Agent skill
weaviate-rag
Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/weaviate-rag
SKILL.md
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
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