53d37e61dd
Created complete working examples for all 4 vector databases with RAG adaptors: Weaviate Example: - Comprehensive README with hybrid search guide - 3 Python scripts (generate, upload, query) - Sample outputs and query results - Covers hybrid search, filtering, schema design Chroma Example: - Simple, local-first approach - In-memory and persistent storage options - Semantic search and metadata filtering - Comparison with Weaviate FAISS Example: - Facebook AI Similarity Search integration - OpenAI embeddings generation - Index building and persistence - Performance-focused for scale Qdrant Example: - Advanced filtering capabilities - Production-ready features - Complex query patterns - Rust-based performance Each example includes: - Detailed README with setup and troubleshooting - requirements.txt with dependencies - 3 working Python scripts - Sample outputs directory Total files: 20 (4 examples × 5 files each) Documentation: 4 comprehensive READMEs (~800 lines total) Phase 2 of optional enhancements complete. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
#!/usr/bin/env python3
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"""
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Step 1: Generate Skill for ChromaDB
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This script:
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1. Scrapes Vue documentation (limited to 20 pages for demo)
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2. Packages the skill in ChromaDB format
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3. Saves to output/vue-chroma.json
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Usage:
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python 1_generate_skill.py
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"""
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import subprocess
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import sys
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from pathlib import Path
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def main():
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print("=" * 60)
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print("Step 1: Generating Skill for ChromaDB")
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print("=" * 60)
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# Check if skill-seekers is installed
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try:
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result = subprocess.run(
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["skill-seekers", "--version"],
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capture_output=True,
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text=True
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)
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print(f"\n✅ skill-seekers found: {result.stdout.strip()}")
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except FileNotFoundError:
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print("\n❌ skill-seekers not found!")
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print("Install it with: pip install skill-seekers")
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sys.exit(1)
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# Step 1: Scrape Vue docs (small sample for demo)
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print("\n📥 Step 1/2: Scraping Vue documentation (20 pages)...")
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print("This may take 1-2 minutes...\n")
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scrape_result = subprocess.run(
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[
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"skill-seekers", "scrape",
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"--config", "configs/vue.json",
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"--max-pages", "20",
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],
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capture_output=True,
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text=True
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)
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if scrape_result.returncode != 0:
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print(f"❌ Scraping failed:\n{scrape_result.stderr}")
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sys.exit(1)
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print("✅ Scraping completed!")
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# Step 2: Package for ChromaDB
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print("\n📦 Step 2/2: Packaging for ChromaDB...\n")
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package_result = subprocess.run(
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[
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"skill-seekers", "package",
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"output/vue",
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"--target", "chroma",
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],
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capture_output=True,
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text=True
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)
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if package_result.returncode != 0:
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print(f"❌ Packaging failed:\n{package_result.stderr}")
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sys.exit(1)
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# Show the output
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print(package_result.stdout)
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# Check if output file exists
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output_file = Path("output/vue-chroma.json")
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if output_file.exists():
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size_kb = output_file.stat().st_size / 1024
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print(f"📄 File size: {size_kb:.1f} KB")
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print(f"📂 Location: {output_file.absolute()}")
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print("\n✅ Ready for upload! Next step: python 2_upload_to_chroma.py")
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else:
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print("❌ Output file not found!")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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