☁️LanceDB Cloud Public Beta, Lance 2.1 , 🎮Game Studio Case Study ☁️ LanceDB Cloud Public Beta The wait is over! LanceDB Cloud is now in Public Beta! No more wait list; just sign-up, get an API key,
💼AnythingLLM's Competitive Edge: LanceDB for Seamless RAG and Agent Workflows AnythingLLM chose LanceDB as their vector database backbone to create a frictionless experience for developers and end-users alike. By leveraging LanceDB's serverless, setup-free
Lance File 2.1: Smaller and Simpler Almost a year ago I announced we were going to be embarking on a journey to build a new 2.0 version of our file
💼 Second Dinner's Secret Weapon: LanceDB-Powered RAG for Faster, Smarter Game Development 💡This is a case study contributed by the Second Dinner team and the LanceDB team. Second Dinner teamed up with LanceDB Cloud’s serverless architecture
RAG with GRPO Fine-Tuned Reasoning Model 💡This is a community post by Mahesh Deshwal Group Relative Policy Optimization is the series of RL techniques for LLMs to guide them to specific
Table Format for ML Workload, LanceDB x Microsoft at Iceberg Summit 🔥 New Blog: Designing a Table Format for ML Workloads Zero-copy schema evolution, indices for everything, and parallelized operations - all designed for modern ML. We
Fintech AI Agent From Scratch 💡This is community post by Vipul Maheshwari AI agents are popping up everywhere, and they’re only going to become more common in the next
Speaker Identification and Transcription Mapping Using Vector Search 💡This is community post by Shresth Shukla I'm sure you all have used LanceDB on Colab a lot, building complex RAG applications and
Designing a Table Format for ML Workloads In recent years the concept of a table format has really taken off, with explosive growth in technologies like Iceberg, Delta, and Hudi. With so
End to End Evaluation Template for RAG Apps 💡This is a community post by Mahesh Deshwal Comprehensive Evaluation Metrics for RAG Applications Using LanceDB In the world of Retrieval-Augmented Generation (RAG) applications, evaluating
⚔️ Lancelot Round Table, 100x Reduction in S3 Requests, VoyageAI❤️LanceDB To inaugurate the new year, we proudly unveiled the first ⚔️Lancelot Round Table in January, celebrating the founding knights who have been instrumental in shaping
Chunking Analysis: Which is the right chunking approach for your language? Before we even start discussing the right chunking approach for your language, the first question that should come to mind is whether the chunking approach
Agentic RAG using LangGraph: Build autonomous Customer support agent Agent? What's that? In the current world where everything is running with and for AI, retrieval-augmented generation (RAG) systems have become essential for
