---

name: weaviate-rag description: Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems. allowed-tools: Read, Write, Grep, Glob

Weaviate RAG Configuration Skill

Configure MoodleNRW RAG system with Weaviate vector store.

Trigger

• RAG system setup or troubleshooting
• Vector store configuration
• Document embedding requests

Running Services

• Weaviate HTTP: localhost:8095
• Weaviate gRPC: localhost:50055
• Chainlit UI: localhost:8000

Server Paths

• RAG System: /opt/cloodle/tools/ai/multi_agent_rag_system/
• Chatbot: /opt/cloodle/tools/ai/moodle-chatbot/

Weaviate Client Configuration

Copyimport weaviate

client = weaviate.Client(
    url="http://localhost:8095",
    additional_headers={
        "X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY", "")
    }
)

Docker Commands

Copy# Start Weaviate
cd /opt/cloodle/tools/ai/multi_agent_rag_system
docker-compose up -d

# Check status
docker ps | grep weaviate

# View logs
docker logs multi_agent_rag_system_weaviate_1

Schema Creation

Copyschema = {
    "class": "MoodleDocument",
    "vectorizer": "text2vec-transformers",
    "properties": [
        {"name": "content", "dataType": ["text"]},
        {"name": "source", "dataType": ["string"]},
        {"name": "course_id", "dataType": ["int"]}
    ]
}
client.schema.create_class(schema)

Embedding Models (Local)

Start Chainlit

Copycd /opt/cloodle/tools/ai/multi_agent_rag_system
source .venv/bin/activate
chainlit run app.py