TigerVector: Supporting Vector Search in Graph Databases for Advanced RAGs
Shige Liu, Zhifang Zeng, Li Chen, Adil Ainihaer, Arun Ramasami, Songting Chen, Yu Xu, Mingxi Wu, Jianguo Wang

TL;DR
TigerVector enhances graph databases by integrating vector search capabilities directly into TigerGraph, enabling advanced, scalable hybrid querying of structured and unstructured data with superior performance.
Contribution
The paper introduces TigerVector, a novel system that seamlessly combines vector search with graph querying within TigerGraph, extending its capabilities significantly.
Findings
Demonstrates hybrid search performance surpassing Neo4j, Amazon Neptune, and Milvus.
Shows scalability and efficiency of the MPP index framework.
Integrates vector search into GSQL for expressive graph queries.
Abstract
In this paper, we introduce TigerVector, a system that integrates vector search and graph query within TigerGraph, a Massively Parallel Processing (MPP) native graph database. We extend the vertex attribute type with the embedding type. To support fast vector search, we devise an MPP index framework that interoperates efficiently with the graph engine. The graph query language GSQL is enhanced to support vector type expressions and enable query compositions between vector search results and graph query blocks. These advancements elevate the expressive power and analytical capabilities of graph databases, enabling seamless fusion of unstructured and structured data in ways previously unattainable. Through extensive experiments, we demonstrate TigerVector's hybrid search capability, scalability, and superior performance compared to other graph databases (including Neo4j and Amazon…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGraph Theory and Algorithms · Data Management and Algorithms · Advanced Database Systems and Queries
