weaviate-cookbooks

name	weaviate-cookbooks
description	Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends.
category	ai
risk	safe
source	community
source_repo	weaviate/agent-skills
source_type	official
date_added	2026-06-29
author	Weaviate
tags	["weaviate","rag","agents","vector-database","ai-apps"]
tools	["python","weaviate","nextjs"]
license	BSD-3-Clause
license_source	https://github.com/weaviate/agent-skills/blob/main/LICENSE

Weaviate Cookbooks

Overview

This skill provides an index of implementation guides and foundational requirements for building Weaviate-powered AI applications. Use the references to quickly scaffold full-stack applications with best practices for connection management, environment setup, and application architecture.

When to Use This Skill

• Use when the user wants a Weaviate-backed RAG, agentic RAG, chatbot, data explorer, or multimodal document-search application.
• Use when selecting between cookbook patterns before writing a full-stack Weaviate app.
• Use when the project needs Weaviate environment, setup, async-client, or frontend guidance.
• Use when the user asks for an official Weaviate blueprint rather than a generic vector database recipe.

Weaviate Cloud Instance

If the user does not have an instance yet, direct them to the cloud console to register and create a free sandbox. Create a Weaviate instance via Weaviate Cloud.

Before Building Any Cookbook

Follow these shared guidelines before generating any cookbook app:

• Project Setup Contract
• Environment Requirements

Then proceed to the specific cookbook reference below.

Cookbook Index

• Query Agent Chatbot: Build a full-stack chatbot using Weaviate Query Agent with streaming and chat history support.
• Data Explorer: Build a full-stack data explorer app including sorting, keyword search and tabular view of weaviate data.
• Multimodal RAG: Building Document Search: Build a multimodal Retrieval-Augmented Generation (RAG) system using Weaviate Embeddings (ModernVBERT/colmodernvbert) and Ollama with Qwen3-VL for generation.
• Basic RAG: Implement basic retrieval and generation with Weaviate. Useful for most forms of data retrieval from a Weaviate collection.