Unleashing the Potential of Cloud Native Open Source Vector Databases
summary
Live demos show a chatbot over documentation and notebooks that run locally then scale to Kubernetes using Milvus and the managed Zill as Cloud path, with code and Helm charts published. The system uses Kafka/Pulsar, LangChain/OpenAI/Hugging Face integrations, and index types like HNSW, IVF and quantized PQ, handling up to 10 billion vectors and single instances with ~1500‑dim vectors; retrieval‑augmented generation (RAG) workflows run end to end and benchmarks and source are available.
AI-written summary · about
chapters (8)
- 0:00Introduction and Overview
- 0:43Understanding Cloud Native and Vector Databases
- 0:56Cool Demos and Practical Applications
- 1:50Deep Dive into Milvus Features
- 3:31Advanced Capabilities and Use Cases
- 7:16Setting Up and Managing Milvus
- 16:13Community and Resources
- 25:48Conclusion and Final Thoughts
related talks
51mUnleashing the Potential of Cloud-Native Vector Databases
37mMilvus: Scaling Vector Data Solutions with Gen AI
35mMilvus 2.0: Building the World's Most Advanced Open-source Vector Database
36mMilvus: Scaling Vector Data Solutions with Gen AI
26mThe Rise of Vector Databases - Lessons from the Milvus Community
36mUnleashing the Power of Vector Databases for AI-Driven Applications
48mA fun and absurd introduction to Vector Databases
19mScalable Data and AI Solutions with Cloud-Native Architecture