---
title: "vectordb-recipes"
type: "tool"
slug: "lancedb-vectordb-recipes"
canonical_url: "https://www.graphcanon.com/tools/lancedb-vectordb-recipes"
github_url: "https://github.com/lancedb/vectordb-recipes"
homepage_url: null
stars: 966
forks: 172
primary_language: "Jupyter Notebook"
license: "Apache-2.0"
categories: ["llm-frameworks", "ai-agents", "evaluation-observability", "data-retrieval", "vector-databases"]
tags: ["embeddings", "deep-learning", "fine-tuning", "agents", "ai", "gpt", "langchain", "lancedb"]
updated_at: "2026-07-07T18:48:29.00668+00:00"
---

# vectordb-recipes

> Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs

A repository with Jupyter Notebook examples, applications, starter code, and tutorials for building GenAI projects.

## Facts

- Repository: https://github.com/lancedb/vectordb-recipes
- Stars: 966 · Forks: 172 · Open issues: 4 · Watchers: 10
- Primary language: Jupyter Notebook
- License: Apache-2.0
- Last pushed: 2026-04-24T11:29:16+00:00

## Categories

- [LLM Frameworks](/categories/llm-frameworks.md)
- [AI Agents](/categories/ai-agents.md)
- [Evaluation & Observability](/categories/evaluation-observability.md)
- [Data & Retrieval](/categories/data-retrieval.md)
- [Vector Databases](/categories/vector-databases.md)

## Tags

embeddings, deep-learning, fine-tuning, agents, ai, gpt, langchain, lancedb

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## README (excerpt)

```text
# VectorDB-recipes
<br />
Dive into building GenAI applications!
This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects.

- These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. 
- It **integrates into Python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc.
- LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions!

<img src="https://github.com/lancedb/vectordb-recipes/assets/5846846/d284accb-24b9-4404-8605-56483160e579" height="85%" width="85%" />

<br />
Join our community for support - <a href="https://discord.gg/zMM32dvNtd">Discord</a> •
<a href="https://twitter.com/lancedb">Twitter</a>

---

This repository is divided into 2 sections:
- [Examples](#examples) - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes!
- [Applications](#projects--applications) - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools


The following examples are organized into different tables to make similar types of examples easily accessible.

### Sections

- [Build from Scratch](#build-from-scratch) - Step-by-step guides to create AI applications from scratch.
- [Multimodal](#multimodal) - Build apps that process and search across both text and images.
- [RAG](#rag) - Combine document retrieval with LLM-powered responses.
- [Vector Search](#vector-search) - Learn to efficiently find relevant documents using vector-based search.
- [Chatbot](#chatbot) - Create AI chatbots that fetch information and generate intelligent replies.
- [Evaluation](#evaluation) - Measure the quality and accuracy of AI-generated answers.
- [AI Agents](#ai-agents) - Build LLM-driven applications where multiple agents collaborate and interact.
- [Recommender Systems](#recommender-systems) - Develop AI-powered recommendation systems for personalized suggestions.
- [Concepts](#concepts) - Tutorials and explanations of key techniques used in AI applications.


### 🌟 New 🌟 
Stay up to date with the latest projects, tools, and improvements added to the repository.
- **V-JEPA Video Search** - <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/v-jepa-video-search/intra-video.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>

### Build from Scratch

Start with the basics! These examples guide you through creating AI applications from the ground up using LanceDB for efficient document retrieval and search.

| Build from Scratch &nbsp; &nbsp;| Interactive Notebook & Scripts &nbsp; | 
|-------- | -------------: |
|||
| [Build RAG from Scratch](./tutorials/RAG-from-Scratch) |   |  |
| [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) |   |  |
| [Multi-Head RAG from Scratch](./tutorials/Multi-Head-RAG-from-Scratch/) |    |  |
| [Fintech AI Agent from Scratch](./examples/fintech-ai-agent) |<a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/fintech-ai-agent/fintech-ai-agent.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>       | |
||||

### MultiModal

Search across different types of data (text, images, and more). Build powerful search applications that work with diverse inputs.

| Multimodal &nbsp; &nbsp;| Interactive Notebook & Scripts &nbsp; | Blog |
| --------- | -------------------------- | ----------- |
||||
| [V-JEPA Video Search](./examples/v-jepa-video-search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/v-jepa-video-search/intra-video.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | |
| [Multimodal CLIP: DiffusionDB](./examples/multimodal_clip_diffusiondb/) | <a hre
```

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/tools/lancedb-vectordb-recipes`](/api/graphcanon/tools/lancedb-vectordb-recipes)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_