weaviate-cookbooks

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Weaviate Cookbooks

Overview

概述

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.

本技能提供了构建基于Weaviate的AI应用的实现指南索引和基础要求。可参考这些指南，快速搭建遵循连接管理、环境设置和应用架构最佳实践的全栈应用。

Weaviate Cloud Instance

Weaviate云实例

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.

如果用户还没有实例，请引导他们前往云控制台注册并创建免费沙箱。通过Weaviate Cloud创建Weaviate实例。

Before Building Any Cookbook

构建任何Cookbook之前

Follow these shared guidelines before generating any cookbook app:

Project Setup Contract
Environment Requirements

Then proceed to the specific cookbook reference below.

在生成任何Cookbook应用之前，请遵循以下通用指南：

项目设置约定
环境要求

之后再继续查看下方特定的Cookbook参考指南。

Cookbook Index

Cookbook索引

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.

Query Agent聊天机器人：使用支持流式传输和聊天历史的Weaviate Query Agent构建全栈聊天机器人。
数据探索器：构建一个包含排序、关键词搜索和Weaviate数据表格视图的全栈数据探索器应用。
多模态RAG：构建文档搜索：使用Weaviate Embeddings（ModernVBERT/colmodernvbert）和搭配Qwen3-VL的Ollama构建多模态检索增强生成（RAG）系统。
基础RAG：使用Weaviate实现基础的检索与生成功能，适用于从Weaviate集合中检索大多数类型的数据。
高级RAG：通过添加重排序、查询分解、查询重写、LLM过滤器选择等额外功能，优化基础RAG的性能。
基础Agent：使用DSPy构建具备结构化输出的工具调用AI Agent，涵盖AgentResponse签名、RouterAgent、工具设计以及多步骤顺序循环。
智能RAG：使用Weaviate构建基于RAG的AI Agent，涵盖原生RAG工具、LLM创建过滤器的分层RAG、向量数据库内存、Weaviate Query Agent以及Elysia集成。

Interface (Optional)

界面（可选）

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.

当用户明确要求为其Weaviate后端构建前端时使用本部分。

前端界面：构建一个Next.js前端以与Weaviate后端交互。