proxysql

Commit Graph

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.	4 months ago

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.

1 Commits (7faf9932950dcbf8cbfc7e8b0cecba25cdce28f0)