Construir un sistema RAG de múltiples consultas avanzado basado en Supabase y GPT-5

• •Clave de API de OpenAI
• •URL y Clave de API de Supabase

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        "content": "## Filtering system\nOnly keeping chunks that have a score >0.4"
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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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