Lantern
Lantern is an open-source PostgreSQL vector database extension that provides high-performance vector search capabilities for building AI applications.
http://lantern.dev/
Product Information
Updated:Nov 9, 2024
What 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.
Key Features of Lantern
Lantern is a powerful PostgreSQL vector database extension designed for building AI applications. It offers fast vector indexing, efficient search capabilities, and easy embedding generation. Lantern provides a managed cloud service as well as self-hosting options, allowing developers to leverage vector search within their existing Postgres databases. With features like one-click vector generation, support for multiple embedding models, and cost-effective scalability, Lantern aims to simplify the development of AI-powered applications.
Fast Vector Indexing: Lantern's index creation is 30x faster than pgvector, enabling rapid setup of vector search capabilities.
One-Click Embedding Generation: Easily generate vector embeddings from unstructured data using over 20 supported embedding models with a single click.
Cost-Effective Scalability: Lantern offers high performance at a fraction of the cost compared to standalone vector databases, potentially saving up to 94% on cloud costs.
SQL and ORM Integration: Perform vector operations using familiar SQL queries or popular ORM libraries, simplifying integration with existing applications.
Managed Cloud Service: Lantern Cloud provides a fully managed database offering with support for embedding generation and management.
Use Cases of Lantern
AI-Powered Search Systems: Implement semantic search in applications by leveraging vector embeddings to find similar content or documents.
Recommendation Engines: Build personalized recommendation systems using vector similarity to suggest products, content, or services to users.
Natural Language Processing Applications: Develop chatbots, text classification, or sentiment analysis tools using vector representations of text data.
Image and Video Analysis: Create systems for image recognition, visual search, or content-based video retrieval using vector embeddings of visual data.
Fraud Detection: Implement anomaly detection systems in financial services by comparing transaction patterns using vector similarity.
Pros
Integrates seamlessly with existing PostgreSQL databases
Offers significant cost savings compared to standalone vector databases
Provides both managed cloud and self-hosted options for flexibility
Supports a wide range of embedding models and easy vector generation
Cons
Relatively new product, may have less community support than more established solutions
Limited to PostgreSQL environments, not suitable for users of other database systems
May require some learning curve for developers not familiar with vector databases
How to Use Lantern
Sign up for Lantern Cloud: Go to lantern.dev and click 'Try Lantern for Free' to create a free account. No credit card is required.
Create a database: After signing up, create a new Postgres database with Lantern enabled.
Connect to your database: Use the provided connection details to connect to your Lantern-enabled Postgres database using your preferred method (e.g. psql, application code, etc.).
Create a table with a vector column: Execute SQL to create a table that includes a column for storing vector embeddings, e.g. 'CREATE TABLE books (id SERIAL PRIMARY KEY, book_embedding REAL[3]);'
Insert vector data: Insert vector embeddings into your table, e.g. 'INSERT INTO books (book_embedding) VALUES ('{0,1,0}'), ('{3,2,4}');'
Create an HNSW index: Create a Lantern HNSW index on your vector column for faster queries, e.g. 'CREATE INDEX book_index ON books USING lantern_hnsw(book_embedding dist_l2sq_ops) WITH (M=2, ef_construction=10, ef=4, dim=3);'
Perform vector similarity search: Use SQL to query for similar vectors, e.g. 'SELECT id FROM books ORDER BY book_embedding <-> '{0,0,0}' LIMIT 1;'
Generate embeddings (optional): Use Lantern's built-in embedding generation to create vectors from text or images, e.g. 'SELECT id FROM books ORDER BY book_embedding <-> text_embedding('BAAI/bge-base-en', 'My text input') LIMIT 1;'
Lantern FAQs
Lantern is a hosted Postgres vector database and toolkit for developers to build high-performance AI applications. It offers vector search capabilities, embedding generation, and efficient indexing.
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Analytics of Lantern Website
Lantern Traffic & Rankings
2.9K
Monthly Visits
#5552939
Global Rank
#35259
Category Rank
Traffic Trends: Jul 2024-Nov 2024
Lantern User Insights
00:01:01
Avg. Visit Duration
1.89
Pages Per Visit
59.57%
User Bounce Rate
Top Regions of Lantern
US: 53.71%
VN: 18.81%
IN: 12.08%
DE: 10.07%
GB: 3.76%
Others: 1.58%
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