I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Tomorrow: Agent Memory in Turbopuffer
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Hey all, Just a note tomorrow I'll be giving a talk on High-Scale Agent Memory in Turbopuffer Its a first step into a larger world! Agent memory helps new sessions feel like they're continuing a conversation not restarting one. Come, have fun, see the mistakes I've made, and get an intro to Turbopuffer. Event link - https://maven.com/p/e48f16/high-scale-agentic-memory-with-turbopuffer Cheers, -Doug Slack Community * Events · Consulting · Training (use code search-tips) You're subscribed to Doug Turnbull's daily search tips where I share tips, blog articles, events, and more. You can always manage your profile: |
Doug Turnbull
I share search tips, blog articles, and free events I'm hosting about the search+retreval industry, vector databases, information retrieval and more.
Over the next two weeks, Trey Grainger and I are hosting a free online conference, The Future of AI Search w/ talks from some of your favorite search people. The lineup Unreasonable Effectiveness of BM25 for Agentic Search by Hornet.dev founder Jo Kristian-Bergum Introducing Luxir: Can we build the fastest search engine? by Solr creator Yonik Seeley Building topk-embed-v1: frontier multi-vector embeddings by Marek Galovic, CEO Top K Can a Decision Model Think like a Search Engineer by David...
In a Jev reranker you ask Jev to give P(Yes) of the answer to a question like Is this document relevant to the query? And you rank by P(yes) It works! But... that's kinda boring. I think we can do better: we could also have an LLM generate the questions per query. IE query "desk for kids" has us evaluate products with criteria like Is it less than 36 inches? Is it a playful design? Does it explicitly say its designed for children? Then rerank this query using the sum of P(yes) of those...
This week the Retrieval Augmented Gathering online conference continues! Last week we explored index-only SPLADE, got a demo of tensors in the search engine, and how to think of agentic memory as telemetry. This week we've got talks below: Embeddings don't solve RAG (today) Solving retrieval for agents isn't as simple as chunk + embed. For one, similarity is not relevance. An decides what's relevant depending on not just raw text, but everything that 'surrounds' it. Places it in context....