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.
Remember flipping through coding manuals? Those quickly became relics with the rise of Google and Stack Overflow, a one-stop shop for developer queries.
Explore columnar file readers in depth: column shredding with practical insights and expert guidance from the LanceDB team.
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.
In our last blog, we talked about chunking and why it is necessary for processing data through LLMs. We covered some simple techniques to perform text chunking.
Explore lance v2: a new columnar container format with practical insights and expert guidance from the LanceDB team.
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.
Working with large image datasets in machine learning can be challenging, often requiring significant computational resources and efficient data-handling techniques.