weaviate-rag
Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems.
npx skills add majiayu000/claude-skill-registry --skill weaviate-rag --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 (L
- Trigger
- Running Services
- Server Paths
- Weaviate Client Configuration
- Docker Commands
- Schema Creation
- Embedding Models (Local)
- Start Chainlit
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 source .venv/bin/activate chainlit run app.py
What does the weaviate-rag skill do?
Implement RAG systems using Weaviate vector database. Use when building semantic search, document retrieval, or knowledge base systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill weaviate-rag --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.