এই এপিসোড সম্বন্ধে
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 a step back: key points
13:45 Don't get caught up in trendy shiny new tech
