What is KDB.AI?
KDB.AI provides a robust and scalable vector database solution tailored for developing production-ready AI applications. It emphasizes high performance, boasting 99.99% uptime and sub-100ms search latency, making it suitable for demanding AI workloads. The platform is engineered to manage the complexities associated with unstructured data types including text, video, audio, and images, facilitating multimodal Retrieval Augmented Generation (RAG).
Offering advanced search functionalities such as dynamic hybrid search, temporal similarity search, and metadata filtering, KDB.AI enhances the relevance and accuracy of search results. It employs unique indexing methods like on-disk indexing (qHNSW, qFlat) and zero embedding for optimized performance and reduced resource consumption. KDB.AI integrates with popular GenAI tools and provides community resources to support developers in building sophisticated AI systems.
Features
- Multimodal RAG: Handles unstructured data like text, video, audio, and images for complex GenAI modeling.
- Multi-Index Search: Unifies multiple indexes for flexible and faster multi-layered embedding search.
- On-Disk Indexing: Utilizes qHNSW and qFlat indexing to lower costs and memory requirements for scaling.
- Zero Embedding: Enables faster search with less memory for temporal data without needing embeddings.
- Killer Compression: Reduces memory/storage by 100x for time-based data sets and accelerates search.
- Dynamic Hybrid Search: Combines similarity, exact, and literal search for relevant results even with content changes.
- Metadata Filtering: Refines search accuracy by filtering vectors based on unlimited metadata.
- Temporal Similarity Search: Finds similar time series windows and detects anomalies in temporal data.
Use Cases
- Building production-grade AI applications requiring high scalability and low latency.
- Implementing semantic search and recommendation systems.
- Developing Retrieval Augmented Generation (RAG) systems for internal knowledge bases.
- Performing anomaly detection in time series data.
- Searching and analyzing multimodal data (text, image, audio, video).
- Optimizing search relevance using hybrid (semantic + keyword) search.
- Managing and searching large-scale temporal datasets efficiently.
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