Repository History
4 repositories tagged with Vector Search
TencentDB Agent Memory: Enhancing AI Agents with Layered Long-Term Memory
TencentDB Agent Memory provides AI agents with fully local, long-term memory through a 4-tier progressive pipeline, eliminating external API dependencies. It significantly reduces token usage and improves task success rates by employing symbolic short-term memory and layered long-term memory. This innovative approach helps agents learn workflows and retain context more effectively.
Dragonfly: A High-Performance, Redis and Memcached Compatible Data Store
Dragonfly is an innovative in-memory data store designed as a modern replacement for Redis and Memcached. It offers significant performance improvements, including up to 25x higher throughput and better memory efficiency, while maintaining full API compatibility. Built with a shared-nothing architecture and novel caching, Dragonfly is ideal for demanding application workloads.
Faiss: Efficient Similarity Search and Clustering for Dense Vectors
Faiss is a library developed by Meta's Fundamental AI Research (FAIR) group, designed for efficient similarity search and clustering of dense vectors. It offers a comprehensive suite of algorithms capable of handling vector sets of any size, including those that exceed RAM capacity. With complete wrappers for Python/numpy and GPU implementations, Faiss provides robust solutions for various vector comparison tasks.
Lance: Modern Columnar Data Format for ML and LLMs
Lance is a modern columnar data format, implemented in Rust, designed for machine learning and large language model workflows. It offers significant performance improvements over Parquet for random access, includes vector indexing, and supports data versioning. Compatible with popular tools like Pandas, DuckDB, and PyTorch, Lance streamlines data management for ML applications.