RAG-basierter LLM-Chatbot

• •Google Drive API-Anmeldedaten
• •OpenAI API Key
• •Pinecone API Key

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  "name": "ChatBot For RAG Based LLMs",
  "tags": [],
  "nodes": [
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      "name": "Bei Empfang einer Chat-Nachricht",
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          "systemMessage": "=You are an AI assistant specialized in analyzing user queries and retreive the data using pine cone vectore store via Vectore store QnA Tool. \n\nYour primary task is to answer questions accurately and precisely using the vector database, which contains relevant documents.\n\nOnly provide information that you retrieve from the documents (or verified expert knowledge). If something is not included in the dataset or is unclear, clearly state that you do not have sufficient information.\n\nStructure of your responses:\n• Concise and to the point\n• Specific numbers and facts, when available\n• Clearly indicate which quarterly tax deduction with the information comes from\n\nObjective:\nProvide users with reliable and quick insights to user questions without unnecessary details."
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          "value": "gpt-4o-mini"
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      "parameters": {
        "options": {
          "pineconeNamespace": "<yourNameSpace>"
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        "pineconeIndex": {
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    {
      "id": "c5130a15-b2fa-4c96-b890-6cda5a5d1e8a",
      "name": "Google Drive-Trigger",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
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      ],
      "parameters": {
        "event": "fileCreated",
        "options": {},
        "pollTimes": {
          "item": [
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              "mode": "everyMinute"
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        },
        "triggerOn": "specificFolder",
        "folderToWatch": {
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        "operation": "download"
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      "name": "Standard-Datenlader",
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      "parameters": {
        "loader": "pdfLoader",
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        "dataType": "binary"
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        "content": "## Data Loader\n- #### GDrive - Trigger \n  This node will trigger for every minute and retrieve details for any change to the folder.\n- #### GDrive - Trigger\n  This node will download the latest changes from GDrive and pass them to pin cone vector store\n- #### PineCone Vector Store\n  Here, we are actually storing the files by splitting them by leveraging the capabilities of default data loader. \n\n\n"
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      "name": "OpenAI Einbettungen",
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          "dimensions": 512
        }
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        "content": "## Data Retrieval\nWe have an AI Agent that connects to Vector Store and Retrieve the required information to the user questions. \n\nThis connects to Vector Store QnA tool which then further connects to Vector Store to fetch the information and share to Ai Agent for further processing. "
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        "description": "Returns documents related to company polices and everything important from those policy documents"
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