---

name: weaviate-cookbooks description: "Build complete AI applications with Weaviate — Query Agent chatbots, data explorers, multimodal PDF RAG, basic/advanced/agentic RAG pipelines, and DSPy tool-calling agents. Use when scaffolding a full-stack Weaviate application from scratch. Triggers: 'build a chatbot', 'RAG pipeline', 'Query Agent app', 'document search', 'data explorer', 'multimodal search', 'agentic RAG', 'Weaviate app'." allowed-tools: Read, Glob, Grep metadata: triggers: Query Agent, RAG pipeline, chatbot, Weaviate app, document search, data explorer, multimodal RAG, agentic RAG, DSPy agent related-skills: vector-database, weaviate, agentic-ai-dev, python-dev domain: backend role: specialist scope: implementation output-format: code last-reviewed: "2026-03-15"

Weaviate Cookbooks

Iron Law

READ references/project_setup.md AND references/environment_requirements.md BEFORE GENERATING ANY COOKBOOK CODE.

Every cookbook shares connection management, dependency setup, and env var patterns. Skipping setup causes runtime failures that look like Weaviate bugs but are actually missing config.

Quick Cookbook Selector

Use Case	Cookbook	Complexity	Best For
Ask questions, get answers	Query Agent Chatbot	Low	Q&A interfaces, conversational search
Browse and filter data	Data Explorer	Low	Exploratory analytics, dashboards
Search PDFs / documents	Multimodal RAG	Medium	Document retrieval, file search
Basic retrieval + generation	Basic RAG	Low	First RAG implementation
Production RAG + reranking	Advanced RAG	High	Complex retrieval, multi-stage ranking
Tool-calling agent	Basic Agent	Medium	Structured task automation with DSPy
RAG + agents combined	Agentic RAG	High	Multi-tool workflows, hierarchical retrieval

Process

1. Read references/project_setup.md — dependency management, venv, project layout
2. Read references/environment_requirements.md — all required env vars
3. Select cookbook from the table above based on use case
4. Read the target cookbook reference file fully before generating code
5. Scaffold — generate project structure, then fill in implementation
6. (Optional) Add frontend via references/frontend_interface.md
7. (Optional) Use async client for FastAPI via references/async_client.md

Reference Files

File	Content	When to Use
references/project_setup.md	venv, dependencies, project layout, connection management	Always — read first
references/environment_requirements.md	All env vars, provider keys, .env template	Always — read first
references/query_agent_chatbot.md	FastAPI backend + streaming chat + chat history	Building Q&A chatbot
references/data_explorer.md	Sorting, keyword search, tabular UI	Building data browser
references/pdf_multimodal_rag.md	ModernVBERT + Ollama Qwen3-VL, PDF ingestion	PDF/document search
references/basic_rag.md	Retrieval + generation, simple pipeline	First RAG implementation
references/advanced_rag.md	Reranking, query decomposition, LLM filter selection	Production RAG
references/basic_agent.md	DSPy AgentResponse, RouterAgent, tool design	Tool-calling agents
references/agentic_rag.md	RAG tools, LLM filters, vector DB memory, Query Agent	Agentic RAG systems
references/frontend_interface.md	Next.js frontend for Weaviate backend	Adding a UI layer
references/async_client.md	Async client, FastAPI lifecycle, multi-cluster	Production async apps

Documentation Sources

Before generating code, consult these sources:

Source	Tool	Purpose
Weaviate Python v4	weaviate-docs MCP	Collection APIs, query patterns, async client
Weaviate general docs	weaviate-docs MCP	Application architecture, best practices
FastAPI	Context7 MCP	Async routes, lifespan events, SSE streaming

If the user has no Weaviate instance, direct them to Weaviate Cloud for a free sandbox, then run /weaviate:quickstart first.

Post-Code Review

After scaffolding a cookbook app, dispatch:

• weaviate-schema-reviewer — collection schema, v4 API compliance, multi-tenancy
• rag-pipeline-reviewer — if the cookbook includes a RAG pipeline
• security-reviewer — if the app handles user input or external data