What is the purpose of the Weaviate RAG skill?

It automates the configuration and management of Retrieval-Augmented Generation systems using Weaviate vector databases for semantic data retrieval.

How does it handle the chat interface?

The skill provides configuration and execution commands for Chainlit, allowing you to run a specialized chatbot UI on port 8000.

Does this skill work with Docker?

Yes, it includes standardized Docker Compose commands for starting, monitoring, and managing Weaviate services and the multi-agent RAG system.

Can I use this for Moodle-based data?

Yes, it includes specific schema configurations designed for MoodleDocument classes, including course IDs and content properties.

Which embedding models does this skill support?

It supports a variety of models including OpenAI embeddings and local options like nomic-embed-text, bge-m3, and mxbai-embed-large.

Weaviate RAG Integration

Name: Weaviate RAG Integration
Author: astoeffer

byastoeffer

Data Science & ML

Implements and manages Retrieval-Augmented Generation (RAG) systems using Weaviate vector databases for semantic search and document retrieval.

About

This skill streamlines the deployment and orchestration of RAG architectures by providing pre-configured patterns for Weaviate vector stores. It enables developers to efficiently manage document embeddings, schema creation, and local model integration within systems like MoodleNRW. By offering standardized Docker workflows, gRPC/HTTP configuration, and Chainlit UI setup, the skill simplifies the transition from raw documents to a fully functional, AI-powered knowledge base capable of handling complex semantic queries.

Key Features

Support for local embedding models including nomic-embed-text and bge-m3
0 GitHub stars
Pre-configured Docker Compose workflows for rapid infrastructure deployment
Seamless Chainlit integration for building interactive chatbot interfaces
Automated Weaviate vector store configuration and schema definition
Specialized handling for Moodle-based document metadata and course indexing

Use Cases

Building a semantic search engine for educational content and documentation
Setting up a private, local RAG system for secure document retrieval
Troubleshooting vector database connectivity and indexing within containerized environments

About

Key Features

Support for local embedding models including nomic-embed-text and bge-m3
0 GitHub stars
Pre-configured Docker Compose workflows for rapid infrastructure deployment
Seamless Chainlit integration for building interactive chatbot interfaces
Automated Weaviate vector store configuration and schema definition
Specialized handling for Moodle-based document metadata and course indexing

Use Cases

Building a semantic search engine for educational content and documentation
Setting up a private, local RAG system for secure document retrieval
Troubleshooting vector database connectivity and indexing within containerized environments