Core features:
• Zero-configuration embedded vector database - no setup, no config files
• HNSW indexing for sub-millisecond vector similarity search
• ACID-compliant storage with write-ahead logging for data integrity
• Complete REST API for language-agnostic integration
• Single Go binary - 8MB download, runs anywhere
• Python SDK with automatic binary management
Technical highlights:
The database uses HNSW (Hierarchical Navigable Small World) graphs for efficient approximate nearest neighbor search, achieving 2.6M+ vectors/sec insert rates and sub-millisecond query latency. Storage is ACID-compliant with WAL (Write-Ahead Logging) for crash recovery and data consistency.
Local AI integration:
Built-in Ollama integration provides high-quality ML embeddings without API costs or internet dependencies. Upload documents (PDF, DOCX, MD, HTML) and get automatic chunking, vectorization, and semantic search - the complete RAG pipeline runs locally.
Unlike hosted vector databases, VittoriaDB runs as part of your application process. No separate infrastructure, no network latency, no external dependencies. Perfect for local development, edge deployments, or anywhere you need vector search without operational complexity.
Performance benchmarks:
• Insert speed: 2.6M+ vectors/second
• Search latency: <1ms for optimized queries
• Memory usage: Linear scaling (1MB per 1K vectors)
• Binary size: 8MB compressed
Real-world usage: Developers use it for RAG applications, semantic search, document processing, and AI prototyping where you need production-grade vector operations without database administration overhead.
Links:
• GitHub: https://github.com/antonellof/VittoriaDB
• PyPI: https://pypi.org/project/vittoriadb/
• Install: pip install vittoriadb