Lantern Introduction
Lantern is an open-source PostgreSQL vector database extension that provides high-performance vector search capabilities for building AI applications.
View MoreWhat is Lantern
Lantern is a powerful database solution designed specifically for developing AI applications. It extends PostgreSQL with advanced vector search capabilities, allowing developers to efficiently work with vector data. Lantern offers a fully managed cloud service called Lantern Cloud, which provides a hosted Postgres vector database along with tools for embedding generation and management. The platform aims to make it easy for developers to add vector search functionality to their applications while leveraging the familiar PostgreSQL environment.
How does Lantern work?
Lantern works by adding vector operations and indexing capabilities to PostgreSQL. It allows users to create tables with vector columns and build efficient indexes for similarity search using algorithms like HNSW (Hierarchical Navigable Small World). Developers can perform vector searches using SQL queries or through client libraries in various programming languages. Lantern Cloud provides a managed service where users can create databases, generate embeddings from unstructured data using various models, and perform vector operations without having to manage the underlying infrastructure. The platform supports both SQL and popular ORMs for interacting with the vector database.
Benefits of Lantern
Using Lantern offers several key benefits for AI application developers. It provides significantly faster index creation compared to alternatives like pgvector, with claims of up to 30x faster performance. Lantern can also be much more cost-effective, potentially saving up to 94% on cloud costs compared to standalone vector databases. By integrating vector search directly into PostgreSQL, Lantern eliminates the need for separate vector and relational databases, reducing complexity and maintenance overhead. The platform's ease of use, scalability, and support for multiple embedding models make it an attractive option for developers looking to incorporate vector search capabilities into their AI-driven projects.
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