Semantic Search & Vector Database
XRPL.Sale's AI-powered semantic search uses advanced vector embeddings and Milvus database for intelligent content discovery across the entire platform.
Technical Architecture
Vector Embeddings
- OpenAI text-embedding-3-small - Latest embedding model
- 1536-dimensional vectors - High-precision content representation
- Semantic understanding - Captures meaning beyond keywords
- Real-time processing - Dynamic embedding generation
Milvus Database
- Zilliz Cloud serverless - Enterprise-grade deployment
- COSINE similarity search - Optimized vector matching
- IVF_FLAT indexing - Fast approximate search
- SQLite fallback - 100% uptime reliability
Indexed Content Categories
Static Pages
Homepage, features, about, technology guides, and all informational content
Documentation
Technical docs, API references, tutorials, and integration guides
Projects
Token projects, descriptions, roadmaps, and launch details
Advanced Search Capabilities
Natural Language Queries
Ask questions in plain English like "How do I launch a token?" or "What are tier benefits?" The AI understands context and intent.
Contextual Understanding
Vector embeddings capture semantic meaning, not just keyword matches. Find relevant content even with different terminology.
Smart Content Filtering
Hybrid search combines vector similarity with traditional filters for precise results.
Performance & Scale
Search Speed
Reliability
Try Semantic Search Now
Experience AI-powered content discovery. Search the entire platform using natural language!