Highlights
VirtuousAI leveraged FalkorDB to create a high-performance, multi-modal data store for ethical AI development, enabling efficient data management, model training, and embedding updates.
Technologies & Integrations
- FalkorDB
- PyTorch and TensorFlow
- Common Crawl
- Private Data Sources
- Various Data Modalities
The Challenge
VirtuousAI needed a scalable and efficient data management solution to handle diverse data modalities (text, image, HTML, video, and tabular) with various link types. The company required a database that could provide high-performance querying and analysis of complex data relationships to feed its foundational model algorithms.
The Solution
VirtuousAI chose FalkorDB, a fast and low-latency Graph Database that utilizes sparse matrices and linear algebra to query and analyze complex data relationships. FalkorDB’s architecture enables high-performance and scalability, making it an ideal solution for VirtuousAI’s requirements. The database will store public data (Common Crawl and other miscellaneous data sources) and private data (client data), providing a centralized data store for VirtuousAI’s AI platform.
"Virtuous AI uses FalkorDB to create a large, centralized data store for public data (common crawl and other miscellaneous data sources) and private data (client data) that contains different modalities (text, image, HTML, video, and tabular) with different link types between this data."
The Result
With FalkorDB, VirtuousAI expects to achieve:
- High-performance querying and analysis of complex data relationships
- Low latency data retrieval and updates
- Scalability to handle large volumes of diverse data modalities
- Improved accuracy in foundational model algorithms via PyTorch and TensorFlow dataloaders
- Efficient data management and updates with output embeddings from AI algorithms
