vectordb-recipes
Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs
GraphCanon updated today · GitHub synced today · 38 views this month
Decision brief
Vectordb-recipes offers resources and tutorials for building GenAI applications using LanceDB. It is particularly designed to help users get started quickly with minimal setup required.
Good fit when
- - When you need a comprehensive set of examples, starter code and tutorials specifically optimized for LanceDB, an open-source vector database that integrates seamlessly into the Python data ecosystem
- - For projects requiring no infrastructure setup, as it leverages serverless capabilities of LanceDB which eliminates the overhead of maintaining databases
Avoid when
- - When seeking support for a specific competitor's vector database (like Pinecone or Weaviate), as Vectordb-recipes focuses solely on LanceDB’s ecosystem
- - If you have strict requirements for custom database tuning that only vendor-specific proprietary databases can offer, as Vectordb-recipes’ focus is on leveraging the out-of-the-box advantages of an
Observed Jul 9, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Slowing (119d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/lancedb/vectordb-recipesHow it fits your stack(11)
Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.
Integrates
Depends on
Related
Relationship graph
Optional deeper exploration of typed edges and category neighbours.
examples3.0k★
langflow153k★
langchain144k★
ragflow87k★
quivr39k★
agents-towards-production21k★
transformers164k★lancedb11k★
awesome-llm-apps131k★examples3.0k★langflow153k★langchain144k★ragflow87k★quivr39k★agents-towards-production21k★transformers164k★lancedb11k★awesome-llm-apps131k★Similar tools
Same-category neighbours not already linked as typed edges.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository providing resources including recipes, examples, and tutorials focused on utilizing vector search and large language models (LLMs) in various applications such as AI agents, RAG systems, and multimodal AI.
Capability facts
- MCP server
- No MCP server detected
Source: repo_scan · Aug 21, 2026
- Languages
- jupyter notebook, javascript
Source: github.language+package.json · Aug 21, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 21, 2026)
- It **integrates into Python data ecosystem** so you can simply start using these in your existing data pipelSource link
Tags
README
VectorDB-recipes
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!
Join our community for support - Discord • Twitter
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.
Sections
- 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.
- Evaluation - 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.
🌟 New 🌟
Stay up to date with the latest projects, tools, and improvements added to the repository.
- V-JEPA Video Search -
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 | |
| Local RAG from Scratch with Llama3 | |
| Multi-Head RAG from Scratch | |
| Fintech AI Agent from Scratch | |
MultiModal
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 | <a hre |
For agents
This page has a .md twin and JSON over the API.