AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
2026-08-10 • Databases
DatabasesArtificial Intelligence
AI summaryⓘ
The authors present AkasicDB, a new database system designed to efficiently handle complex tasks that combine searching by similarity, exploring graphs, and filtering data all at once. Unlike existing systems that use separate steps or poorly integrated methods, AkasicDB performs these operations together within one framework. This allows users to better retrieve and reason with information compared to using only similarity searches. The authors demonstrated their system through an interactive chat that shows its strengths and the challenges current databases face with such tasks.
Retrieval-Augmented GenerationVector similarity searchGraph traversalRelational filteringDatabase architectureVector retrievalChimera systemOmni RAGStructured knowledgeInformation retrieval
Authors
Geonho Lee, Jeongho Park, Donghyoung Han, Min-Soo Kim
Abstract
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