<!-- SC_OFF --><div class="md"><p>Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for
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<p>Whenever someone asks me to add search to an application, I try to find ways around it. The implementation itself isn't the problem, it's everything that comes with it: extra
<p>Most semantic search systems don’t fail because of embeddings. They fail because of how the system is designed around them.</p> <p>When I started building a semantic search API
<p>Our careers page search often felt broken. Searching for “Java” could return JavaScript roles. A misspelling could produce no results at all. Searching for “manager” could
<p>Some time ago at my work, we had to implement a small search engine for help articles. The search was very simple: it used lexical, prefix-based matching. This was a problem
<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked
<p>I have helped a number of teams pick a vector database in the last year. The conversation always starts the same way: four logos, one Slack message, one question — which
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