Blog category:
OpenClaw and similar personal autonomous agents need a local-first long-term memory layer. LanceDB fits that role with embedded deployment, filesystem-native storage, and multimodal retrieval.
Great RAG comes from a tight iteration loop. Learn how to systematically improve each layer of your RAG system using Kiln and LanceDB.
A lightweight open source web UI for exploring Lance datasets, viewing schemas, and browsing table data with vector visualization support.
Explore chunking analysis: which is the right chunking approach for your language? with practical insights and expert guidance from the LanceDB team.
Building a Cursor-like @codebase RAG solution. Part 2 focuses on the generating embeddings and the retrieval strategy using a combination of techniques in LanceDB.
Building a Cursor-like @codebase RAG solution. Part 1 focuses on indexing techniques, chunking strategies, and generating embeddings in LanceDB.
Unlock about implement contextual retrieval and prompt caching with lancedb. Get practical steps, examples, and best practices you can use now.
Train a Variational Autoencoder end‑to‑end using Lance for fast, scalable data handling. You’ll set up the dataset, build the VAE in PyTorch, and run training, sampling, and reconstructions.
Unlock about multi document agentic rag: a walkthrough. Get practical steps, examples, and best practices you can use now.