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The explosion of embedding-based applications created a new challenge: efficiently storing, indexing, and searching these high-dimensional vectors at scale. This gap gave rise to the vector database category, with companies like Pinecone leading the charge in 2022-2023 by defining specialized infrastructure for vector operations.

The category saw explosive growth following ChatGPT's launch in late 2022, as developers rushed to build AI applications using Retrieval-Augmented Generation (RAG). This surge was partly driven by a widespread misconception that embedding-based similarity search was the only viable method for retrieving context for LLMs!!!

The resulting "vector database gold rush" saw massive investment and attention directed toward vector search infrastructure, even though traditional information retrieval techniques remained equally valuable for many RAG applications.

https://x.com/jobergum/status/1872923872007217309

Chapters

00:00 Introduction to Trondheim and Background
03:03 The Rise and Fall of Vector Databases
06:08 Convergence of Search Technologies
09:04 Embeddings and Their Importance
12:03 Building Effective Search Systems
15:00 RAG Applications and Recommendations
17:55 The Role of Knowledge Graphs
20:49 Future of Embedding Models and Innovations