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• These are built using LanceDB, a free, open-source, serverless vectorDB thatrequires no setup.
• Itintegrates into Python data ecosystem so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc.
• LanceDB hasnative Typescript SDK using which you canrun vector search in serverless functions!

This repository is divided into 2 sections:

• Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes!
• 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.

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

Stay up to date with the latest projects, tools, and improvements added to the repository.

• V-JEPA Video Search -

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
Local RAG from Scratch with Llama3
Multi-Head RAG from Scratch
Fintech AI Agent from Scratch

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

Multimodal	Interactive Notebook & Scripts	Blog
V-JEPA Video Search
Multimodal CLIP: DiffusionDB
Multimodal CLIP: Youtube videos
Cambrian-1: Vision centric exploration of images
Multimodal Jina CLIP-V2 : Food Search
Multimodal vector search: Voyage AI X LanceDB

Generated Responses by retrieving relevant documents before answering. This section covers different approaches to implementing RAG in your projects.

RAG	Interactive Notebook & Scripts	Blog
RAG using Deepseek R1 vs OpenAI o1
RAG On PDF
RAG with Contextual Retrieval and Hybrid search
RAG with Matryoshka Embeddings and LlamaIndex
RAG with IBM Watsonx
Cognee RAG
Improve RAG with Re-ranking
Improve RAG with HyDE
Improve RAG with LOTR
Advanced RAG: Context Enrichment Window
Advanced RAG: Late Chunking
Corrective RAG with Langgraph
Contextual-Compression-with-RAG
Improve RAG with FLARE
Agentic RAG
GraphRAG
GraphRAG with CSV File
GraphRAG with cognee - Multimedia

Find relevant documents quickly! These projects show how to use vector-based search techniques to make AI-powered searches faster and smarter.

Vector Search	Interactive Notebook & Scripts	Blog
Inbuilt Hybrid Search
Hybrid search BM25 & lancedb
NER powered Semantic Search
Vector Arithmetic with LanceDB
Summarize and Search Reddit Posts
Imagebind demo app
Search Within Images
Zero Shot Object Detection with CLIP
Vector Search with TransformersJS
Geospatial Recommendation System
Accelerate Vector Search Applications Using OpenVINO

Create chatbots that understand user queries and fetch relevant responses using LanceDB’s vector search capabilities.

Chatbot	Interactive Notebook & Scripts	Blog
Databricks DBRX Website Bot
CLI-based SDK Manual Chatbot with Phidata
Youtube transcript search bot
Langchain: Code Docs QA bot
Chatbot with any website using Crawl4AI
Context-Aware Chatbot using Llama 2 & LanceDB

These projects provide tools to compare AI-generated responses against reference data and fine-tune accuracy.

Evaluation	Interactive Notebook & Scripts	Blog
Monitoring and Tracing RAG using HoneyHive
Evaluating RAG with RAGAs

Build applications where multiple AI agents interact to complete tasks efficiently. These projects show how agents can collaborate, exchange data, and automate workflows.

AI Agents	Interactive Notebook & Scripts	Blog
Trip Planner Swarm style Agent
Multi Source Agent
AI email assistant with Composio
Assitant Bot with OpenAI Swarm
AI Trends Searcher with CrewAI
SuperAgent Autogen
Build autonomous Customer support agent using Langgraph
AI Agents: Reducing Hallucination
Multi Document Agentic RAG
RASA: Customer Support Bot

Personalized AI recommendations! These projects help you build recommendation engines that suggest content based on user preferences.

Recommender Systems	Interactive Notebook & Scripts	Blog
Movie Recommender
Product Recommender
Arxiv paper recommender
Music Recommender

Learn the core ideas behind AI applications—including text chunking, retrieval strategies, and optimization techniques—to improve your understanding of vector search and AI pipelines.

Concepts	Interactive Notebook	Blog
A Primer on Text Chunking and its Types
Langchain LlamaIndex Chunking
Create structured dataset using Instructor
Comparing Cohere Rerankers with LanceDB
Product Quantization: Compress High Dimensional Vectors
LLMs, RAG, & the missing storage layer for AI
Fine-Tuning LLM using PEFT & QLoRA
Extracting Complex tables-text from PDFs using LlamaParse
Convert any Image dataset to lance Format

Ready-to-use AI applications built with LanceDB! Use these projects as-is, customize them, or integrate them into your own applications.

Project Name	Description	Screenshot
Writing assistant	Writing assistant app using lanchain.js with LanceDB, allows you to get real time relevant suggestions and facts based on you written text to help you with your writing.
Sentence Auto-Complete	Sentance auto complete app using lanchain.js with LanceDB, allows you to get real time relevant auto complete suggestions and facts based on you written text to help you with your writing.You can also upload your data source in the form of a pdf file.You can switch between gpt models to get faster results.
Article Recommendation	Article Recommender: Explore vast data set of articles with Instant, Context-Aware Suggestions. Leveraging Advanced NLP, Vector Search, and Customizable Datasets, Our App Delivers Real-Time, Precise Article Recommendations. Perfect for Research, Content Curation, and Staying Informed. Unlock Smarter Insights with State-of-the-Art Technology in Content Retrieval and Discovery!".
AI Powered Job Search	Transform your job search experience with this AI-driven application. Powered by LangChain.js, LanceDB, and advanced semantic search, it provides real-time, highly accurate job listings tailored to your preferences. Featuring customizable datasets and advanced filtering options (e.g., skills, location, job type, and salary range), this app ensures you find the right opportunities quickly and effortlessly. Best suited for job seekers, recruiters, career platforms, custom job boards.
AI Powered Multimodal meme search	An advanced AI-powered meme search engine that allows users to find memes using both text and image queries. By leveraging LanceDB as a high-performance vector database and Roboflow's CLIP model for embedding generation, the platform delivers fast and accurate meme retrieval.
AI Powered Feedback search and analysis	An AI-powered employee feedback analysis platform designed to collect, store, analyze, and retrieve insightful employee feedback. This system leverages LanceDB for high-speed vector-based semantic search, React.js for an interactive UI, Node.js for backend processing, and LangChain.js with an Ambient Agent for intelligent analysis and actionable insights.
Hierarchical Multi Agent	The AI-Powered Law Assistant is aHierarchical Multi-Agent System leveraging LangGraph, LangChain, and LanceDB for efficient legal query processing. It features a Supervisor Agent that delegates tasks to specialized agents for IPC and NDPS laws, each with sub-agents for case retrieval and legal summarization. Using LanceDB, it stores and retrieves vectorized legal documents, enabling fast, structured, and context-aware responses for legal professionals, researchers, and law students.

Project Name	Description	Screenshot
YOLOExplorer	Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds
Website Chatbot (Deployable Vercel Template)	Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package.
Advanced Chatbot with Parler TTS	This Chatbot app uses Lancedb Hybrid search, FTS & reranker method with Parlers TTS library.
Multi-Modal Search Engine	Create a Multi-modal search engine app, to search images using both images or text
Evaluate RAG	A working Streamlit RAG App designed to demonstrate end to to end production grade evaluation using 50+ scores and metrics which include guards, software metrics, traditional metrics and LLM as judge metrics. It uses mixture of specialised deep learning models and LLM as Judge models to do the evaluations
Multi-Agent Collaboration Chatbot	Multi-Agent collabration chatbot using langgraph for share-market use case using Lancedb & tools such as Polygon ,Tavily
Multimodal Myntra Fashion Search Engine	This app uses OpenAI's CLIP to make a search engine that can understand and deal with both written words and pictures.
Multilingual-RAG	Multilingual RAG with cohere embedding & support 100+ languages
Music Recommender	Music Recommendation system using audio feature extraction and vector similarity search. By utilizingLanceDB,PANNs for audio tagging, andLibrosa for audio feature extraction, the system finds and recommends tracks with similar audio characteristics based on a query song.
NoOCR	End-to-end solution for complex PDFs, powered byColPali andLanceDB.

🌟 New! 🌟 Applied GenAI and VectorDB course on UdacityLearn about GenAI and vectorDBs using LanceDB in the recently launchedUdacity Course

If you're working on some cool applications that you'd like to add to this repo, please open a PR!