Find anything fast with Google's vector search technology
I built multiple systems using vector search, one of them demoed in a search engine for non-commercial content at <a href="http:&#x2F;&#x2F;teclis.com" rel="nofollow">http:&#x2F;&#x2F;teclis.com</a><p>Running vector search (also sometimes referred to as semantic search, or a part of semantic search stack) is a trivial matter with open-source libraries like Faiss <a href="https:&#x2F;&#x2F;github.com&#x2F;facebookresearch&#x2F;faiss" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;facebookresearch&#x2F;faiss</a><p>It takes 5 minutes to set up. You can search billion vectors on common hardware. For low-latency (up to couple of hundred milliseconds) use cases, it is highly unlikely that any cloud solution like this would be a better choice than something deployed on premise because of the network overhead.<p>(worth noting is that there are about two dozen vector search libraries, all benchmarked at <a href="http:&#x2F;&#x2F;ann-benchmarks.com&#x2F;" rel="nofollow">http:&#x2F;&#x2F;ann-benchmarks.com&#x2F;</a> and most of them open-source)<p>A much more interesting (and harder) problem is creating good vectors to begin with. This refers to the process of converting a text or an image to a multidimensional vector, usually done by a machine learning model such as BERT (for text) or ImageNet (for images).<p>Try entering a query like &#x27;gpt3&#x27; or &#x27;2019&#x27; into the news search demo linked in the Google&#x27;s PR:<p><a href="https:&#x2F;&#x2F;matchit.magellanic-clouds.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;matchit.magellanic-clouds.com&#x2F;</a><p>The results are nonsensical. Not because the vector search didn&#x27;t do its job well, but because generated vectors were suboptimal to begin with. Having good vectors is 99% of the semantic search problem.<p>A nice demo of what semantic search can do is Google&#x27;s Talk to Books <a href="https:&#x2F;&#x2F;books.google.com&#x2F;talktobooks&#x2F;" rel="nofollow">https:&#x2F;&#x2F;books.google.com&#x2F;talktobooks&#x2F;</a><p>This area of research s fascinating. For those who want to play with this more, an interesting end-to-end (including both vector generation and search) open-source solution is Haystack <a href="https:&#x2F;&#x2F;github.com&#x2F;deepset-ai&#x2F;haystack" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;deepset-ai&#x2F;haystack</a>