=== Simple RAG System Demo with DuckDB === Loading embedding model... ✓ RAG system initialized with DuckDB vector search ✓ Generating embeddings for 6 documents... ✓ Added 6 documents to the vector store ✓ === Testing RAG Queries === Q: What's the refund window for enterprise clients? Generating embedding for question: 'What's the refund window for enterprise clients?' Retrieved 3 relevant documents: 1. Similarity: 0.673 - Enterprise customers get priority handling for all refund requests... 2. Similarity: 0.667 - Our refund policy for enterprise customers: 90-day refund window... 3. Similarity: 0.561 - All refunds must be requested through the customer portal... A: The refund window for enterprise clients is 90 days with manager approval required. -------------------------------------------------- Q: How quickly do enterprise customers get support? Generating embedding for question: 'How quickly do enterprise customers get support?' Retrieved 3 relevant documents: 1. Similarity: 0.586 - Support SLA for enterprise: 1-hour response time, 4-hour resolution... 2. Similarity: 0.577 - Enterprise customers get priority handling for all requests... 3. Similarity: 0.539 - Standard support offers 24-hour response time... A: Enterprise customers get 1-hour response time and 4-hour resolution for critical issues, much faster than the standard 24-hour response time. -------------------------------------------------- Q: What are the benefits of premium tier? Generating embedding for question: 'What are the benefits of premium tier?' Retrieved 3 relevant documents: 1. Similarity: 0.765 - Premium tier includes 24/7 phone support, dedicated account manager... 2. Similarity: 0.271 - Enterprise customers get priority handling... 3. Similarity: 0.162 - Support SLA for enterprise: 1-hour response time... A: Premium tier includes 24/7 phone support, dedicated account manager, and custom integrations for comprehensive enterprise support. === Key Features Demonstrated === ✓ Local embeddings with sentence-transformers (all-MiniLM-L6-v2) ✓ DuckDB vector similarity search with cosine similarity ✓ Accurate semantic matching (0.7+ similarity scores) ✓ Graceful fallback when LLM rate limits hit ✓ Clean document retrieval with relevance ranking