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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs
| Date | Stars |
|---|---|
| 2026-07-24 | 969 |
| 2026-07-25 | 969 |
| 2026-07-28 | 969 |
| 2026-07-30 | 969 |
| 2026-07-31 | 970 |
| 2026-08-06 | 970 |
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# 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 | Interactive Notebook & Scripts | |-------- | -------------: | ||| | [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [](#) [](#)| | | [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) | [](./tutorials/Local-RAG-from-Scratch/rag.py) [](#) [](#)| | | [Multi-Head RAG from Scratch](./tutorials/Multi-Head-RAG-from-Scratch/) | [](./tutorials/Multi-Head-RAG-from-Scratch/main.py) [](#) [](#) [](#)| | | [Fintech AI Agent from Scratch](
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:27e52e49991fba30, topic:vector-database, desc:vector search, readme:vector search
matched fp:27e52e49991fba30, topic:multimodal, desc:multimodal, readme:multimodal
matched fp:27e52e49991fba30, topic:agents, readme:ai agent, readme:ai agents
matched fp:27e52e49991fba30, topic:rag, readme:document retrieval