Vector Databases: The Rise, Fall and Future - by NotebookLM

About this episode

Dmitry Kan discusses the evolution and future of vector databases with insights from NotebookLM.

https://www.vectorpodcast.com/ I had fun interacting with NotebookLM - mostly for self-educational purposes. I think this tool can help by bringing an additional perspective over a textual content. It ties to what RAG (Retrieval Augmented Generation) can do to content generation in another modality. In this case, text is used to augment the generation of a podcast episode. This episode is based on my blog post: https://dmitry-kan.medium.com/the-rise-fall-and-future-of-vector-databases-how-to-pick-the-one-that-lasts-6b9fbb43bbbe Time codes: 00:00 Intro to the topic 1:11 Dmitry's knowledge in the space 1:54 Unpacking the Rise & Fall idea 3:14 How attention got back to Vector DBs for a bit 4:18 Getting practical: Dmitry's guide for choosing the right Vector Database 4:39 FAISS 5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines 6:41 Exception to the rule: Late-interaction models 8:30 Latency and QPS: GSI APU, Vespa, Hyperspace 9:28 Strategic approach 9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud 10:14 Community voice: pgvector 10:48 Picture of the fascinating future of the field 12:23 Question to the audience 12:44 Taking…

People in this episode

Host: Dmitry Kan

Topics covered

• vector databases
• content generation
• Retrieval Augmented Generation
• technology trends
• database selection
• cloud solutions

Keywords

• vector databases
• RAG
• content generation
• database selection
• cloud solutions
• FAISS
• Apache Lucene

Mentioned in this episode

Organizations: NotebookLM, FAISS, Apache Lucene, GSI APU, Vespa, Hyperspace, CosmosDB, Vertex AI, Pinecone, Weaviate Cloud

Books & works: The Rise, Fall and Future of Vector Databases

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