4 个仓库
Techniques for converting high-precision vectors into compact forms to reduce memory footprint and latency.
Distinct from Vector Indexing: Specifically addresses the compression of vectors for memory efficiency, whereas the parent is general indexing management.
Explore 4 awesome GitHub repositories matching data & databases · Vector Index Compression. Refine with filters or upvote what's useful.
ParlAI is a conversational AI research framework designed for training, evaluating, and sharing dialogue models using a unified interface for datasets and agents. It functions as a PyTorch-based training platform and a dialogue data collection system, providing a centralized model zoo for the distribution of versioned pretrained agents. The project distinguishes itself through a knowledge-grounded retrieval system that combines dense and sparse indexing to ground responses in external information. It also provides a comprehensive infrastructure for gathering human-AI interaction data via inte
Utilizes compressed embeddings within FAISS indices to optimize memory usage and speed up passage retrieval.
zvec is an embedded vector database engine and indexing library designed for high-dimensional similarity search. It functions as a hybrid search engine and a retrieval-augmented generation knowledge base, allowing for the storage and retrieval of dense and sparse vectors. The system is distinguished by its hybrid retrieval pipeline, which fuses vector similarity, full-text keyword matching, and scalar metadata filtering into single query operations. It supports a plugin-based model integration system for registering custom embedding models and rerankers, as well as language bindings for nativ
Converts high-precision vectors into compact forms to reduce memory usage and lower query latency.
RAGatouille 是一个检索框架和搜索引擎,旨在实现和训练后期交互 (late-interaction) 检索模型。它作为生成式 AI 流水线的模块化检索组件,专注于高性能文档排序以提高搜索准确性。 该项目提供了一个使用对和三元组训练及微调检索模型的工具包,具有用于领域适应的自动硬负样本挖掘功能。它实现了一种后期交互机制,通过利用压缩嵌入在检索速度和精度之间取得平衡。 该系统涵盖了文档索引和检索操作,利用基于磁盘的向量存储来处理超出可用系统内存的数据集。它进一步支持通过映射标记级嵌入来创建检索增强生成工作流,以保留细粒度的语义信息。
Implements quantized vector storage to reduce memory footprint and latency during retrieval.
USearch is a high-performance vector similarity search engine and approximate nearest neighbor index designed for dense embeddings. It functions as a low-level vector database core and high-dimensional vector indexer, providing the primitives necessary to store and retrieve vectors across massive datasets. The engine distinguishes itself through hardware-level SIMD acceleration for distance kernels and a proximity-graph indexing system that enables fast retrieval across billions of vectors. It supports multi-precision vector quantization to balance memory usage and accuracy, and utilizes memo
Reduces memory footprint by storing vectors as half-precision floats, 8-bit integers, or packed binary formats.