Elasticsearch, every mention
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every year anyone Michael Royzen 4Shawn Wang 2Simon Eskildsen 1Sarah Sachs 1Jo Kristian Bergum 1Anish Agarwal 1Alessio Fanelli 1
Verbatim, from the transcripts: the passages where Elasticsearch comes up
The Agent Cloud: Databricks’ Bet on the Future of AI — Matei Zaharia and Reynold Xin
- ▶ 30:53 Shawn Wang Yeah, which for people, Elasticsearch is like a big- 2 times in the scene
Notion’s Sarah Sachs & Simon Last on Custom Agents, Evals, and the Future of Work
- ▶ 1:15:50 Sarah Sachs And so for every query that's hitting our elastic search or our vector indices, they're not coming from humans and the queries are structured differently and what's returned has a different requirement.
Retrieval After RAG: Hybrid Search, Agents, and Database Design — Simon Eskildsen of Turbopuffer
- ▶ 3:27 Alessio Fanelli So people might say, well, didn't Elasticsearch already do this?
- ▶ 7:51 Simon Eskildsen The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was Elasticsearch.
⚡️Traversal: Causal ML and Reinforcement Learning
- ▶ 7:16 Anish Agarwal And you have to like query autonomously many different systems, whether it's Elastic, Grafana, ServiceNow, Datadog, Elastic.
The Rise and Fall of the Vector DB category: Jo Kristian Bergum (ex-Chief Scientist, Vespa)
- ▶ 4:26 Jo Kristian Bergum And you have it also in more traditional search engines like Elasticsearch, Solar, Vespa.
- ▶ 10:10 unnamed speaker Your search system, like Elasticsearch is typically like you duplicate your, whatever your, uh, main, uh, uh, storage or record is.
- ▶ 14:07 unnamed speaker Um, then they could split it out to maybe use Elasticsearch or Vespa.
The Four Wars of the AI Stack - Dec 2023 Recap
- ▶ 56:19 unnamed speaker Redis, Elasticsearch.
Beating GPT-4 with Open Source Models - with Michael Royzen of Phind
- ▶ 8:15 Michael Royzen Um, the demo itself, it used, I think, BART as the model, and in the notebook, it had support for both, um, an elastic search, um, index of Wikipedia, as well as a dense index, um, powered by Facebook's face. 4 times in the scene