InfinityFlow

InfinityFlow is an AI-native database built specifically for large language model (LLM) applications. Its core function is to provide incredibly fast hybrid search across multiple data types. You can use it to search dense embeddings (like those from OpenAI or Cohere), sparse embeddings, tensors, and full text simultaneously, all while applying filters. This makes it a practical engine for applications that need to retrieve relevant information quickly, such as chatbots that pull from a knowledge base or search systems that understand semantic meaning.

What sets InfinityFlow apart is its performance and design. It achieves query latencies as low as 0.1 milliseconds on datasets with millions of vectors and can handle up to 15,000 queries per second. The database supports several reranking methods to refine results, including Reciprocal Rank Fusion (RRF), weighted sum, and ColBERT. For developers, it offers an intuitive Python API and is packaged as a single binary with no external dependencies, which simplifies deployment significantly.

This tool is most useful for developers and ML engineers building production-grade AI applications. If you're creating a retrieval-augmented generation (RAG) pipeline, a semantic search platform, or any system that requires efficient storage and recall of vectorized data, InfinityFlow provides the specialized database layer. It handles the complex search operations so you can focus on the application logic.