📌 TL;DR​

Lantern is a PostgreSQL vector database extension for building AI applications. Install and use our extension here.

🚀 Features today + Coming soon​

We have the most complete feature set of all the PostgreSQL vector database extensions.

Here’s what we support today:

• Creating an AI application end to end without leaving your database (example)
• Embedding generation for popular use cases (CLIP model, Hugging 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
• Request a feature at support@lantern.dev

📈 Performance + Benchmarks​

Lantern is a PostgreSQL extension that creates an index to efficiently search for similar vectors.

Important takeaways:

• 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.
• We plan to continue to make performance improvements to ensure we are the best perf