Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends.
SkillScore86Maintained
Automatic
◆AI Engineer pack
Last verified 8/12/2026
What it changes
Passed 19 of 22 test cases with the skill, and 12 of 22 without it.
Without skill55%
With skill86%
Checks it passed that the model alone missed
Recommends using Weaviate Query Agent
Requires checking the Project Setup Contract
Mentions reviewing prompt injection exposure and security
and 6 more in the full report
7 fixed12 passed either way3 still failing0 broke
Pass-rate lift
+32 pp
Tokens
+7%
Agent turns
0%
Response time
-28%
Model gemini-3.6-flashMeasured 8/12/2026v119 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 7
Prompt
Before generating code for any application pattern, what shared project verification step must be established first?
What was checked, without and with the skill
Checked
Without skill
With skill
Requires checking the Project Setup Contract
Missed
Passed
Requires checking Environment Requirements
Passed
Passed
case-12 · gemini-3.6-flash · 8/12/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md51 lines12 files
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.