lancedb/vectordb-recipes: A Notebook Collection for LanceDB RAG and Multimodal Workflows
Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs
At a glance
- What is it?
- The repository is a set of Jupyter notebooks and Python scripts that demonstrate LanceDB across RAG, multimodal search, agents and evaluation. It is useful as a starting point for prototypes, but it is not a library and it is not a maintained application.
- Who is it for?
- Adopt vectordb-recipes if you need a runnable LanceDB example to copy into a prototype, particularly for multimodal search or a RAG tutorial. Do not treat it as a dependency or as production code: it is a collection of notebooks and scripts with no releases retrieved, so there is no version to pin.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 145 days ago.
- What is it written in?
- Mainly Jupyter Notebook, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
What vectordb-recipes is and who it is aimed at
The repository describes itself as containing "examples, applications, starter code, & tutorials to help you kickstart your GenAI projects." It is a collection of Jupyter notebooks and Python scripts, not a library. The default branch is main, the primary language is Jupyter Notebook, and the licence is Apache-2.0. There are no releases retrieved, which means there is no tagged version to depend on. You clone it or open a notebook in Colab.
The intended reader is someone who already knows they want to use LanceDB and needs a working example of how it fits into a RAG pipeline, a multimodal search app, or an agent loop. The README organises the material into sections such as Build from Scratch, Multimodal, RAG, Vector Search, Chatbot, Evaluation, AI Agents, Recommender Systems and Concepts. Each entry is labelled with badges for the runtime (Python or Colab), the model backend (openai-api or local-llm) and a difficulty level (beginner or advanced). That labelling is the most useful navigational feature in the repository, because it lets you filter out examples that require an OpenAI key or a local model before you open them.
If you are looking for a documented API surface, a changelog, or a support contract, this is the wrong repository. It is teaching material.
How the examples are structured and what runs where
The repository is split into two top-level sections according to the README: Examples, described as "get right into the code with minimal introduction", and Applications, described as "ready to use Python and web apps". Underneath that split, the examples are grouped by task rather than by technique. A multimodal entry such as Multimodal CLIP: DiffusionDB lives next to a video search example, and both are separate from the RAG and agent entries.
Each entry follows the same pattern: a directory under examples/ or tutorials/, a notebook, and often a matching .py script. The Build from Scratch table lists, for example, ./tutorials/RAG-from-Scratch with RAG_from_Scratch.ipynb, ./tutorials/Local-RAG-from-Scratch with rag.py, and ./tutorials/Multi-Head-RAG-from-Scratch/ with main.py. The Colab badges point at raw notebook paths on GitHub, so the notebooks are meant to be opened directly in a hosted runtime rather than installed as a package.
The data flow implied by the layout is consistent: load a dataset, embed it, write the vectors into a LanceDB table, query that table, and pass the retrieved rows to an LLM. Because the examples are independent directories, there is no shared utility module tying them together. That is a deliberate choice for teaching, and it means you cannot import a helper from one example into another without copying the file.
Running an example: Colab versus local
The README gives two routes. The Colab route is the one the repository promotes: click the badge next to an entry, which opens the notebook from the main branch on GitHub. No install step is documented for that path beyond what the notebook itself contains.
For the local route, the README points at the .py scripts, for example ./tutorials/Local-RAG-from-Scratch/rag.py and ./tutorials/Multi-Head-RAG-from-Scratch/main.py. The README does not list a requirements.txt, a pyproject.toml, or a setup command for the repository as a whole, and it does not state a minimum Python version. The only dependency statement in the supplied material is that LanceDB is "a free, open-source, serverless vectorDB that requires no setup" and that it "integrates into Python data ecosystem" so you can use it with pandas, arrow and pydantic. A native TypeScript SDK is also mentioned for running vector search in serverless functions.
That means the practical install step is per example, not per repository. If a notebook needs an OpenAI key, the badge openai-api marks it; if it runs a local model, the badge local-llm marks it. You should read the first cell of the notebook before assuming anything about its dependencies, because the repository README does not enumerate them.
The difficulty badges are the most honest part of the README
Most example collections label everything as beginner. This one does not. The Fintech AI Agent from Scratch example carries an advanced badge, while RAG from Scratch, Local RAG from Scratch with Llama3 and Multi-Head RAG from Scratch are marked beginner. The distinction appears to track how many moving parts the example has: a single retrieval loop is beginner, an agent with tool calls and state is advanced.
That labelling has a practical consequence. If you are evaluating LanceDB for the first time, the beginner entries are the ones that isolate the vector store from everything else. The advanced entries mix LanceDB with agent orchestration, which makes it harder to tell whether a failure comes from the retrieval layer or from the model loop. For a first read of the API, start with the entries that carry only a Python or Colab badge and a beginner label.
The badges also indicate backend, and that matters for cost. An example marked openai-api will make network calls that you pay for. An example marked local-llm will not, but it will need the model weights available locally. The README does not state model sizes or hardware requirements for the local variants, so that is something you have to check inside each notebook.
Where the repository stops being useful
The first limitation is that nothing here is versioned. No releases were retrieved, so there is no tag to pin and no changelog to read. The last push recorded is 2026-04-24, which tells you the repository is active but not which LanceDB version any given notebook targets. LanceDB's Python API has changed over time, and a notebook written against an older client may fail on import or on a table method. That is the most likely failure mode when you run an older example: an API mismatch, not a logic error.
The second limitation is that the examples are not tested as a suite in any way the material describes. There is no CI configuration mentioned, no test directory referenced, and no statement that the notebooks are executed on a schedule. A notebook can rot silently. If a Colab badge opens a notebook that no longer runs end to end, the repository will not tell you.
The third limitation is scope. This is a LanceDB showcase, so every example uses LanceDB as the vector store. If you want to compare retrieval backends, or if you have already standardised on a different store, the repository offers you nothing except the pipeline shape. The embedding and LLM portions of the examples are transferable; the storage portion is not.
How it differs from LangChain templates and LlamaIndex examples
The topics list includes langchain and llama-index, and the natural comparison is with the example galleries those projects publish. The difference is one of framing. LangChain templates and LlamaIndex example directories are organised around their own abstractions: you get a chain or an index class, and the vector store is a pluggable component behind that abstraction. Here the vector store is the subject. The README's framing is that these examples are "built using LanceDB", and the retrieval code is written against LanceDB directly rather than through an orchestration layer.
That has two consequences. On the plus side, you see the actual table creation, the actual query call, and the actual schema, which is what you need if you are deciding whether LanceDB fits your data. On the minus side, if your application already uses LangChain or LlamaIndex, you will be reading code you have to translate into your own abstractions rather than code you can drop in. The examples that do use those frameworks are the ones where the framework is the point of the example, not the LanceDB usage.
A second difference is the multimodal coverage. The repository has dedicated entries for CLIP-based image search over DiffusionDB, CLIP-based YouTube video search, and V-JEPA video search, plus a Kaggle notebook for Cambrian-1 vision-centric image exploration. That is broader multimodal coverage than a typical RAG-focused example gallery, and it is the strongest reason to look at this repository specifically rather than a general one.
Maintenance, licensing and what to verify before copying code
The repository is licensed Apache-2.0, which permits commercial use and modification provided the licence and notices are preserved. That covers the code in the repository. It does not cover the datasets, model weights or third-party services the examples pull in: DiffusionDB, YouTube content, CLIP weights, Cambrian-1 and V-JEPA each carry their own terms, and the supplied material says nothing about how those are licensed for reuse. If you plan to ship something derived from an example, check the upstream asset licences separately. This is not legal advice.
On maintenance: the repository is a documentation surface, not a dependency, so there is no upgrade cost in the usual sense. You do not bump a version of vectordb-recipes. The cost is the reverse: when LanceDB changes, the notebooks may drift, and you will be the one reconciling the difference. Because there are no releases retrieved, you also cannot tell from the repository metadata which LanceDB version was current when a given notebook was last touched.
The concrete verification step is to open the example you care about, read its first cell to see what it imports and what keys it expects, and run that single notebook before reading the rest. If the LanceDB import or the table creation call fails, the example predates your installed client and you are reading history rather than a working reference.
Editorial conclusion
Adopt vectordb-recipes if you need a runnable LanceDB example to copy into a prototype, particularly for multimodal search or a RAG tutorial. Do not treat it as a dependency or as production code: it is a collection of notebooks and scripts with no releases retrieved, so there is no version to pin. Before copying anything, check the individual example directory for its own requirements and verify which LanceDB API version the notebook was written against.
Community notes