Infinity v0.2 delivers the most comprehensive hybrid search solution to date, including vector search, full-text search, sparse vector search, and tensor search. It also provides three fusion reranking methods: RRF, Weighted Sum, and ColBERT Reranker. How effective are these search and ranking solutions in practice? This blog article delves into the details for you.
Infinity v0.2 was released, introducing two new data types: Sparse vector and Tensor. Besides full-text search and vector search, Infinity v0.2 offers more retrieval methods. As shown in the diagram below, users can now do retrieval from as many ways as they wish (N ≥ 2) in a hybrid search, making Infinity the most powerful database for RAG so far.
Infinity v0.2 was released, offering the most comprehensive and fastest multi-way retrieval in the industry. This blog post explains how Infinity achieves this.
Infinity is a database with sophisticated designs at both storage engine and execution engine levels. The following diagram illustrates the workflow of Infinity's execution engine: after binding the API queries, the execution plan is compiled into a pipeline execution plan. This mechanism differs from those commonly seen in modern data warehouses. Pipelines in data warehouses are designed mainly for parallel query execution; Infinity's pipeline serves both parallel querying and concurrent query execution to optimize scheduling strategies and CPU affinity for query operators during high-concurrency execution, and avoid overhead caused by invalid context switches. This optimization in design translates to reduced end-to-end query overhead and an overall query latency comparable to latencies running a single retrieval library.