Reordenamiento de RAG

• •Credenciales de API de Google Drive
• •Clave de API de OpenAI
• •URL y Clave de API de Supabase

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  "name": "RAG Reranking",
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  "nodes": [
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          "value": "16ahWlNwBvd53xFHA4UUh6EbkFd8ogxBv",
          "cachedResultUrl": "https://drive.google.com/file/d/16ahWlNwBvd53xFHA4UUh6EbkFd8ogxBv/view?usp=drivesdk",
          "cachedResultName": "Rules_of_Golf_Simplified.pdf"
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              {
                "name": "ruleNumber",
                "value": "={{ $('Metadata Agent').item.json.output }}"
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      "name": "Nota Adhesiva",
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      "position": [
        -320,
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      ],
      "parameters": {
        "color": 2,
        "width": 1000,
        "height": 440,
        "content": "# Vectorize Document w/ Metadata\n(this code node is set up for the golf rules PDF specifically)"
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    },
    {
      "id": "406521ff-0f01-4688-a352-62ae49d71ff6",
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        "width": 620,
        "height": 380,
        "content": "# RAG Agent\n"
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        "content": "## Vector Store w/ Reranker\n"
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      "parameters": {
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          "systemMessage": "=# Overview\nYour job is to understand the rule number that the human is requesting and output only the number.\n\n## Example\nInput - what's rule number 27?\nOutput - 27"
        }
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    },
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      "parameters": {
        "options": {
          "systemMessage": "=# Overview\nYou are an AI agent who is an expert at the rules of golf. You will receive a question from the human, and you must use your tool called \"Supabase Vector Store\" in order to retrieve information from the database to make sure you are answering the question accurately. "
        }
      },
      "typeVersion": 2
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      "name": "Agente RAG 2",
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      "parameters": {
        "text": "={{ $('When chat message received').item.json.chatInput }}",
        "options": {
          "systemMessage": "=# Overview\nYou are an AI agent who is an expert at the rules of golf. You will receive a question from the human, and you must use your tool called \"Supabase Vector Store\" in order to retrieve information from the database to make sure you are answering the question accurately. "
        },
        "promptType": "define"
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      "position": [
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      ],
      "parameters": {
        "color": 4,
        "width": 440,
        "height": 400,
        "content": "# RAG Agent\n"
      },
      "typeVersion": 1
    },
    {
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      "name": "Nota Adhesiva4",
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      "position": [
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        "content": "## Vector Store w/ Reranker & Metadata\n"
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      "position": [
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        "width": 440,
        "height": 400,
        "content": "# Metadata Agent\n"
      },
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      "position": [
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      "parameters": {
        "width": 700,
        "height": 800,
        "content": "# 🛠️ Setup Guide  \n**Author:** [Nate Herk](https://www.youtube.com/@nateherk)\n\nFollow the steps below to get your Retrieval-Augmented Generation (RAG) workflow up and running:\n\n### ✅ Step 1: Connect Your [Supabase](https://supabase.com/) Vector Store  \nEnsure your Supabase instance is ready and accessible. This will store your embedded documents with metadata.\nHere is a [video tutorial](https://youtu.be/JjBofKJnYIU) on setting that up.\n\n### ✅ Step 2: Connect Your [OpenAI](https://platform.openai.com/account/api-keys) Embeddings  \nUse the `text-embedding-3-small` or similar model for embedding your documents. Make sure your API key is active.\n\n### ✅ Step 3: Connect Your [OpenAI API Key](https://platform.openai.com/account/api-keys)  \nThis powers your embedding generation model. Add it via the HTTP Request node or a credential.\n\n### ✅ Step 4: Add Your [OpenRouter](https://openrouter.ai/) API Key  \nUse this for your main RAG agent—add your key via HTTP request or credential node.\n\n### ✅ Step 5: Connect a [Cohere](https://dashboard.cohere.com/api-keys) Re-Ranker  \nThe re-ranker improves answer quality. Add your API key for better relevance ranking on retrieved documents.\n\n### ✅ Step 6: Vectorize Documents with Metadata  \nEnsure your data ingestion process tags documents with meaningful metadata before vectorization. This helps with structured retrieval.\n\n### 💬 Final Step: Start Chatting  \nPrompt your agent and test the RAG flow end-to-end—watch it pull context-rich answers from your vector store.\n"
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