playbook/antigravity-awesome-skills/skills/weaviate-cookbooks/SKILL.md

name, description, category, risk, source, source_repo, source_type, date_added, author, tags, tools, license, license_source

name	description	category	risk	source	source_repo	source_type	date_added	author	tags	tools	license	license_source
weaviate-cookbooks	Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends.	ai	safe	community	weaviate/agent-skills	official	2026-06-29	Weaviate	weaviate rag agents vector-database ai-apps	python weaviate nextjs	BSD-3-Clause	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.
• Advanced RAG: Improve on basic RAG by adding extra features such as re-ranking, query decomposition, query re-writing, LLM filter selection.
• Basic Agent: Build a tool-calling AI agent with structured outputs using DSPy. Covers AgentResponse signatures, RouterAgent, tool design, and sequential multi-step loops.
• Agentic RAG: Build RAG-powered AI agents with Weaviate. Covers naive RAG tools, hierarchical RAG with LLM-created filters, vector DB memory, Weaviate Query Agent, and Elysia integration.

Interface (Optional)

Use this when the user explicitly asks for a frontend for their Weaviate backend.

• Frontend Interface: Build a Next.js frontend to interact with the Weaviate backend.

Client Usage

• Async Client: Guide for using the Weaviate Python async client in production applications (FastAPI, async frameworks). Covers connection patterns, lifecycle management, common pitfalls, and multi-cluster setups.

Limitations

• Cookbook blueprints still need adaptation to the user's data model, embedding provider, auth model, deployment platform, and latency/cost targets.
• This skill does not validate live Weaviate credentials, cloud quotas, or model availability unless the user provides and approves the relevant environment.
• Generated apps should be reviewed for security, data privacy, prompt injection exposure, and production observability before launch.