KDB.AI
KDB.AI is a proprietary vector database and search engine designed for real-time AI applications. It offers advanced vector search, integrates with popular ML tools, and supports temporal and semantic context for embeddings. KDB.AI Server is a high-performance vector database and search engine from KX, designed for real-time analytics and AI applications requiring rapid similarity search.
About this tool
KDB.AI
Category: Vector Database Engines
Website: https://kdb.ai/
KDB.AI is a proprietary vector database and search engine designed for real-time AI applications, offering advanced vector search capabilities and integrations with popular machine learning tools. It supports temporal and semantic context for embeddings and is optimized for rapid similarity search and analytics.
Features
- Real-Time Vector Search: Sub-100ms latency for fast similarity search in AI applications.
- Hybrid Search: Combines semantic (dense) and keyword (sparse) vector searches in a single query for increased relevance.
- Metadata Filtering: Refine search accuracy by filtering vectors based on unlimited metadata and structured data.
- Temporal Similarity Search: Perform searches and anomaly detection over time series and temporal data.
- Multimodal RAG: Handles unstructured data such as text, video, audio, and images for generative AI applications.
- Multi-Index Search: Unifies multiple indexes for multi-layered embeddings and faster, flexible searches.
- On-Disk Indexing: Uses purpose-built qHNSW and qFlat indexing to reduce costs and memory requirements.
- Zero Embedding Search: Enables fast search (17x faster, 12x less memory than HNSW) for rapidly changing temporal data without needing embeddings.
- Data Compression: Reduces memory and storage usage by up to 100x for slow-changing time-based datasets and accelerates search by 10x.
- Dynamic Hybrid Search: Supports similarity, exact, and literal search in a single query, maintaining relevance as content changes.
- Integration with AI Tools: Integrates with LangChain, LlamaIndex, Vector-io, OpenAI, Azure AI, HuggingFace, and Unstructured.io.
- Support for Retrieval Augmented Generation: Build internal knowledge bases to enhance LLMs.
- Chunking Strategies: Chunk documents for better data injection into LLMs and reduce hallucinations.
Pricing
- Free Tier: Sign up and build production-grade AI apps for free with 99.99% uptime. (No detailed pricing plans provided in available content.)
Tags
proprietary, real-time, ai, vector-search
For more details, visit the official website.