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Store Embeddings
Generate Embeddings
Automatic Embedding Generation
Calculate Distance
Query Embeddings
Create Index
Quantization
Asynchronous Tasks
Weighted Vector Search
Troubleshooting
Single Operator
Postgres Notes
Security

Languages

Javascript
Python
Ruby
Rust

Migrate

Migrate from Postgres to Lantern Cloud
Migrate from pgvector to Lantern Cloud
Migrate from pgvector to self-hosted Lantern
Migrate from Pinecone to Lantern Cloud

Lantern HNSW

Installation

Lantern Extras

Installation
Generate Embeddings
Lantern Daemon

Lantern CLI

Installation
Generate Embeddings
Indexing Server
Daemon
Autotune Index
Product Quantization

Contributing

Dockerfile
Visual Studio Code

Develop

Get Started

Lantern Cloud

The easiest way to get started with all of our tools is with Lantern Cloud.

In Lantern Cloud, you can create create a database with just a few clicks. Once you load your data into Lantern, you can generate embeddings with a single click from dozens of provided open source or proprietary embedding models. You can then create a vector index from your dashboard or run an index-tuning experiment to choose the best parameters for index creation.

Ubicloud

Lantern is also available as a managed offering on Ubicloud. You'll still receive direct support from the Lantern team for instances on Ubicloud.

Ubicloud is an open-source alternative to AWS. It offers cloud services like virtual machines, block storage, and managed Postgres at rates 2-10x lower than hyperscalers like AWS, Azure, and GCP.

Self-Host

Alternatively, you can also use our tools locally or self-host them. There are three tools that are provided out-of-the-box with Lantern Cloud.

  • Lantern HNSW, our core Postgres extension, provides vector search in Postgres.
  • Lantern Extras, which further extends Postgres to support embedding generation.
  • Lantern CLI provides routines for generating embeddings and indexes.

You can install the tools individually by following the instructions linked.

Overview

Here is a non-comprehensive overview of what you can do with Lantern. The examples below use SQL, but we also provide examples for Python, JavaScript, Ruby, and Rust.

Create a table with an embedding column

sql
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CREATE TABLE books (id SERIAL PRIMARY KEY, book_embedding REAL[3]);

Generate embeddings

sql
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SELECT text_embedding('BAAI/bge-base-en', 'My text input');
SELECT openai_embedding('openai/text-embedding-ada-002', 'My text input');

Insert embeddings

sql
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INSERT INTO books (book_embedding) VALUES ('{0,1,0}'), ('{3,2,4}');

Calculate distance and select nearest rows

sql
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SELECT book_embedding <-> '{0,0,0}' FROM books
    ORDER BY book_embedding <-> '{0,0,0}' LIMIT 1;

Create an index to speed up nearest neighbor search

sql
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CREATE INDEX book_index ON books USING lantern_hnsw(book_embedding dist_l2sq_ops)
    WITH (M=2, ef_construction=10, ef=4, dim=3);

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  • Lantern Cloud
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