Unlike standalone vector engines, Lantern enables seamless combination of relational data and vector data for applications. Tap into the power of embedding models and large language models to easily build data-driven applications.
Lantern benchmarks outperform pgvector, the only other PostgreSQL extension on the market. Our commitment to open-source means Lantern is free for all. Benefit from the ever-growing enhancements and features that developers passionately contribute to.
Extensions like pgvector utilize IVFflat, an algorithm that leads to clunky index management requirements and poor performance at scale. We built a vector database using a better algorithm, HNSW, to enable better throughput, latency, recall, and scalability.
Index legal documents like contracts, briefs, filings etc. by passing them through an LLM to generate vector embeddings. Vectorize legal document drafts to find similar documents that may lead to better final products.
Ingest your private company data, vectorize and store your data in Lantern. Build applications that allow Large Language Models like OpenAI to reference the data and generate responses
Embed transactional data, like past legitimate and fraudulent transactions such as purchase history, web/app activity, geolocation, etc. Use vector search to determine if new transactions are fraudulent.
Self-hosted enterprise solution
Integrations with LangChain and LlamaIndex
Cloud hosted Lantern database
Industry specific templates for building applications
Benchmarking key performance metrics against prominent vector databases