lancedb/vectordb-recipes: a notebook collection for LanceDB, RAG and multimodal search

What vectordb-recipes is, and the problem it removes

Building a retrieval or multimodal search demo usually starts with the same chores: pick a vector store, wire an embedding model, chunk documents, store vectors, query them, then hand the results to an LLM. vectordb-recipes is a catalogue of finished versions of that loop. The README describes it as "examples, applications, starter code, & tutorials to help you kickstart your GenAI projects," all built on LanceDB, which the README calls a free, open-source, serverless vectorDB that requires no setup.

The audience is narrow and clear. You are a Python or TypeScript developer who already knows what an embedding is and wants a reference implementation rather than a specification. The repository splits into Examples, for getting "from an idea to PoC within minutes," and Applications, described as ready-to-use Python and web apps. If you are evaluating vector databases at an architectural level, this repository will not help you compare them; it assumes LanceDB and shows what you can build on it.

How the examples are organised, and what that says about the data flow

The README groups everything into tables: Build from Scratch, Multimodal, RAG, Vector Search, Chatbot, Evaluation, AI Agents, Recommender Systems and Concepts. Each row points at a folder under tutorials/ or examples/ and, where one exists, a Colab badge. The top level of the repository contains applications/, assets/, examples/, tutorials/, .github/, .vscode/, package.json, requirements.txt, LICENSE and compile_testing.js.

The recurring mechanism across the catalogue is the same. A notebook or script loads a dataset, produces embeddings, writes them into a LanceDB table, runs a vector or hybrid search, and passes the retrieved rows to a model. The folder names tell you which part is being varied: examples/Chunking_Analysis/ and examples/Advanced_RAG_Late_Chunking/ vary the splitting stage; examples/Hybrid_search_bm25_lancedb/ and examples/Inbuilt-Hybrid-Search/ vary the retrieval stage; examples/Evaluating_RAG_with_RAGAs/ adds a scoring stage. Multimodal entries such as examples/ColPali-vision-retriever/ and examples/multimodal_clip_diffusiondb/ swap the text encoder for an image one. That structure is the real value: it isolates one variable per folder, so you can copy the pattern rather than the whole pipeline.

Installing and running a first example

The repository does not give a single install command. The README points at Colab badges for the notebook examples, and the root requirements.txt lists the shared Python dependencies. The safest first step is to install those dependencies, then open one notebook from its own folder, because imports and data paths are relative to that folder.

The dependency set is short. requirements.txt lists lancedb, pylance, openai==0.28, datasets, nbconvert and pytest, and only openai carries a pinned version. That mix matters: the OpenAI client changed its interface after 0.28, so examples written against that version may not run unchanged against a current client.

The README's Build from Scratch table lists tutorials/RAG-from-Scratch as the beginner entry, with a Colab badge and an openai-api tag. Open the notebook from the repository root path rather than from a copied file, so the relative data paths resolve. You should see the notebook open with its cells in order. The README tags this example as beginner and as using the OpenAI API, so expect to supply an API key in the environment the notebook reads. The README does not document which environment variable name that example uses; check the notebook's first cells before running them.

For a local model instead of an API, the same table lists tutorials/Local-RAG-from-Scratch, which the README tags local-llm and points at rag.py rather than a notebook. The README gives no run command for it, so check the script's own header before executing it.

The JavaScript side is separate. The root package.json is named vectordb-example-js-openai and depends on openai ^3.3.0 and vectordb ^0.1.19, which is a different package name and version line from the Python lancedb package in requirements.txt.

Where the repository stops being enough

The repository has no releases, so there is nothing to pin to and no changelog to read when an upstream API moves. requirements.txt pins only openai, and the JavaScript manifest pins vectordb at ^0.1.19 while the README talks about LanceDB's native TypeScript SDK without naming a version. If you build on a copied example, you inherit whatever API surface it was written against with no record of when that was.

The README also does not document rollback, a support policy, or a contribution process beyond the Discord and Twitter links. There is no statement about how examples are retired when a dependency breaks. A catalogue of this shape ages unevenly: the RAG-from-scratch notebook and a ColPali vision retriever have very different dependency surfaces, and the repository offers no signal about which ones are still exercised. compile_testing.js at the root suggests some automated checking exists, but the README does not describe what it covers.

It is also the wrong tool if you need a maintained library rather than worked examples. Nothing here is published as a package you can depend on. If your requirement is a vector store with a stability guarantee, this repository is documentation for one, not the thing itself.

How this differs from a framework tutorial set like LlamaIndex or LangChain

LlamaIndex and LangChain both ship their own example collections, and the difference is where the abstraction sits. Those projects put an interface in front of the vector store: you construct an index object, and the store is a configuration choice underneath. This repository does the opposite. LanceDB is the fixed point, and the examples show the store directly, which is why the folder names read like retrieval techniques rather than framework features.

That has a practical consequence. A LlamaIndex example teaches you the framework's index and query classes, and swapping the backing store is a config change. An example here teaches you the LanceDB table and search call, and swapping the store means rewriting the retrieval code. If you have already committed to a framework, these examples are still useful as a reference for chunking and evaluation patterns, but you will be translating rather than copying. If you have not committed to one, this repository shows the lower-level mechanics first, which is harder to unlearn incorrectly later.

Maintenance status and what the licence allows

The repository is not archived. Its last push was on 2026-04-24, which is roughly five months before the date of this article, so it falls inside the six-month window and the recent-push fact alone does not settle whether it is actively developed. There are no retrieved releases, so there is no version history to read. Treat the catalogue as a snapshot: the examples that matter to you should be checked against the current LanceDB API before you build on them.

The licence is Apache-2.0, stated in the README's repository metadata and in the root LICENSE file, and the JavaScript package.json also declares "license": "Apache-2.0". Apache-2.0 permits commercial use and modification and includes a patent grant, but it also requires that you preserve notices and state significant changes. Several examples in the catalogue are adaptations of published techniques, so if you lift code into a product, check the individual example folder for attribution rather than assuming the root licence covers every file's provenance. This is a description of the licence text, not legal advice.

Editorial conclusion

Adopt this repository if you want a working starting point for LanceDB plus a named LLM or embedding stack, and you are willing to read each example's own files because there is no top-level install path. Skip it if you need a supported library with a changelog, a versioning policy or a documented upgrade path; the repository has no releases and the README states no support terms. Before relying on any example, open its folder, check its imports against requirements.txt (lancedb, pylance, openai==0.28, datasets, nbconvert, pytest) and confirm the API calls still match the LanceDB version you install.

Frequently asked questions

Official sources

1. Issues
2. lancedb/vectordb-recipes on GitHub
3. License: Apache-2.0
4. README