<story><title>Show HN: Lantern – a PostgreSQL vector database for building AI applications</title><url>https://docs.lantern.dev/blog/2023/09/13/hello-world</url><text>We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications. 
Install and use our extension here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;lanterndata&amp;#x2F;lantern&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;lanterndata&amp;#x2F;lantern&lt;/a&gt;&lt;p&gt;We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search.&lt;p&gt;There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.&lt;p&gt;** Here’s what we support today **&lt;p&gt;- Creating an AI application end to end without leaving your database (example: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;ezra-varady&amp;#x2F;lanterndb-semantic-image-search&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;ezra-varady&amp;#x2F;lanterndb-semantic-image-sear...&lt;/a&gt;)&lt;p&gt;- Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)&lt;p&gt;- Interoperability with pgvector&amp;#x27;s data type, so anyone using pgvector can switch to Lantern&lt;p&gt;- Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.&lt;p&gt;** Here’s what’s coming soon **&lt;p&gt;- Cloud-hosted version of Lantern&lt;p&gt;- Templates and guides for building applications for different industries&lt;p&gt;- Tools for generating embeddings (support for third party model API&amp;#x27;s, more local models)&lt;p&gt;- Support for version control and A&amp;#x2F;B test embeddings&lt;p&gt;- Autotuned index type that will choose appropriate index creation parameters&lt;p&gt;- 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support&lt;p&gt;** Why we started Lantern today **&lt;p&gt;There&amp;#x27;s dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it&amp;#x27;s super important to build on top of PostgreSQL&lt;p&gt;- Developers know how to use PostgreSQL.&lt;p&gt;- Companies already store their data on PostgreSQL.&lt;p&gt;- Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data.&lt;p&gt;We are open source and excited to have community contributors!
Looking forward to hearing your feedback!</text></story><parent_chain></parent_chain><comment><author>bryan0</author><text>&amp;gt; Switch from pgvector, get FREE AirPods Pro.&lt;p&gt;&amp;gt; Book some time here, and we will help switch you over for FREE and get you a pair of FREE AirPods Pro&lt;p&gt;This just comes off as sketchy to me. If the tech is good it will stand on its own.</text></comment>
<story><title>Show HN: Lantern – a PostgreSQL vector database for building AI applications</title><url>https://docs.lantern.dev/blog/2023/09/13/hello-world</url><text>We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications. 
Install and use our extension here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;lanterndata&amp;#x2F;lantern&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;lanterndata&amp;#x2F;lantern&lt;/a&gt;&lt;p&gt;We have the most complete feature set of all the PostgreSQL vector database extensions. Our database is built on top of usearch — a state of the art implementation of HNSW, the most scalable and performant algorithm for handling vector search.&lt;p&gt;There’s three key metrics we track. CREATE INDEX time, SELECT throughput, and SELECT latency. We match or outperform pgvector and pg_embedding (Neon) on all of these metrics.&lt;p&gt;** Here’s what we support today **&lt;p&gt;- Creating an AI application end to end without leaving your database (example: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;ezra-varady&amp;#x2F;lanterndb-semantic-image-search&quot;&gt;https:&amp;#x2F;&amp;#x2F;github.com&amp;#x2F;ezra-varady&amp;#x2F;lanterndb-semantic-image-sear...&lt;/a&gt;)&lt;p&gt;- Embedding generation for popular use cases (CLIP model, Hugging Face models, custom model)&lt;p&gt;- Interoperability with pgvector&amp;#x27;s data type, so anyone using pgvector can switch to Lantern&lt;p&gt;- Parallel index creation capabilities -- Support for creating the index outside of the database and inside another instance allows you to create an index without interrupting database workflows.&lt;p&gt;** Here’s what’s coming soon **&lt;p&gt;- Cloud-hosted version of Lantern&lt;p&gt;- Templates and guides for building applications for different industries&lt;p&gt;- Tools for generating embeddings (support for third party model API&amp;#x27;s, more local models)&lt;p&gt;- Support for version control and A&amp;#x2F;B test embeddings&lt;p&gt;- Autotuned index type that will choose appropriate index creation parameters&lt;p&gt;- 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support&lt;p&gt;** Why we started Lantern today **&lt;p&gt;There&amp;#x27;s dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it&amp;#x27;s super important to build on top of PostgreSQL&lt;p&gt;- Developers know how to use PostgreSQL.&lt;p&gt;- Companies already store their data on PostgreSQL.&lt;p&gt;- Standalone vector databases have to rebuild all of what PostgreSQL has built for the past 30-years, including all of the optimizations on how to best store and access data.&lt;p&gt;We are open source and excited to have community contributors!
Looking forward to hearing your feedback!</text></story><parent_chain></parent_chain><comment><author>carlossouza</author><text>I&amp;#x27;m using pgvector in production, mainly in a table with 500k-1M rows.&lt;p&gt;My main use case is to return search results &lt;i&gt;with pagination&lt;/i&gt;: page 1 from 1-50, page 2 from 51-100, page 3 from 101-150, etc. (Think LIMIT and OFFSET).&lt;p&gt;After a lot of experimentation and help from pgvector&amp;#x27;s team, we discovered that, for this specific use case, IVFFLAT index is much faster than HNSW.&lt;p&gt;I looked at your documentation and only saw HNSW, no IVFFLAT.&lt;p&gt;What would be Lantern&amp;#x27;s performance for this specific use case?&lt;p&gt;Thx!</text></comment>