BilberryDB

BilberryDB is an enterprise-grade vector database designed for managing and querying high-dimensional embeddings across image, video, audio, and text data. It provides a unified platform for storing multimodal vectors and performing similarity search at scale, enabling developers to build retrieval-augmented generation (RAG), recommendation, and search applications with consistent performance and reliability. The core purpose of BilberryDB is to handle large volumes of vector data while maintaining low-latency queries and robust operational capabilities suitable for production environments.

BilberryDB supports approximate and exact nearest neighbor search, allowing teams to balance accuracy and speed based on workload requirements. It offers horizontal scalability, high availability, and fault tolerance, making it suitable for large-scale deployments where uptime and performance are critical. Built-in indexing strategies, such as HNSW or IVF-like structures, optimize query performance for diverse data distributions. The database typically exposes APIs and SDKs for common programming languages, integrates with popular AI frameworks and embedding models, and supports metadata filtering so that queries can combine semantic similarity with structured constraints.