weaviate-rag

Development

Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems.

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How to use this skill

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/ai-llm/weaviate-rag/SKILL.md

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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

import weaviate

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

Docker Commands

# 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

schema = {
    "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)

Model	Dimensions	Best For
nomic-embed-text	768	General purpose
bge-m3	1024	Multilingual
mxbai-embed-large	1024	High quality

Start Chainlit

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