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.
1---2name: weaviate-cookbooks3description: Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends.4license: BSD-3-Clause5---67# Weaviate Cookbooks89## Overview1011This 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.1213## When to Use This Skill1415- Use when the user wants a Weaviate-backed RAG, agentic RAG, chatbot, data explorer, or multimodal document-search application.16- Use when selecting between cookbook patterns before writing a full-stack Weaviate app.17- Use when the project needs Weaviate environment, setup, async-client, or frontend guidance.18- Use when the user asks for an official Weaviate blueprint rather than a generic vector database recipe.1920### Weaviate Cloud Instance2122If 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](https://console.weaviate.cloud/signin?utm_source=github&utm_campaign=agent_skills).2324## Before Building Any Cookbook2526Follow these shared guidelines before generating any cookbook app:2728- [Project Setup Contract](references/project_setup.md)29- [Environment Requirements](references/environment_requirements.md)3031Then proceed to the specific cookbook reference below.3233## Cookbook Index3435- [Query Agent Chatbot](references/query_agent_chatbot.md): Build a full-stack chatbot using Weaviate Query Agent with streaming and chat history support.36- [Data Explorer](references/data_explorer.md): Build a full-stack data explorer app including sorting, keyword search and tabular view of weaviate data.37- [Multimodal RAG: Building Document Search](references/pdf_multimodal_rag.md): Build a multimodal Retrieval-Augmented Generation (RAG) system using Weaviate Embeddings (ModernVBERT/colmodernvbert) and Ollama with Qwen3-VL for generation.38- [Basic RAG](references/basic_rag.md): Implement basic retrieval and generation with Weaviate. Useful for most forms of data retrieval from a Weaviate collection.39- [Advanced RAG](references/advanced_rag.md): Improve on basic RAG by adding extra features such as re-ranking, query decomposition, query re-writing, LLM filter selection.40- [Basic Agent](references/basic_agent.md): Build a tool-calling AI agent with structured outputs using DSPy. Covers AgentResponse signatures, RouterAgent, tool design, and sequential multi-step loops.41- [Agentic RAG](references/agentic_rag.md): 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.4243## Interface (Optional)4445Use this when the user explicitly asks for a frontend for their Weaviate backend.4647- [Frontend Interface](references/frontend_interface.md): Build a Next.js frontend to interact with the Weaviate backend.4849## Client Usage5051- [Async Client](references/async_client.md): 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.5253## Limitations5455- Cookbook blueprints still need adaptation to the user's data model, embedding provider, auth model, deployment platform, and latency/cost targets.56- This skill does not validate live Weaviate credentials, cloud quotas, or model availability unless the user provides and approves the relevant environment.57- Generated apps should be reviewed for security, data privacy, prompt injection exposure, and production observability before launch.
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