Fortgeschrittener Multi-Query-RAG-System basierend auf Supabase und GPT-5

• •OpenAI API Key
• •Supabase URL und API Key

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        "description": "Use this tool after you got the output of the knowledge retrieval tool to critically analyse the initial user query and the content you've retrieved from the knowledge retrieval tool and reason to prepare the best answer possible, challenge the content to be sure that you actually have the right information to be able to respond.\n\nBe very token efficient when using this tool, write 50 words max which is enough to reason."
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        "content": "# Advanced Multi-Query RAG Agent\n\nThis template demonstrates a sophisticated RAG (Retrieval-Augmented Generation) pattern for building high-quality AI agents. It's designed to overcome the limitations of a basic RAG setup.\n\n## How it works\n\nInstead of a simple query, this agent uses a more intelligent, four-step process:\n1.  **Decompose:** It breaks complex questions into multiple, simpler sub-queries.\n2.  **Retrieve:** It sends these queries to a smart sub-workflow that fetches data from your vector store.\n3.  **Filter:** The sub-workflow filters out any retrieved information that doesn't meet a minimum relevance score, ensuring high-quality context.\n4.  **Synthesize:** The agent uses a \"Think\" tool to reason over the filtered information before crafting a final, comprehensive answer.\n\n## How to use\n\n1.  **Connect your accounts:** You need to connect **Supabase** and **OpenAI** in both this main workflow and in the \"RAG sub-workflow\".\n2.  **Customize the agent:** Edit the **AI Agent's system prompt** to match your specific knowledge base (e.g., \"You are a helpful assistant that answers based on our company's internal documents.\").\n3.  **Adjust the relevance filter:** In the sub-workflow, you can change the similarity score in the **Filter** node (default is >0.4) to control the quality of the retrieved information."
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