Vector ML systems for AI search with LanceDB

LanceDB wanted to show what a vector database can do once it’s wired into real ML work: multimodal data handling, scalable vector search, and efficient production workflows across RAG systems, image processing, recommendations, and LLM applications. The question was how to show that in a way builders would actually use.

A single demo proves that LanceDB can store and query vectors. It doesn’t answer what practitioners arrive asking. How does my dataset get in? What does retrieval look like when the answer spans several documents? Each of those pulls in a different part of the ML stack, from PyTorch and CLIP to LlamaIndex and LangChain, and that handoff between tools is where integration projects usually stall.

One Pattern per Project

So instead of one showcase, we built a set of self-contained, open-source applications. Each takes a single pattern end to end, from raw data to a working query, and makes it clear where the vector store carries the weight and where it hands off to the rest of the pipeline.

In total we shipped 8+ open-source ML and RAG applications and technical examples with LanceDB as the core vector store, covering conversion, retrieval, classification, training, and recommendation.

Getting Data In Without the Boilerplate

Most image pipelines gloss over the first step: turning a dataset on disk into something a database can use. Lancify collapses encoding, chunking, and schema handling into one conversion into a Lance dataset. That cut the boilerplate around dataset preparation by roughly 90%, which matters because it’s the step where most people give up before they reach the interesting part.

Retrieval That Reasons Across Sources

The agentic multi-document RAG system answers questions for an automotive assistant across six JSON sources. Rather than searching everything at once, retrieval first selects which source to query, then chains tool responses across documents as the question requires. Answers come back with relevance scores of 0.58 or higher.

Images, Recommendations, and Scenes

For classification, we paired CLIP embeddings with LanceDB search to label CIFAR-100 images zero-shot. CLIP already places images and text in the same space, so a nearest-neighbor lookup does the job of a classifier, with no training run and no labelled set. The CNN training pipeline carries the same idea to the training side, with custom PyTorch dataset loaders that read images directly from Lance datasets.

The movie recommender combines genre classification with vector similarity across 45,000 films, showing where structured filters and similarity search work together. The GTA-V example retrieves across screenshots with CLIP embeddings, the multimodal case where the query is a scene rather than a sentence.

Each project has its own write-up:

• Lancify - image dataset converter
• Multi-document agentic RAG
• Zero-shot image classification
• CNN training with Lance Dataset
• Movie recommendation with RAG and genre classification
• Multimodal GTA-V RAG

Across the set we worked with LanceDB, PyTorch, Hugging Face, CLIP, LlamaIndex, LangChain, PyArrow, Pandas, GPT-4, ResNet, and Python.