As 2024 comes to a close, the development of Retrieval-Augmented Generation (RAG) has been nothing short of turbulent. Let's take a comprehensive look back at the year's progress from various perspectives.
The final release of RAGFlow for the year of 2024, v0.15.0, has just been released, bringing the following key updates:
This version introduces several enhancements to the Agent, including additional APIs, step-run debugging, and import/export capabilities. Since v0.13.0, RAGFlow's Agent has been restructured to improve usability. The step-run debugging feature finalizes this process, enabling operators in the Agent workflow to be executed individually, thereby assisting users in debugging based on output information.
Infinity is a database specifically designed for Retrieval-Augmented Generation (RAG), excelling in both functionality and performance. It provides high-performance capabilities for dense and sparse vector searches, as well as full-text searches, along with efficient range filtering for these data types. Additionally, it features tensor-based reranking, enabling the implementation of powerful multi-modal RAG and integrating ranking capabilities comparable to Cross Encoders.
RAGFlow v0.9 introduces support for GraphRAG, which has recently been open-sourced by Microsoft, allegedly the next generation of Retrieval-Augmented Generation (RAG). Within the RAGFlow framework, we have a more comprehensive definition of RAG 2.0. This proposed end-to-end system is search-centric and consists of four stages. The last two stages—indexing and retrieval—primarily require a dedicated database, while the first two stages are defined as follows: