Agentic RAG Using LangGraph: Build an Autonomous Customer Support Agent
Build an autonomous customer support agent using LangGraph and LanceDB that automatically fetches, classifies, drafts, and responds to emails with RAG-powered policy retrieval.
Implement Contextual Retrieval and Prompt Caching with LanceDB
Unlock about implement contextual retrieval and prompt caching with lancedb. Get practical steps, examples, and best practices you can use now.
Training a Variational AutoEncoder from Scratch with Lance File Format
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.
The Case for Random Access I/O
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.
Chat with Your Stats Using Langchain Dataframe Agent & LanceDB Hybrid Search
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.
Developers, Ditch the Black Box: Welcome to Continue
Remember flipping through coding manuals? Those quickly became relics with the rise of Google and Stack Overflow, a one-stop shop for developer queries.
Benchmarking Cohere Rerankers with LanceDB
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.
Tokens per Second Is NOT All You Need
Explore about tokens per second is not all you need. Get practical steps, examples, and best practices you can use now.
Convert Any Image Dataset to Lance
In our article, we explored the remarkable capabilities of the Lance format, a modern, columnar data storage solution designed to revolutionize the way we work with large image datasets in machine learning.