Vector Databases · lancedb

vectordb-recipes

VectorDB-recipes is a collection of tutorials, examples, and starter applications for building AI applications using LanceDB (a serverless vector database), RAG, multimodal AI, and LLMs. It provides Jupyter notebooks and Python scripts covering beginner to advanced use cases, designed to help developers move quickly from concept to working prototype.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/lancedb/vectordb-recipes

966

GitHub stars

172

Forks

Jupyter Notebook

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	lancedb/vectordb-recipes
Owner	lancedb
Primary language	Jupyter Notebook
License	Apache-2.0 — OSI-approved
Stars	966
Forks	172
Open issues	4
Latest release	Unknown
Last updated	2026-04-24
Source	https://github.com/lancedb/vectordb-recipes

What vectordb-recipes is

A curated repository of GenAI examples using LanceDB for vector search, integrated with LangChain, LLaMA-Index, OpenAI, and local LLMs. Covers RAG pipelines, multimodal embeddings (CLIP, V-JEPA), AI agents, and chatbots with examples in Jupyter Notebook and Python, emphasizing serverless vector search in Python and TypeScript environments.

Quickstart

Get the vectordb-recipes source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/lancedb/vectordb-recipes.git2cd vectordb-recipes3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Rapid RAG Prototyping

Use the 'Build from Scratch' and 'RAG' sections to quickly prototype Retrieval-Augmented Generation pipelines—both cloud-based (OpenAI) and local (Llama3)—with minimal setup overhead.

Multimodal Search Applications

Leverage CLIP and V-JEPA examples to build text+image or video search applications without engineering heavy lifting, ideal for content discovery and visual exploration tools.

Learning and Team Onboarding

Use tutorials to teach engineers how vector search, embeddings, and LLM integration work in practice, with executable Colab notebooks and step-by-step code guides.

Implementation considerations

Examples assume Python 3.x and Jupyter Notebook/Colab environments; integration into production systems requires containerization and API exposure.
Many examples depend on external APIs (OpenAI GPT-4, Voyage AI) or large model downloads (Llama3); plan for API costs, rate limits, and offline model caching.
Multimodal examples (CLIP, V-JEPA) involve substantial tensor operations; GPU availability and inference latency impact real-time feasibility.
Recipes do not address data privacy, PII redaction, or compliance; you must add security and governance layers for regulated or sensitive data.
LanceDB integration examples assume local or serverless setup; adapt for your data warehouse or cloud object storage backend.

When to avoid it — and what to weigh

Production-grade Vector DB Selection — This is a recipes/examples repo, not a production database hardening or deployment guide. Do not use it as primary documentation for choosing or operating LanceDB in high-scale environments.
Proprietary or Regulated Data Handling — Examples rely on public APIs (OpenAI, Voyage AI) and open-source models. If your use case requires on-premises, air-gapped, or HIPAA/PCI-compliant infrastructure, custom adaptation is essential.
Real-Time or Mission-Critical Systems — Recipes are educational/PoC-focused. Production SLAs, error handling, monitoring, and failover strategies are not documented or guaranteed.
Dependency on Specific LLM Vendors — Examples hardcode OpenAI, local Llama, and other specific models; you will need to refactor for different vendor lock-in or multi-model strategies.

License & commercial use

Licensed under Apache License 2.0 (Apache-2.0), a permissive OSI-approved license allowing commercial use, modification, and distribution with proper attribution and liability disclaimer.

Apache-2.0 is permissive and allows commercial use of code in this repository. However, confirm licensing of dependencies (LanceDB, LangChain, embedding models, LLMs) you integrate into commercial products, as some may have additional or non-commercial restrictions.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Examples use public APIs and open-source models; no formal security audit or threat model documented. Developers must implement input validation, API key rotation, rate limiting, and data encryption. Serverless setup reduces infrastructure attack surface but shifts responsibility to code and dependency security.

Alternatives to consider

LangChain Docs & Examples

Official LangChain documentation also covers RAG, agents, and integrations; less multimodal focus but more actively maintained as part of core library.

RAG from Scratch (Andrew Ng / DeepLearning.AI)

Focused educational content on RAG fundamentals and evaluation; more curated but less breadth of application examples.

Pinecone Examples / Weaviate Recipes

Competing vector database platforms with their own example repositories; similar structure but tied to different infrastructure and pricing models.

Software development agency

Build on vectordb-recipes with DEV.co software developers

Explore VectorDB-recipes to prototype RAG pipelines, multimodal search, and LLM agents. Fork the repo, run Colab notebooks, and adapt examples to your use case.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

lancedb vectordb rag-cookbooks

Related on DEV.co

Explore the category and the services that help you build with it.

Vector Databases AI Application Development Web App Development API Development Talk to DEV.co

vectordb-recipes FAQ

Can I use these recipes in production?

Recipes are designed for learning and prototyping. Production use requires significant hardening: error handling, monitoring, security (API keys, PII), cost optimization, and fallback strategies not included in examples.

Do I need LanceDB specifically, or can I swap in another vector DB?

Examples are LanceDB-focused but the patterns (embedding, retrieval, LLM prompt) are portable. You will need to rewrite vector store integration code for Pinecone, Weaviate, or Milvus.

What are the costs of running these examples?

Depends on external APIs (OpenAI: variable per token), GPU compute (if local inference), and data storage. Colab is free but rate-limited; production scales will incur cloud costs.

Is offline/local execution possible?

Yes, for local LLM examples (Llama3, CLIP). However, some recipes default to OpenAI; you will need to refactor for fully offline operation.

Software developers & web developers for hire

Need help beyond evaluating vectordb-recipes? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and vector databases integrations — and maintain them long-term.

Ready to Build AI Applications?

Explore VectorDB-recipes to prototype RAG pipelines, multimodal search, and LLM agents. Fork the repo, run Colab notebooks, and adapt examples to your use case.

Talk to DEV.co Schedule a Consultation