<story><title>Vector Databases: A Technical Primer [pdf]</title><url>https://tge-data-web.nyc3.digitaloceanspaces.com/docs/Vector%20Databases%20-%20A%20Technical%20Primer.pdf</url></story><parent_chain><item><author>ripley12</author><text>The &amp;quot;Do you need a Dedicated Vector Database?&amp;quot; slide is quite interesting, but doesn&amp;#x27;t really answer its own question! This is something I&amp;#x27;ve been wondering myself, if anyone has any guidelines or rules of thumb I would appreciate it.&lt;p&gt;I&amp;#x27;ve recently been using Simon Willison&amp;#x27;s (hi simonw) excellent `llm` tool that can help with embeddings, and it takes the simplest approach possible: just store embeddings in SQLite with a few UDFs for calculating distance etc.&lt;p&gt;The simplicity of that approach is very appealing, but presumably at some level of traffic+data an application will outgrow it and need a more specialized database. Does anyone have a good intuition for where that cutoff might be?</text></item></parent_chain><comment><author>estebarb</author><text>Some years ago I wrote a vector index that used LSH. Search was implemented in the most trivial way: scanning everything and comparing using hamming distance (xor and popcount). I was able to scan 200k hashes and find the most similar in less than 10ms, in a single core of a 2011 MBP.</text></comment>
<story><title>Vector Databases: A Technical Primer [pdf]</title><url>https://tge-data-web.nyc3.digitaloceanspaces.com/docs/Vector%20Databases%20-%20A%20Technical%20Primer.pdf</url></story><parent_chain><item><author>ripley12</author><text>The &amp;quot;Do you need a Dedicated Vector Database?&amp;quot; slide is quite interesting, but doesn&amp;#x27;t really answer its own question! This is something I&amp;#x27;ve been wondering myself, if anyone has any guidelines or rules of thumb I would appreciate it.&lt;p&gt;I&amp;#x27;ve recently been using Simon Willison&amp;#x27;s (hi simonw) excellent `llm` tool that can help with embeddings, and it takes the simplest approach possible: just store embeddings in SQLite with a few UDFs for calculating distance etc.&lt;p&gt;The simplicity of that approach is very appealing, but presumably at some level of traffic+data an application will outgrow it and need a more specialized database. Does anyone have a good intuition for where that cutoff might be?</text></item></parent_chain><comment><author>summarity</author><text>I’ve had some success scaling a quick and dirty engine (that powers findsight.ai) to tens of millions of vectors, details in the talk here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;youtu.be&amp;#x2F;elNrRU12xRc?t=1556&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;youtu.be&amp;#x2F;elNrRU12xRc?t=1556&lt;/a&gt;&lt;p&gt;That’s maybe 1kLOC so I didn’t need an external one after all.</text></comment>