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 various solutions to this problem are largely classified as backpressure.

Explore a practical guide to fine-tuning embedding models with practical insights and expert guidance from the LanceDB team.

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.

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

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.

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.

Get about hybrid search: rag for real-life production-grade applications. Get practical steps, examples, and best practices you can use now.

Discover about efficient rag with compression and filtering. Get practical steps, examples, and best practices you can use now.

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.