1 Commits (a94b7d6b2bc78d2f516f5e19e7db04e5bb9ef545)

Author	SHA1	Message	Date
Rene Cannao	e75bd7c84a	Add comprehensive sqlite-rembed examples and documentation ... This commit adds a complete set of well-documented examples and test scripts for the sqlite-rembed integration in ProxySQL. The sqlite-rembed extension enables text embedding generation via HTTP API calls directly within SQL queries, complementing the sqlite-vec extension for vector similarity search. Key additions: 1. sqlite-rembed-examples.sql - Primary SQL demonstration file - Standalone SQL file with 8 phases of examples - Demonstrates complete AI pipeline: client config → embedding gen → storage → search - Includes proper subquery pattern for similarity search JOINs (vec0 requirement) - Well-documented with clear explanations for each phase 2. sqlite-rembed-test.sh - Comprehensive test suite - 9-phase test covering all integration aspects - Color-coded output with test result tracking - Error handling and edge case testing 3. SQLITE-REMBED-TEST-README.md - Complete documentation - Detailed test suite documentation - Usage instructions, troubleshooting, CI/CD integration examples 4. Supporting bash scripts for different use cases - sqlite-rembed-examples.sh - Phase-by-phase interactive examples - sqlite-rembed-demo.sh - Single-session demonstration script Security considerations: - All hardcoded API keys replaced with YOUR_API_KEY placeholder - Clear comments instructing users to replace with actual credentials - Synthetic OpenAI endpoint used as example (public test endpoint) Technical details: - Embedding dimensions: 768 (nomic-embed-text-v1.5 model) - Embedding size: 3072 bytes (768 × 4 bytes per float) - Similarity search pattern: Uses subqueries for JOIN compatibility with vec0 - Client configuration: temp.rembed_clients virtual table (per-connection) The examples provide a solid baseline for building applications that leverage sqlite-rembed and sqlite-vec in ProxySQL for AI-powered applications.	2 months ago