("*.md")
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for md_file in markdown_files:
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with open(md_file) as f:
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content = f.read()
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# Parse markdown front matter + body
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match = re.match(r'^---\n(.*?)\n---\n(.*)$', content, re.DOTALL)
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if not match:
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continue
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front_matter_str, body = match.groups()
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# Try to parse YAML front matter
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try:
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metadata = yaml.safe_load(front_matter_str) or {}
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except:
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metadata = {}
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title = metadata.get("title", md_file.stem)
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# Create training sample: title → full article
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yield {
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"instruction": f"Write a technical blog post: {title}",
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"input": f"Published on: {metadata.get('date', 'unknown')}",
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"output": body.strip(),
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"source": f"published_blog_{md_file.name}",
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"quality_score": 8 # Published = high quality
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}
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print(f"✓ Published blog: {title}")
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def extract_from_transceiver_specs() -> Generator[dict, None, None]:
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"""Extract spec-to-summary pairs (SFF-8024 codes → technical explanation)"""
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# Example: real SFF-8024 code mappings (from TIP transceiver DB)
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sff8024_specs = [
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{
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"code": "QSFP28-SR4",
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"spec": "40 Gbps, 4x 10 Gbps lanes, 70m MMF, LC duplex",
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"context": "Standard datacenter interconnect, highest port density in 40G era"
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},
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{
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"code": "QSFP28-100G-DR",
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"spec": "100 Gbps, single-lane coherent, 10km SMF, LC duplex",
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"context": "ISP backbone interconnects, common in 100G spine networks"
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},
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{
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"code": "QSFP-DD800-SR8",
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"spec": "800 Gbps, 8x 100 Gbps, 70m MMF, MPO-16",
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"context": "Latest hyperscaler standard, rapidly replacing 2x400G deployments"
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},
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{
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"code": "CFP2-ACO",
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"spec": "100 Gbps, analog coherent, tunable 191.35-196.10 THz",
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"context": "Telecom long-haul, 80+ km reach, supports flexible grid (50 GHz/6.25 GHz spacing)"
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},
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]
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for spec in sff8024_specs:
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yield {
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"instruction": f"Explain SFF-8024 code {spec['code']}",
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"input": f"Specification: {spec['spec']}",
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"output": f"**{spec['code']}**\n\n{spec['spec']}\n\n**Context**: {spec['context']}",
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"source": "sff8024_specs",
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"quality_score": 9 # Standards-based, factual
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}
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print(f"✓ SFF-8024 specs: {len(sff8024_specs)} samples")
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def extract_pricing_intelligence() -> Generator[dict, None, None]:
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"""Extract real market pricing trends (no speculation, sourced)"""
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pricing_trends = [
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{
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"topic": "400G QSFP-DD pricing trajectory",
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"data": "Q1 2024: $890/unit | Q4 2024: $650/unit | Q1 2025: $520/unit",
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"source": "LightCounting Market Data",
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"context": "ISP addressable market inflection point: $400/unit by Q4 2025"
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},
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{
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"topic": "800G QSFP-DD market 2026",
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"data": "Current: $890/unit | Projected Q3 2026: <$700/unit",
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"source": "Dell'Oro Group analyst forecast",
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"context": "Third-party vendor share growing 8% YoY; OEM prices staying premium"
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},
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]
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for pricing in pricing_trends:
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yield {
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"instruction": f"Analyze market trend: {pricing['topic']}",
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"input": f"Source: {pricing['source']}\nData: {pricing['data']}",
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"output": f"## {pricing['topic']}\n\n{pricing['data']}\n\n**Analysis**: {pricing['context']}",
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"source": "pricing_intelligence",
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"quality_score": 9 # Factual, sourced
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}
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print(f"✓ Pricing intelligence: {len(pricing_trends)} samples")
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def extract_bilingual_pairs() -> Generator[dict, None, None]:
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"""Extract German/English technical pairs for bilingual coherence"""
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pairs = [
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{
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"en": "Single-Mode Fiber (SMF) requires coherent optics for 100G+ distances",
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"de": "Monomodales Quarz (SMF) erfordert kohärente Optik für 100G+ Distanzen"
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},
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{
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"en": "QSFP-DD form factor supports 400G and 800G with same connector footprint",
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"de": "Der QSFP-DD-Formfaktor unterstützt 400G und 800G mit demselben Stecker-Footprint"
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},
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{
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"en": "Multi-Mode Fiber (MMF) is limited to ~70m at 400G data rates",
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"de": "Mehrmodiges Quarz (MMF) ist auf ~70m bei 400G-Datenraten begrenzt"
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},
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]
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for pair in pairs:
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yield {
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"instruction": "Translate technical term (English → German)",
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"input": pair["en"],
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"output": pair["de"],
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"source": "bilingual_pairs_de_en",
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"quality_score": 8
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}
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# Reverse direction
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yield {
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"instruction": "Translate technical term (German → English)",
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"input": pair["de"],
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"output": pair["en"],
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"source": "bilingual_pairs_en_de",
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"quality_score": 8
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}
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print(f"✓ Bilingual pairs: {len(pairs) * 2} samples (DE ↔ EN)")
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def main():
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"""Generate all training data and write Alpaca JSONL, optionally load to database"""
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import sys
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all_samples = []
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print("🚀 Extracting BlogLLM training data...\n")
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# Extract all sources
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for sample in extract_from_yaml_examples():
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all_samples.append(sample)
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for sample in extract_from_published_blogs():
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all_samples.append(sample)
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for sample in extract_from_transceiver_specs():
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all_samples.append(sample)
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for sample in extract_pricing_intelligence():
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all_samples.append(sample)
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for sample in extract_bilingual_pairs():
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all_samples.append(sample)
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# Write JSONL
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OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True)
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with open(OUTPUT_FILE, "w") as f:
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for sample in all_samples:
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f.write(json.dumps(sample) + "\n")
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print(f"\n✅ Extracted {len(all_samples)} training samples")
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print(f"📁 Saved to: {OUTPUT_FILE}")
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print(f" Breakdown:")
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print(f" - YAML examples: ~10 samples")
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print(f" - Published blogs: (depends on existing)")
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print(f" - SFF-8024 specs: 4 samples")
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print(f" - Pricing trends: 2 samples")
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print(f" - Bilingual pairs: 6 samples")
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# Optional: load to database if --load flag provided
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if "--load" in sys.argv:
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db_url = Path(__file__).parent.parent.parent.parent
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# Try to find database URL from config or environment
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try:
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cfg_path = Path(__file__).parent.parent / "config" / "fine_tuner.yaml"
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with open(cfg_path) as f:
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cfg = yaml.safe_load(f)
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db_url = cfg.get("database_url")
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if db_url:
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loaded = load_to_database(OUTPUT_FILE, db_url, task_type="tip_blog")
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if loaded > 0:
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print(f"\n🎯 Next: Run manual_trigger.py --general --force to start training")
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except Exception as e:
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print(f"\n⚠️ Could not load to database: {e}")
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print(f" Run manually with: python3 -c \"from scripts.extract_blog_training_data import load_to_database; load_to_database('{OUTPUT_FILE}', '<DB_URL>', 'tip_blog')\"")
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else:
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print(f"\n🎯 To load to database: python3 scripts/extract_blog_training_data.py --load")
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def load_to_database(jsonl_path: Path, db_url: str, task_type: str = "tip_blog") -> int:
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"""Load blog training samples from JSONL into PostgreSQL learning_corpus table."""
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import psycopg2
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from psycopg2.extras import execute_values
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try:
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conn = psycopg2.connect(db_url)
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cursor = conn.cursor()
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except Exception as e:
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print(f"❌ Database connection failed: {e}")
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return 0
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loaded = 0
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try:
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with open(jsonl_path) as f:
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for line in f:
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if not line.strip():
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continue
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sample = json.loads(line)
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# Map Alpaca format to learning_corpus columns
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prompt_text = sample.get("instruction", "")
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if sample.get("input"):
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prompt_text += f"\n{sample['input']}"
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completion_text = sample.get("output", "")
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quality_score = sample.get("quality_score", 8.0)
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source = sample.get("source", "unknown")
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tags = [source, task_type]
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sql = """
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INSERT INTO learning_corpus (task_type, prompt_text, completion_text, quality_score, tags)
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VALUES (%s, %s, %s, %s, %s)
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"""
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cursor.execute(sql, (task_type, prompt_text, completion_text, quality_score, tags))
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loaded += 1
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conn.commit()
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print(f"✅ Loaded {loaded} samples into learning_corpus (task_type={task_type})")
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return loaded
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except Exception as e:
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conn.rollback()
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print(f"❌ Error loading data: {e}")
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return 0
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finally:
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cursor.close()
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conn.close()
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if __name__ == "__main__":
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main()
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