RAG Isn't One-Size-Fits-All: Here's How to Tune It for Your Use Case
Great RAG comes from a tight iteration loop. Learn how to systematically improve each layer of your RAG system using Kiln and LanceDB.
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Introducing Lance Data Viewer: A Simple Way to Explore Lance Tables
A lightweight open source web UI for exploring Lance datasets, viewing schemas, and browsing table data with vector visualization support.
Chunking Analysis: Which is the right chunking approach for your language?
Explore chunking analysis: which is the right chunking approach for your language? with practical insights and expert guidance from the LanceDB team.
Building RAG on codebases: Part 2
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 RAG on codebases: Part 1
Building a Cursor-like @codebase RAG solution. Part 1 focuses on indexing techniques, chunking strategies, and generating embeddings in LanceDB.
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.
Multi Document Agentic RAG: a Walkthrough
Unlock about multi document agentic rag: a walkthrough. Get practical steps, examples, and best practices you can use now.
Zero Shot Image Classification with Vector Search
Get about zero shot image classification with vector search. Get practical steps, examples, and best practices you can use now.