Agent skill
weaviate-rag
Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems.
Stars
163
Forks
31
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
python
import weaviate
client = weaviate.Client(
url="http://localhost:8095",
additional_headers={
"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY", "")
}
)
Docker Commands
bash
# 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
python
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
bash
cd /opt/cloodle/tools/ai/multi_agent_rag_system
source .venv/bin/activate
chainlit run app.py
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