68 lines
4.0 KiB
Markdown
68 lines
4.0 KiB
Markdown
---
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name: weaviate-cookbooks
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description: "Build Weaviate AI apps from official cookbook blueprints for RAG, agentic RAG, data exploration, multimodal PDF search, async clients, and frontends."
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category: ai
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risk: safe
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source: community
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source_repo: weaviate/agent-skills
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source_type: official
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date_added: "2026-06-29"
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author: Weaviate
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tags: [weaviate, rag, agents, vector-database, ai-apps]
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tools: [python, weaviate, nextjs]
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license: "BSD-3-Clause"
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license_source: "https://github.com/weaviate/agent-skills/blob/main/LICENSE"
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---
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# Weaviate Cookbooks
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## Overview
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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.
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## When to Use This Skill
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- Use when the user wants a Weaviate-backed RAG, agentic RAG, chatbot, data explorer, or multimodal document-search application.
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- Use when selecting between cookbook patterns before writing a full-stack Weaviate app.
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- Use when the project needs Weaviate environment, setup, async-client, or frontend guidance.
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- Use when the user asks for an official Weaviate blueprint rather than a generic vector database recipe.
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### Weaviate Cloud Instance
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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](https://console.weaviate.cloud/signin?utm_source=github&utm_campaign=agent_skills).
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## Before Building Any Cookbook
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Follow these shared guidelines before generating any cookbook app:
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- [Project Setup Contract](references/project_setup.md)
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- [Environment Requirements](references/environment_requirements.md)
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Then proceed to the specific cookbook reference below.
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## Cookbook Index
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- [Query Agent Chatbot](references/query_agent_chatbot.md): Build a full-stack chatbot using Weaviate Query Agent with streaming and chat history support.
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- [Data Explorer](references/data_explorer.md): Build a full-stack data explorer app including sorting, keyword search and tabular view of weaviate data.
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- [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.
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- [Basic RAG](references/basic_rag.md): Implement basic retrieval and generation with Weaviate. Useful for most forms of data retrieval from a Weaviate collection.
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- [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.
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- [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.
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- [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.
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## Interface (Optional)
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Use this when the user explicitly asks for a frontend for their Weaviate backend.
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- [Frontend Interface](references/frontend_interface.md): Build a Next.js frontend to interact with the Weaviate backend.
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## Client Usage
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- [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.
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## Limitations
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- Cookbook blueprints still need adaptation to the user's data model, embedding provider, auth model, deployment platform, and latency/cost targets.
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- This skill does not validate live Weaviate credentials, cloud quotas, or model availability unless the user provides and approves the relevant environment.
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- Generated apps should be reviewed for security, data privacy, prompt injection exposure, and production observability before launch.
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