OpenClaw and similar personal autonomous agents need a local-first long-term memory layer. LanceDB fits that role with embedded deployment, filesystem-native storage, and multimodal retrieval.

Train a Variational Autoencoder end‑to‑end using Lance for fast, scalable data handling. You’ll set up the dataset, build the VAE in PyTorch, and run training, sampling, and reconstructions.

This article will teach us how to make an AI Trends Searcher using CrewAI Agents and their Tasks. But before diving into that, let's first understand what CrewAI is and how we can use it for these applications.

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.

Explore about scalable computer vision with lancedb & voxel51. Get practical steps, examples, and best practices you can use now.

An untold story about how we make LanceDB vector search fast. Get practical steps and examples from 'My SIMD is faster than Yours'.

Build a multimodal fashion search engine with LanceDB and CLIP embeddings. Follow a step‑by‑step workflow to register embeddings, create the table, query by text or image, and ship a Streamlit UI.

Remember flipping through coding manuals? Those quickly became relics with the rise of Google and Stack Overflow, a one-stop shop for developer queries.

Even though text-generation models are good at generating content, they sometimes need to improve in returning facts. This happens because of the way they are trained.

Have you ever thought about how search engines find exactly what you're looking for? They usually use a mix of matching specific words and understanding the meaning behind them.

Combine keyword and vector search for higher‑quality results with LanceDB. This post shows how to run hybrid search and compare rerankers (linear combination, Cohere, ColBERT) with code and benchmarks.

Unlock about implement contextual retrieval and prompt caching with lancedb. Get practical steps, examples, and best practices you can use now.

Speed up vector index training in LanceDB with CUDA or Apple Silicon (MPS). See how GPU‑accelerated IVF/PQ training compares to CPU and how to enable it in code.

Working with large image datasets in machine learning can be challenging, often requiring significant computational resources and efficient data-handling techniques.

See about custom datasets for efficient llm training using lance. Get practical steps, examples, and best practices you can use now.

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.

In this blog, we’ll explore how to build a chat application that interacts with CSV and Excel files using LanceDB’s hybrid search capabilities.

by Akash A. Get practical steps and examples from 'Better RAG with Active Retrieval Augmented Generation FLARE'.

Compress vectors with PQ and accelerate retrieval with IVF_PQ in LanceDB. The tutorial explains the concepts, memory savings, and a minimal implementation with search tuning knobs.

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.

Build an autonomous customer support agent using LangGraph and LanceDB that automatically fetches, classifies, drafts, and responds to emails with RAG-powered policy retrieval.

In the world of search engines, the quest to find the most relevant information is a constant challenge. Researchers are always on the lookout for innovative ways to improve the effectiveness of search results.

We show how to use the CLIP from OpenAI for Text-to-Image and Image-to-Image searching. We’ll also do a comparative analysis of the PyTorch model, FP16 OpenVINO format, and INT8 OpenVINO format in terms of speedup.