InfiniFlow - The AI-Native Database for LLM Applications

InfiniFlow The AI-Native Database for LLM Applications

What is InfiniFlow?

InfiniFlow represents a breakthrough in AI-native database technology, specifically engineered for Large Language Model (LLM) applications. The system excels in delivering incredibly fast hybrid search capabilities, processing queries across dense embeddings, sparse embeddings, tensors, and full-text data with remarkable efficiency.

At its core, InfiniFlow combines exceptional performance with user-friendly features, achieving query latencies as low as 0.1 milliseconds on million-scale vector datasets and handling up to 15,000 queries per second. The database employs a single-binary architecture that eliminates dependency complications, making deployment straightforward while supporting various reranking methods including RRF, weighted sum, and ColBERT.

Features

Lightning-Fast Query Processing: 0.1ms latency on million-scale vector datasets
High Throughput: Supports up to 15K QPS on large-scale datasets
Hybrid Search Capability: Combines dense embedding, sparse embedding, tensor, and full-text search
Advanced Reranking: Supports RRF, weighted sum, and ColBERT reranking methods
Simplified Deployment: Single-binary architecture with no dependencies
Python Integration: Intuitive Python API for easy implementation

Use Cases

Large Language Model Application Development
Vector Database Implementation
High-Performance Search Systems
AI-Powered Data Retrieval
Enterprise Search Solutions
Machine Learning Infrastructure

FAQs

What types of data can InfiniFlow handle?

InfiniFlow supports a wide range of data types including strings, numerics, vectors, dense embeddings, sparse embeddings, tensors, and full text.
What is the query performance on large datasets?

InfiniFlow achieves 0.1 milliseconds query latency on million-scale vector datasets and can handle up to 15K QPS (Queries Per Second).
What reranking methods are supported?

InfiniFlow supports several types of rerankers including RRF (Reciprocal Rank Fusion), weighted sum, and ColBERT.