Research & Innovation
Explore our cutting-edge research papers that power the next generation of vector search technology. Each paper represents breakthrough innovations in vector indexing, metadata-aware search, and AI memory systems.
CHANNI: A Multi-Level Vector Search Index with Nested Graph Navigation
A Disk-Based ANN Index For Massively Scaled Apps
CHANNI introduces a novel vector indexing architecture that bridges the gap between memory-efficient clustering and high-performance graph navigation, combining hierarchical navigable small world graphs at multiple levels.
Key Innovations:
- Multi-level navigation architecture leveraging HNSW graphs
- Primary-based cluster representation for efficient memory utilization
- Innovative sample-based clustering strategy with minimal computational expense
MAVANN: Metadata-Aware Vector Approximate Nearest Neighbor
A Hierarchical Approach to Integrated Metadata Filtering in Vector Similarity Search
MAVANN introduces a novel approach to metadata filtering in vector databases, integrating metadata directly into the vector search process for dramatically improved performance without sacrificing result quality.
Key Innovations:
- Metadata-aware vector navigation for efficient pre-filtering during search
- Dynamic re-routing based on both vector similarity and metadata constraints
- Up to 20x faster query performance with complex metadata filtering
Toward a Human-Like Memory System for AI Agents
Finite Storage, Sparse Contexts, and Probabilistic Recall
A comprehensive memory architecture for AI agents that addresses the fundamental challenge of "Infinite Memory, Finite Storage" through dynamic, context-aware memory management systems that mirror human cognitive processes.
Key Innovations:
- Human-inspired memory architecture for AI systems
- Dynamic information retention and retrieval mechanisms
- Enhanced long-term context understanding for AI agents
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