打造统一数据库，让向量、图谱与关系查询无缝协同
AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
- 将向量、图谱和关系查询在单框架内原生融合执行
- 实测性能优于传统向量单独处理方式，支持复杂推理
- 适合需要多模态知识检索的AI系统研发者
近期检索增强生成（RAG）系统越来越多地结合向量检索与结构化知识，如图谱RAG和过滤向量搜索。但现有数据库架构难以高效支持此类复杂RAG工作流，因依赖数据库外管道或非原生集成，导致开销过高。本文展示AkasicDB，一种原生支持此类RAG工作流的数据库系统，可在单一执行框架中联合执行向量相似性搜索、图遍历和关系过滤。AkasicDB基于我们先前的Chimera系统扩展了原生向量支持，实现统一执行。在此基础上，我们首次演示了向量-图谱-关系RAG的原生集成，称之为Omni RAG。通过交互式聊天式演示，用户可执行并可视化Omni RAG查询，直观体验其相较于纯向量方法的优越检索与推理能力，同时观察现有数据库架构在支持Omni RAG时的实际局限。演示视频见 https://youtu.be/8d09_dtrEIM
原文摘要 · Abstract (English)
Recent Retrieval-Augmented Generation (RAG) systems increasingly combine vector retrieval with structured knowledge, such as Graph RAG and Filtered vector search. However, existing database architectures struggle to support such complex RAG workflows efficiently, as they rely on out-of-DB pipelines or in-DB non-native integration, leading to high overhead. This demo paper presents AkasicDB, a database system that natively supports such RAG workflows by jointly executing vector similarity search, graph traversal, and relational filtering within a single execution framework. AkasicDB extends our prior work, Chimera, with native vector support to enable such unified execution. Based on AkasicDB, we demonstrate the first native integration of Vector-Graph-Relational RAG, which we refer to as Omni RAG. Through an interactive chat-style demonstration, users execute and visualize Omni RAG queries, directly experiencing its superior retrieval and reasoning over vector-only approaches while observing the practical limitations of existing database architectures in supporting Omni RAG. A demonstration video is available at https://youtu.be/8d09_dtrEIM
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