NornicDB: Fast Distributed Graph+Vector Database

NornicDB: Fast Distributed Graph+Vector Database

NornicDB is a high-performance, distributed database designed for AI developers. It combines graph and vector database capabilities, enabling efficient storage and retrieval of complex relationships and embeddings. This tool is particularly suited for applications requiring rapid querying of both structured graph data and unstructured vector data, such as knowledge graphs, recommendation engines, and semantic search systems.

What NornicDB Does

NornicDB provides a unified platform for managing and querying data that exhibits both relational (graph) and similarity (vector) characteristics. It allows developers to store nodes and edges, alongside associated vector embeddings, and perform complex queries that leverage both data types. This dual functionality streamlines development by eliminating the need for separate graph and vector databases in many AI architectures.

Key Features

• Distributed Architecture: Scales horizontally to handle large datasets and high query loads.
• Sub-Millisecond HNSW Search: Utilizes Hierarchical Navigable Small Worlds (HNSW) for extremely fast approximate nearest neighbor vector search.
• Efficient Graph Traversals: Optimized for navigating relationships within the graph structure.
• Unified Data Model: Integrates graph and vector data storage and querying within a single system.
• Open Source (MIT License): Available on GitHub, fostering community contribution and transparency.

Who NornicDB is For

NornicDB is an ideal solution for AI engineers, data scientists, and software developers building applications that require:

• Knowledge Graph Construction and Querying: Storing and querying complex relationships between entities.
• Recommendation Systems: Generating personalized recommendations based on user behavior and item similarities.
• Semantic Search: Enabling search based on meaning and context rather than just keywords.
• AI Model Deployment: Providing fast access to embeddings and related data for inference.
• Applications with Hybrid Data Needs: Projects that benefit from the combined power of graph and vector databases.