StarRocks X Tencent - Introducing Vector Similarity Search

In this video, Petri Zhang from Tencent reveals how Tencent leverages StarRocks for Retrieval-Augmented Generation (RAG) with hybrid search. He details their journey in building a billion-scale image repository that enables both reverse image search and traditional BI dashboards within a unified platform. Petri also shares the challenges they faced along the way and the key reasons why Tencent ultimately chose StarRocks over other vector databases.

Session Highlights:
🌟Vector Index Basics
🌟StarRocks as a VectorDB
🌟Advanced Vector Similarity Search
🌟Tencent Use Cases
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00:00 Intro
01:41 Agenda
02:29 Tencent's Use of StarRocks: 20,000+ cores + PB-Scale Analytics
04:08 Tencent's Approach to Vector Similarity Search
07:16 Challenges We Faced With Existing VectorDBs
08:07 Scale and Compatibility
09:19 Complex Search Over Both Unstructured Vectors and Structured Data
10:43 Heterogeneous Workloads
12:09 Tencent's Solution: StarRocks as VectorDB and Why They Selected It Over Existing VectorDBs
14:50 Implementation -Solution 1: How They Added Vector Index Support to StarRocks
15:02 StarRocks with Vector Indexes: SQL Interface
17:17 StarRocks with Vector Indexes: Distributed Storage
18:35 StarRocks with Vector Indexes: Data Updates and MVCC
19:58 Implementation -Solution 2: Dedicated Library and Algorithms-TenANN (TNN)
22:35 Implementation -Solution 3: Serving Processing and Workload Isolation
23:24 Benchmarking: StarRocks vs Milvus, Zilliz Cloud, and Pinecone
25:37 StarRocks as Vector DB: Tencent's Use Cases
25:42 Use Case1: RAG (Retrieval Augmented Generation)
27:06 Use Case2: AIGC Image Management
28:25 Summary
29:48 Q&A

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