Late Interaction & Efficient Multi-modal Retrievers Need More Than a Vector Index
Explore late interaction & efficient multi-modal retrievers need more than a vector index with practical insights and expert guidance from the LanceDB team.
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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.
My Summer Internship Experience at LanceDB
I'm Raunak, a master's student at the University of Illinois, Urbana-Champaign. This summer, I had the opportunity to intern as a Software Engineer at LanceDB, an early-stage startup based in San Francisco.
Columnar File Readers in Depth: APIs and Fusion
The API used to read files has evolved over time, from simple full table reads to batch reads and eventually to iterative record batch readers. Lance takes this a step further to return a stream of read tasks.
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.
Columnar File Readers in Depth: Parallelism without Row Groups
Explore columnar file readers in depth: column shredding with practical insights and expert guidance from the LanceDB team.
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.
Lance v2: A New Columnar Container Format
Explore lance v2: a new columnar container format with practical insights and expert guidance from the LanceDB team.
Effortlessly Loading and Processing Images with Lance: a Code Walkthrough
Working with large image datasets in machine learning can be challenging, often requiring significant computational resources and efficient data-handling techniques.