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.
Build an autonomous customer support agent using LangGraph and LanceDB that automatically fetches, classifies, drafts, and responds to emails with RAG-powered policy retrieval.
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.
One of the reasons we started the Lance file format and have been investigating new encodings is because we wanted a format with better support for random access.
In this blog, we’ll explore how to build a chat application that interacts with CSV and Excel files using LanceDB’s hybrid search capabilities.
Remember flipping through coding manuals? Those quickly became relics with the rise of Google and Stack Overflow, a one-stop shop for developer queries.
Improve retrieval quality by reranking LanceDB results with Cohere and ColBERT. You’ll plug rerankers into vector, FTS, and hybrid search and compare accuracy on real datasets.
Explore about tokens per second is not all you need. Get practical steps, examples, and best practices you can use now.