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Today we are shipping Polars 2.0. In the earlier announcement post we went through the rationale of the version bump. This post we will discuss what features 2.0 brings. Even though we didn’t intend to make it a big feature release, it still packs a lot to get enthousiastic about.
Learn how to use Marimo for interactive data analysis with Python, Pandas, and Altair, and turn a reactive notebook into a simple shareable dashboard.
GPU-Accelerated HNSW Indexing in Qdrant Time: 45 min Level: Intermediate Output: GitHub Since Qdrant v1.13, Qdrant has supported GPU-accelerated Hierarchical Navigable Small World (HNSW) indexing on self-hosted instances.
Prevent Unoptimized Usage Time: 20 min Level: Intermediate Output: GitHub After a bulk upload or a configuration change, a Qdrant collection can see higher search latency for a while.
Compressed Multivector Search Time: 25 min Level: Intermediate Output: GitHub Multivectors let a model like ColBERT represent a document as one vector per token instead of one vector per document, which improves retrieval quality through late interaction, at the cost of storing many more vectors…
The mall 0.2.0 update for R and Python introduces support for external LLM providers like OpenAI and Gemini. This version also features parallel processing for R users, the ability to run NLP on string vectors in Python, and a brand new cheatsheet.
We are proud to introduce the {mall} package. With {mall}, you can use a local LLM to run NLP operations across a data frame. (sentiment, summarization, translation, etc). {mall} has been simultaneously released to CRAN and PyPi (as an extension to Polars).
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