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>
27 lines
641 B
Python
27 lines
641 B
Python
#!/usr/bin/env python3
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"""Generate skill for FAISS (same as other examples)"""
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import subprocess, sys
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from pathlib import Path
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print("=" * 60)
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print("Step 1: Generating Skill for FAISS")
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print("=" * 60)
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# Scrape
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subprocess.run([
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"skill-seekers", "scrape",
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"--config", "configs/flask.json",
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"--max-pages", "20"
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], check=True)
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# Package
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subprocess.run([
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"skill-seekers", "package",
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"output/flask",
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"--target", "faiss"
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], check=True)
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output = Path("output/flask-faiss.json")
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print(f"\n✅ Ready: {output} ({output.stat().st_size/1024:.1f} KB)")
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print("Next: python 2_build_faiss_index.py (requires OPENAI_API_KEY)")
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