Weaviate Cookbooks

From the author

Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.

— the description by Weaviate

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Weaviate Cookbooks skill serves as a central repository for developers building AI-powered applications using the Weaviate vector database. It provides structured guidance on architectural patterns, ranging from basic RAG implementations to complex agentic workflows. The skill emphasizes best practices for project setup, environment management, and integration, offering specific blueprints for chatbots, data explorers, and multimodal document search systems. It is designed to help developers scaffold full-stack applications efficiently by providing modular references and standardized implementation strategies.

What it does

Provides architectural blueprints for building RAG and agentic systems.
Offers standardized project setup and environment configuration guidelines.
Includes specific implementation guides for chatbots, data explorers, and multimodal search.
Provides best practices for using the Weaviate Python async client in production.
Offers guidance on integrating frontend interfaces with Weaviate backends.
Directs users to official resources for Weaviate Cloud instance management.

When to use it

When you need to build an AI application powered by Weaviate.
When you are looking for architectural patterns for RAG or agentic workflows.
When you need to set up a new Weaviate-based project with industry best practices.
When you need guidance on implementing advanced features like re-ranking or query decomposition.
When you are building a frontend interface to interact with a Weaviate backend.

Advantages

Reduces development time with ready-to-use architectural blueprints.
Ensures consistent project structure and dependency management.
Provides clear paths for scaling from basic RAG to advanced agentic systems.
Offers modular, reference-based learning for specific Weaviate features.
Promotes the use of modern development tools like uv for dependency management.

Usage examples

“How do I build a basic RAG system with Weaviate?”: The agent will provide the steps from the Basic RAG cookbook, including project setup, environment requirements, and the core retrieval logic.
“I need to build an agent that uses RAG to answer questions. Where should I start?”: The agent will guide you through the Agentic RAG cookbook, explaining how to implement naive RAG tools or hierarchical RAG with LLM-created filters.
“How can I improve my RAG system's performance?”: The agent will reference the Advanced RAG cookbook, suggesting techniques like query re-writing, decomposition, and re-ranking.
“I want to build a frontend for my Weaviate data explorer.”: The agent will provide the Frontend Interface reference, detailing how to connect a Next.js frontend to your Weaviate backend.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/weaviate/weaviate-cookbooks/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip weaviate-cookbooks.zip -d .agents/skills

Documentation by Weaviate

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.

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:

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.

Files

ATTRIBUTION.mdAdded by AutoGineer874 B
LICENSELicence file added by AutoGineer1.5 KiB
SKILL.md3.1 KiB
references/advanced_rag.md10.4 KiB
references/agentic_rag.md13.3 KiB
references/async_client.md14.6 KiB
references/basic_agent.md10.3 KiB
references/basic_rag.md6.3 KiB
references/data_explorer.md11.3 KiB
references/environment_requirements.md2.6 KiB
references/frontend_interface.md4.7 KiB
references/pdf_multimodal_rag.md19.7 KiB
references/project_setup.md1.4 KiB
references/query_agent_chatbot.md5.8 KiB

references/frontend_interface.md

Frontend Interface (Next.js + Weaviate Backend)

Quick reference

Item	Value
Stack	Next.js (App Router), Tailwind v4, shadcn/ui, Framer Motion, react-icons, ai-sdk
Node	v25.3.0+
Backend	`NEXT_PUBLIC_BACKEND_HOST` (default: `localhost:8000`)
App type	Single-page app; main view updates in place, no full-page navigations
Layout	shadcn Sidebar (left) + main content area; sidebar buttons switch the main view per feature

Setup (run in order)

1. Next.js

Command: npx create-next-app@latest . --yes (run from repo root; may need required_permissions: ["all"] in sandbox)
Result: TypeScript, ESLint, Tailwind v4, App Router, Turbopack, @/* → ./*, no src/. App in app/, static in public/.
Scripts: dev | build | start | lint. Dev server: http://localhost:3000.
Routes: app/layout.tsx, app/page.tsx. Imports: @/ = project root.
Ref: Next.js App Router Installation — verify against current docs.

2. shadcn/ui

Requires: Next.js + Tailwind v4 + App Router + @/*, no src/.
Init: npx shadcn@latest init -t next -y -b zinc --no-src-dir
Add components: npx shadcn@latest add button -y (e.g. card, dialog, input; -o overwrites).
Output: components.json, lib/utils.ts (cn), app/globals.css (tw-animate, shadcn/tailwind.css, CSS vars). UI in components/ui/<name>.tsx. Import: import { Button } from "@/components/ui/button".
Ref: shadcn Next.js | CLI.

3. Framer Motion

npm i framer-motion

Ref: Framer Motion

4. AI SDK (optional)

Note: Install only when create a conversational user interface for your chatbot application. It enables the streaming of chat messagesyou need to stream responses from the backend using useChat().

When: Add this step only if the app needs a chat UI (e.g. query-agent or chatbot flows).
Stack: Use the Vercel AI SDK (ai + @ai-sdk/*). Use useChat and SDK UI primitives for the chat view.
Ref: AI SDK – useChat | Next.js App Router setup — follow current docs for install and wiring.

npm i ai @ai-sdk/react zod

5. Environment

Required:

NEXT_PUBLIC_BACKEND_HOST="localhost:8000"

Use the actual backend host when not local.

Rules (must follow)

Stack and structure

UI: Use shadcn components only for layout and interactive elements (buttons, cards, inputs, dialogs, etc.). Do not add another UI library.
Architecture: SPA — one main page, update main view in place. Avoid full-page navigations unless necessary.
Icons: Use react-icons only; prefer one set (e.g. react-icons/fa or react-icons/hi) for consistency.
Animation: Use Framer Motion only. Do not add another animation library.

Visual style

Goal: Minimal, sleek, clean. No clutter, heavy borders, or noisy backgrounds.
Aesthetic: “Liquid glass” — frosted, translucent; soft blur; light borders and shadows; depth without heaviness. Use backdrop-blur, semi-transparent fills, subtle gradients where they support this.

Motion

Style: Subtle, springy, purposeful (fade in, hover, enter/exit). Prefer spring physics over linear/ease-out.

Layout

Left: shadcn Sidebar component.
Right: Main content area.
Navigation: One sidebar button per backend feature (e.g. data explorer, chat). Click switches the main view only.

Responsiveness

Layout and components must work on small and large screens.

Docs (verify against current versions)

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

What our scanner noticed· Medium risk

Instructions that use the network
Downloads piped into a shell

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download weaviate-cookbooks.zip