vespa.ai

Will Vespa be killed by Claude?🤔deathbyclawd.com/?url=vespa.ai

Filters are everywhere in real-world vector search, and they're quietly killing your recall and latency. Join Radu Gheorghe and search veteran Doug Turnbull (formerly Reddit, Shopify, Wikipedia) for a practical deep dive into: ✅ Why HNSW struggles with filtered queries ✅ Strategies like ACORN-1, brute force kNN, post-filtering, overfetching & adaptive beam search ✅ How to use HNSWTuner + VespaNNParameterOptimizer to find your optimal settings Most teams building vector search don't think about this until it bites them in production. This is your chance to get ahead of it. 🔍 Tuning HNSW Parameters for Filtered Search 📅 April 16, 2026 | 5:00 PM UTC, 2PM EST 👉 Register here: maven.com/p/601c82/tunin…

Seriously now, it should be insightful for anyone doing vector search. Ideally with @vespaengine but not necessarily (there are common techniques there). Sign up at maven.com/p/601c82/tunin…

Everyone is talking about AI agents. But the hard part isn’t the agent. It’s retrieval. Agents need to search, filter, rank, and reason over huge amounts of data in real time. Without that foundation, they hallucinate or miss critical signals. In our latest blog, learn how Metal is building agent-driven intelligence on Vespa Cloud, powering AI agents with real-time retrieval and ranking. 👉 Read the story: blog.vespa.ai/agent-driven-i…

#AI introduces new expectations around speed, relevance, and adaptability. In this discussion with experts from @vespaengine, learn how a practical view of the workings of AI in digital commerce reduces complexity by simplifying how systems work together. em360tech.com/podcasts/how-s….

The blog post contains more details and advice: blog.vespa.ai/embedding-trad…

Access the dashboard comparing all eligible embedding models on HuggingFace: huggingface.co/spaces/vespa-e…

Choosing the right embedding model for search. A hard problem because this impacts memory and cpu/gpu usage, latency, and quality. So, we built an interactive dashboard where you can compare all the leading open models. ⬇️

GigaOm has just released its latest Radar for Vector Databases, now in its third edition. Thanks to @vespaengine thenewstack.io/thoughts-on-th…

The December Vespa newsletter is out. 🚢New features shipped: - Automated ANN performance tuning - Accelerated vector distance computations - Precise lexical matching in chunks - NEAR lexical matching with exclusion - Create tensors from structs in ranking - Inner rank profiles - Quantiles in grouping - Streamed JSONL output from visiting And so much content to help you build better faster. Link 👇

Du you understand tensors? 🎄The advent of tensors starts today: - A new fun puzzle every day. - Solutions are posted the next day. - There will be prizes! Link 👇

From this great article: vinted.engineering/2025/11/18/den…

When you use Vespa instead of spending your days on plumbing

In the next few weeks, you will learn how to build a production grade search in a stepwise manner. Starting from basic bm25 to embeddings, hybrid search to a fully fledged RAG application that works on millions (and billions) of documents. Keep an eye out on my youtube channel!

In our latest episode of the Vespa Voice podcast with Ravindra Harige, founder of Searchplex, we explore a common but often misdiagnosed issue in modern software: search problems hiding in plain sight. #episode-7-how-hidden-search-problems-derail-great-products" target="_blank" rel="nofollow noopener">vespa.ai/vespa-voice/#e…

This hack isn't for everyone, but in some situations it's possible and useful to turn off summary fetching.

If you want to create your own RAG application with this level of quality, clone the open source RAG Blueprint. github.com/vespa-engine/s…