What are the core features of InfiniFlow?
InfiniFlow offers incredibly fast query performance, powerful hybrid search, support for rich data types, and ease of use. It achieves 0.1 milliseconds latency on million-scale vector datasets and supports up to 15K QPS. It also supports several reranker types and provides an intuitive Python API.
What data types does InfiniFlow support?
InfiniFlow supports a wide range of data types including strings, numerics, vectors, and more. Its hybrid search capability specifically covers dense embedding, sparse embedding, tensor, and full-text data.
How does InfiniFlow perform in terms of search capabilities?
InfiniFlow is designed for incredibly fast hybrid search, combining dense embedding, sparse embedding, tensor, and full-text data. It also supports filtering and several reranker types, including RRF, weighted sum, and ColBERT.
What does InfiniFlow offer for deployment?
InfiniFlow offers a single-binary architecture with no dependencies, making deployment straightforward. It is an open-source tool, as indicated by its tags.
What is InfiniFlow and what problem does it solve?
InfiniFlow is an AI-native database designed specifically for LLM applications. It solves the problem of needing high-performance, hybrid search across diverse data types like dense embeddings, sparse embeddings, tensors, and full text with millisecond-level latency.
Who is InfiniFlow designed for?
InfiniFlow is a developer tool, specifically designed for developers building applications with large language models. It is categorized within Data & Analytics and its core use case is structured data querying for LLM apps.