lanterndata

lantern

Rust2023–2024

lanterndata / lantern

lantern

PostgreSQL vector database extension for building AI applications

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6Mentions

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896GitHub stars

REPOSITORY AT A GLANCE

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What it is

PostgreSQL vector database extension for building AI applications

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week6 dated mentions

Observed mentions: 2023-09-13 17:41:58 UTC to 2024-02-06 16:47:50 UTC. Weeks start Monday, UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges

UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Submissions	Comments
Sep 11	2023-09-11 00:00:00 UTC	2023-09-18 00:00:00 UTC	1	1	0
Sep 18	2023-09-18 00:00:00 UTC	2023-09-25 00:00:00 UTC	0	0	0
Sep 25	2023-09-25 00:00:00 UTC	2023-10-02 00:00:00 UTC	0	0	0
Oct 2	2023-10-02 00:00:00 UTC	2023-10-09 00:00:00 UTC	0	0	0
Oct 9	2023-10-09 00:00:00 UTC	2023-10-16 00:00:00 UTC	0	0	0
Oct 16	2023-10-16 00:00:00 UTC	2023-10-23 00:00:00 UTC	0	0	0
Oct 23	2023-10-23 00:00:00 UTC	2023-10-30 00:00:00 UTC	0	0	0
Oct 30	2023-10-30 00:00:00 UTC	2023-11-06 00:00:00 UTC	0	0	0
Nov 6	2023-11-06 00:00:00 UTC	2023-11-13 00:00:00 UTC	0	0	0
Nov 13	2023-11-13 00:00:00 UTC	2023-11-20 00:00:00 UTC	0	0	0
Nov 20	2023-11-20 00:00:00 UTC	2023-11-27 00:00:00 UTC	0	0	0
Nov 27	2023-11-27 00:00:00 UTC	2023-12-04 00:00:00 UTC	0	0	0
Dec 4	2023-12-04 00:00:00 UTC	2023-12-11 00:00:00 UTC	0	0	0
Dec 11	2023-12-11 00:00:00 UTC	2023-12-18 00:00:00 UTC	0	0	0
Dec 18	2023-12-18 00:00:00 UTC	2023-12-25 00:00:00 UTC	0	0	0
Dec 25	2023-12-25 00:00:00 UTC	2024-01-01 00:00:00 UTC	0	0	0
Jan 1	2024-01-01 00:00:00 UTC	2024-01-08 00:00:00 UTC	3	0	3
Jan 8	2024-01-08 00:00:00 UTC	2024-01-15 00:00:00 UTC	0	0	0
Jan 15	2024-01-15 00:00:00 UTC	2024-01-22 00:00:00 UTC	0	0	0
Jan 22	2024-01-22 00:00:00 UTC	2024-01-29 00:00:00 UTC	1	0	1
Jan 29	2024-01-29 00:00:00 UTC	2024-02-05 00:00:00 UTC	0	0	0
Feb 5	2024-02-05 00:00:00 UTC	2024-02-12 00:00:00 UTC	1	0	1

What Hacker News says

6 sources

SparkyMcUnicorn·Feb 6, 2024

pgvector[0] is supported by AWS RDS, Azure, etc. I haven't gotten a chance to try out Latern[1] yet, but have heard some good things[2]. [0] https://github.com/pgvector/pgvector [1] https://github.com/lanterndata/lantern [2] https://tembo.io/blog/postgres-vector-search-pgvector-and-la...

In PostgreSQL is enough

yolovoe·Jan 26, 2024

Traditional DBs already kinda support vector DBs via pg_vector extensions and such. There is a YC startup, latnern, that also built their own extension for postgres that is open source and is better for vector DB use cases: https://github.com/lanterndata/lantern But yeah! Traditional DBs already support this, if you consider this extension to be part of Postgres.

In Are we at peak vector database?

tristan957·Jan 2, 2024

This extension is licensed under the Business Source License[0], which makes it incompatible with most DBaaS offerings. The BSL is a closed-source license. Good choice for Lantern, but unusable for everyone else. Some Postgres offerings allow you to bring your own extensions to workaround limitations of these restrictive licenses, for instance Neon[1], where I work. I tried to look at the AWS docs for you, but couldn't find anything about that. I did find Trusted Language Extensions[2], but that seems to be more about writing your own extension. Couldn't find a way to upload arbitrary extensions. I will add that you could use logical replication[3] to mirror data from your primary database into a Lantern-hosted database (or host your own database with the Lantern extension). This obviously has a couple downsides, but thought I would menti

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on it. [0]: https://github.com/lanterndata/lantern/commit/dda7f064ca80af... [1]: https://neon.tech/docs/extensions/pg-extensions#custom-built... [2]: https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Postg... [3]: https://www.postgresql.org/docs/current/logical-replication....

In 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

diqi·Jan 2, 2024

Ah, thank you for noticing! We actually have typo in the error message. It actually should be the operator <?> instead of <->. There's some context on the operator <?> here: https://github.com/lanterndata/lantern?tab=readme-ov-file#a-...

In 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

mattashii·Jan 2, 2024

I'm also concerned about these (tested!) errors: > https://github.com/lanterndata/lantern/blob/040f24253e5a2651... > Operator <-> can only be used inside of an index Isn't the use of the distance operator in scan+sort critical for generating the expected/correct result that's needed for validating the recall of an ANN-only index?

In 90x Faster Than Pgvector – Lantern's HNSW Index Creation Time

ngalstyan4·Sep 13, 2023Submission

Show HN: Lantern – a PostgreSQL vector database for building AI applications

We are excited to share Lantern! Lantern is a PostgreSQL vector database extension for building AI applications. Install and use our extension here: https://github.com/lanterndata/lantern 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. 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. ** Here’s what we support today ** - Creating an AI application end to end without leaving your database (example: https://github.com/ezra-varady/lanterndb-semantic-image-sear...) - Embedding generation for popular use cases (CLIP model, Hugg

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ing Face models, custom model) - Interoperability with pgvector's data type, so anyone using pgvector can switch to Lantern - 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. ** Here’s what’s coming soon ** - Cloud-hosted version of Lantern - Templates and guides for building applications for different industries - Tools for generating embeddings (support for third party model API's, more local models) - Support for version control and A/B test embeddings - Autotuned index type that will choose appropriate index creation parameters - 1 byte and 2 byte vector elements, and up to 8000 dimensional vectors support ** Why we started Lantern today ** There's dozens of vector databases on the market, but no enterprise option built on top of PostgreSQL. We think it's super important to build on top of PostgreSQL - Developers know how to use PostgreSQL. - Companies already store their data on PostgreSQL. - 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. We are open source and excited to have community contributors! Looking forward to hearing your feedback!

196 points · Open discussion