November Feature Roundup Lance On November 13th, we released Lance 0.19.2. This release includes several new features and improvements, including: * Flexible handling of data for inserts
🎉 2 Million Monthly Downloads to Celebrate the Holiday Season 🔥2 Million Monthly Downloads🔥 * We are so excited to announce that we surpassed 𝟮 𝗠𝗶𝗹𝗹𝗶𝗼𝗻 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱𝘀 per month across Python, Typescript, Java, and Rust. Our community is indexing
LLMs powering Computer Vision tasks 💡This is a community post by Prashant Kumar Computer vision turns visual data into valuable insights, making it exciting. Now, with Large Language Models (LLMs)
An attempt to build cursor's @codebase feature - RAG on codebases - part 2/2 💡This is a community post by Sankalp Shubham What this looks like: Quick recap I recommend reading Part1 before you read Part 2. Part 1
An attempt to build cursor's @codebase feature - RAG on codebases - part 1/2 💡This is a community post by Sankalp Shubham The project will look something like this: Introduction If you have used the Cursor code editor'
LanceDB Enterprise Architecture, Lance Community Office Hour, Petabyte Scale Multimodal AI 🔥LanceDB Enterprise Documentation LanceDB Enterprise transforms your data lake into a high performance vector database that can operate at extreme scale. Serve millions of tables
Customer support bot with OpenAI's Swarm Agent 💡This is a community blog by Prashant Kumar Difference between an assistant and a Swarm Agent- an assistant is a simple sequential program without function
Practical introduction to Late Chunking or Chunked Pooling 💡This is a community blog by Mahesh Deshwal Improved Contextual Retrieval over large Documents for RAG applications The pronoun is the King (or Queen), and
Columnar File Readers in Depth: Backpressure Streaming data applications can be tricky. When you can read data faster than you can process the data then bad things tend to happen. The
Advanced RAG with context enrichment window .... and some advanced filtering and re-ranking technique We all know what's Vanilla RAG, how it works, and how to use it but sometimes
Foundation of Compound AI Meetup Kicking Off, More LanceDB Integrations Community contributions 💡Dspy now works with LanceDB. LanceDB helps DSPy by quickly storing and retrieving information. This lets DSPy find relevant details faster, making prompts
Implement contextual retrieval and prompt caching with LanceDB Decrease chunk retrieval failure rate by 35% to 50% Have you ever worked on a task where your users search in one language and the
Late interaction & efficient Multi-modal retrievers need more than a vector index A lot has happened in the last few months in AI. Let's look at what's new with document retrieval. Try this
