weaviate-rag

Name: weaviate-rag
Availability: InStock
Author: astoeffer

Configure a Weaviate RAG system with Docker Compose and embedding models.

Updated Aug 27, 2026

Search Tags:#semantic search #rag #weaviate #embedding #vector store

Ready to run

npx skills add https://github.com/astoeffer/plugin-marketplace --skill weaviate-rag

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: weaviate-rag
Source: https://github.com/astoeffer/plugin-marketplace/tree/main/plugins/cloodle-ai-integration/skills/weaviate-rag
Command: npx skills add https://github.com/astoeffer/plugin-marketplace --skill weaviate-rag

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Implements a Weaviate-based RAG system for document embedding, retrieval, and semantic search.

Core Features & Use Cases

Weaviate Client: Configures a Weaviate client with embedding support.
Schema & Embedding: Defines a MoodleDocument class and embedding models.
Dockerized Run: Docker commands to start Weaviate and Chainlit UI.

Quick Start

Start Weaviate, initialize the schema, and run the chat/UI interface to begin semantic search on your documents.

Frequently Asked Questions about weaviate-rag

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I set up semantic search on documents using Weaviate?▼

Semantic search with Weaviate involves configuring a vector store to embed and retrieve documents by meaning rather than keyword matching. This Skill configures a Weaviate client with embedding models, defines a schema for document storage, and deploys the system via Docker to enable similarity-based retrieval across your document corpus.

What embedding models can I use with Weaviate for RAG?▼

Weaviate supports multiple embedding models including nomic-embed-text, bge-m3, and mxbai-embed-large. This Skill configures the text2vec-transformers vectorizer to automatically embed documents during ingestion, allowing you to choose the model that best fits your semantic search performance and latency requirements.

Can I run Weaviate locally with Docker for RAG development?▼

Yes. This Skill provides Docker Compose configuration to deploy Weaviate locally with HTTP endpoint at localhost:8095 and gRPC at localhost:50055, enabling you to develop and test RAG workflows on your machine before moving to production environments.

How do I configure a Weaviate client to work with the OpenAI API?▼

Configure the Weaviate client by providing an OPENAI API key header for authentication. This Skill handles client setup with the required credentials, allowing your RAG system to authenticate embedding and retrieval requests within MoodleNRW workflows and Chainlit UI interfaces.

What's the difference between semantic search and keyword search for document retrieval?▼

Keyword search matches exact terms; semantic search understands meaning and context to find relevant documents even when phrasing differs. Weaviate-powered RAG uses vector embeddings to perform semantic search, returning contextually similar results that keyword systems might miss.

Do I need to define a schema before storing documents in Weaviate?▼

Yes. This Skill defines a MoodleDocument schema with the text2vec-transformers vectorizer, specifying how documents are structured and embedded. The schema ensures consistent document processing and enables efficient semantic search across stored entries.