lancedb/vectordb-recipes
Resource, examples & tutorials for multimodal AI, RAG and agents using vector search and LLMs
Built on LanceDB—a serverless, setup-free vector database with native Python/Pandas integration and TypeScript support for edge deployment—this collection provides production-ready code patterns for RAG, multimodal search (CLIP, V-JEPA video embeddings), agentic workflows, and recommender systems. Examples span from foundational tutorials (local Llama3 RAG, multi-head retrieval) to advanced applications like fintech agents, enabling developers to prototype GenAI systems within minutes while leveraging existing data pipelines.
929 stars. Actively maintained with 2 commits in the last 30 days.
Stars
929
Forks
166
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 05, 2026
Commits (30d)
2
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/lancedb/vectordb-recipes"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
ErickWendel/neo4j-ai-experiments
Examples of my tutorial on how to use Neo4j for empowering AI RAG systems
nath1295/LLMFlex
A python package for developing AI applications with local LLMs.
Emirateyang/llm-agent
Java library to simplify the integration of LLMs
ISNE11/CheatSheet-LLM
Run local Large Language Models (LLMs) offline using Ollama – interact with textbooks and custom...
lisekarimi/lexo
🗯️LLM toolkit for RAG, tuning, agents, and more