Traitement de documents RAG piloté par IA et chatbot - Google Drive, Supabase, OpenAI

• •Informations d'identification Google Drive API
• •Clé API OpenAI
• •Clé API Google Gemini
• •URL et Clé API Supabase

{
  "meta": {
    "instanceId": "d1786ab0d745a7498abf13a9c2cdabb1374c006e889b79eef64ce0386b8f8a41",
    "templateCredsSetupCompleted": true
  },
  "nodes": [
    {
      "id": "f13af0a4-02e8-41b4-9ad8-55443604f031",
      "name": "Boucle sur les éléments",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        540,
        -80
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "3a3a9bee-fd2c-48fb-a946-850bdd574b21",
      "name": "Extraire du PDF",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        1720,
        -120
      ],
      "parameters": {
        "options": {},
        "operation": "pdf"
      },
      "typeVersion": 1
    },
    {
      "id": "42a44446-4cd9-4b8f-bcc8-dc912954e515",
      "name": "Extraire de CSV",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        1720,
        80
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "2141dbdd-4fce-4306-a852-581b138864e4",
      "name": "Chargeur de données par défaut",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        3800,
        80
      ],
      "parameters": {
        "options": {
          "metadata": {
            "metadataValues": [
              {
                "name": "file_id",
                "value": "={{ $('Set File ID').first().json.file_id}}"
              },
              {
                "name": "title",
                "value": "={{ $('Create Metadata Title & Description').item.json.output.title }}"
              },
              {
                "name": "description",
                "value": "={{ $('Create Metadata Title & Description').item.json.output.description }}"
              }
            ]
          }
        },
        "jsonData": "={{ $json.concatenated_text }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "af8b85b1-1cfa-4b76-b94e-48213ae232d8",
      "name": "Déclencheur fichier créé Google Drive",
      "type": "n8n-nodes-base.googleDriveTrigger",
      "position": [
        280,
        -80
      ],
      "parameters": {
        "event": "fileCreated",
        "options": {},
        "pollTimes": {
          "item": [
            {
              "mode": "everyMinute"
            }
          ]
        },
        "triggerOn": "specificFolder",
        "folderToWatch": {
          "__rl": true,
          "mode": "url",
          "value": "https://drive.google.com/drive/u/0/folders/1B-Wl-ktVFbTmX685DB978jNvs9Ihnxiv"
        }
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "SEUhrgz30NMJS3cH",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "97304af3-3a09-4724-8e87-7fff0ece6eca",
      "name": "Séparateur de texte par caractères",
      "type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter",
      "position": [
        3900,
        260
      ],
      "parameters": {
        "separator": "###SPLIT###"
      },
      "typeVersion": 1
    },
    {
      "id": "af31206c-94e6-48fe-895e-f8a000486457",
      "name": "Définir l'ID du fichier",
      "type": "n8n-nodes-base.set",
      "position": [
        800,
        -60
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "0144a9a7-6e73-46c4-979f-838ad5c62b89",
              "name": "file_id",
              "type": "string",
              "value": "={{ $json.id }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "580b1192-be67-46f1-aea4-ae1aaeab10ea",
      "name": "Télécharger le fichier",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        1040,
        -60
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $('Set File ID').item.json.file_id }}"
        },
        "options": {
          "googleFileConversion": {
            "conversion": {
              "docsToFormat": "application/pdf"
            }
          }
        },
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "SEUhrgz30NMJS3cH",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "af92c035-f1f0-47e4-b542-c13a7386892b",
      "name": "Commutateur",
      "type": "n8n-nodes-base.switch",
      "position": [
        1320,
        -60
      ],
      "parameters": {
        "rules": {
          "values": [
            {
              "outputKey": "application/pdf",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "e959d5a8-d311-4a29-b400-7c07468a72fe",
                    "operator": {
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $binary.data.mimeType }}",
                    "rightValue": "application/pdf"
                  }
                ]
              },
              "renameOutput": true
            },
            {
              "outputKey": "text/csv",
              "conditions": {
                "options": {
                  "version": 2,
                  "leftValue": "",
                  "caseSensitive": true,
                  "typeValidation": "strict"
                },
                "combinator": "and",
                "conditions": [
                  {
                    "id": "d5ae6f2d-e62d-4e08-aa06-629e6dfa7ee8",
                    "operator": {
                      "name": "filter.operator.equals",
                      "type": "string",
                      "operation": "equals"
                    },
                    "leftValue": "={{ $binary.data.mimeType }}",
                    "rightValue": "text/csv"
                  }
                ]
              },
              "renameOutput": true
            }
          ]
        },
        "options": {}
      },
      "typeVersion": 3.2
    },
    {
      "id": "1a2575ef-8792-4389-84ea-a016c5ba535e",
      "name": "Analyseur de sortie structurée",
      "type": "@n8n/n8n-nodes-langchain.outputParserStructured",
      "position": [
        2540,
        160
      ],
      "parameters": {
        "jsonSchemaExample": "{\n\t\"title\": \"Test Title (Replace it with real title\",\n    \"description\":\"Test Description (Replace it with real description)\"\n}"
      },
      "typeVersion": 1.2
    },
    {
      "id": "0c8ba080-daab-493d-8c7d-4f1c7e9fb1e7",
      "name": "Modèle de chat Google Gemini",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        3320,
        40
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "gdaO8lU3HwsldifM",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "e12a0a90-a1d7-4de0-901a-58ce35b95675",
      "name": "Traiter le contexte",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        3160,
        -120
      ],
      "parameters": {
        "text": "=<document> \n{{ $('Document Data').first().json.data }}\n</document> \n\nHere is the chunk we want to situate within the overall document:\n\n<chunk> \n{{ $json.chunk }}\n</chunk> \n\nPlease:\n- Provide a short and succinct **context** to situate this chunk within the document for improved search retrieval.\n- Return the **original chunk** exactly as provided unless a correction is necessary.\n- If the chunk contains an **incomplete number, percentage, or entity**, correct it using the full document.\n- If part of a **sentence is cut off**, reconstruct the missing words only if necessary for clarity.\n- If the chunk is part of a table, include the complete table entry to maintain data integrity\n- Do not add any additional explanations or formatting beyond the required output.\n\nFill in the following format:\n[succinct context] : [original chunk or corrected version if necessary]\n\nYour response should contain only the text that replaces these placeholders, without including the placeholder labels themselves.",
        "promptType": "define"
      },
      "typeVersion": 1.6
    },
    {
      "id": "d6f500ba-c0f3-4cc8-af60-e2c38307582c",
      "name": "Données du document",
      "type": "n8n-nodes-base.set",
      "position": [
        1980,
        -120
      ],
      "parameters": {
        "mode": "raw",
        "options": {},
        "jsonOutput": "={\n  \"data\":  {{ JSON.stringify($json.text) }}\n} "
      },
      "typeVersion": 3.4
    },
    {
      "id": "de223e76-1d95-41a9-b1ff-961d8b744044",
      "name": "Diviser",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        2780,
        -120
      ],
      "parameters": {
        "options": {
          "destinationFieldName": "chunk"
        },
        "fieldToSplitOut": "chunks"
      },
      "typeVersion": 1
    },
    {
      "id": "7d2eb334-a73c-4777-b4a9-757356201311",
      "name": "Limiter",
      "type": "n8n-nodes-base.limit",
      "position": [
        2960,
        -120
      ],
      "parameters": {
        "maxItems": 20
      },
      "typeVersion": 1
    },
    {
      "id": "d8f88a96-bed6-461a-ae5f-6924c655ba84",
      "name": "Résumer",
      "type": "n8n-nodes-base.summarize",
      "position": [
        3480,
        -120
      ],
      "parameters": {
        "options": {},
        "fieldsToSummarize": {
          "values": [
            {
              "field": "text",
              "separateBy": "other",
              "aggregation": "concatenate",
              "customSeparator": "###SPLIT###"
            }
          ]
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "820dda10-a369-4415-a739-16f197d0b1ad",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        3680,
        80
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "MGwGMKEkdcjzlYCw",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "1a622d78-d5f1-498d-8882-1cbc9166e4b4",
      "name": "Ajouter des données au magasin vectoriel Supabase",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
        3680,
        -120
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "documents",
          "cachedResultName": "documents"
        }
      },
      "credentials": {
        "supabaseApi": {
          "id": "XijKo1j1EsG3XUhz",
          "name": "Supabase account"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "51d8e196-8752-4fe7-a182-c4a00d3caefa",
      "name": "Modèle de chat Google Gemini1",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        2340,
        180
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "gdaO8lU3HwsldifM",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "23b491e1-3732-46eb-a62b-c86b975df894",
      "name": "Note adhésive",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        200,
        -260
      ],
      "parameters": {
        "color": 4,
        "width": 1960,
        "height": 560,
        "content": "## 📁 Data Processing from Google Drive\nAutomatically monitors your Google Drive folder and processes new files. When you upload a PDF, CSV, or Google Doc, this section downloads the file, extracts all the text content, and prepares it for AI analysis. Think of it as your automatic document reader that never sleeps."
      },
      "typeVersion": 1
    },
    {
      "id": "5d95cc1c-5dec-4238-8ecc-d47d5c107e53",
      "name": "Note adhésive1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2220,
        -260
      ],
      "parameters": {
        "color": 4,
        "width": 1940,
        "height": 700,
        "content": "## 🧠 RAG Data Upload Pipeline\nTakes your extracted document text and makes it AI-ready. It creates smart summaries, breaks documents into digestible chunks, adds context to each piece, and stores everything in a searchable database. This is where your documents become \"AI-searchable\" knowledge that can answer questions."
      },
      "typeVersion": 1
    },
    {
      "id": "d5ab29ee-d9a6-48c6-a2d5-20374cda6296",
      "name": "Créer un titre et une description de métadonnées",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        2280,
        -120
      ],
      "parameters": {
        "text": "=Create metadata title and metadata description based on the document specified below (below ##Document). Metadata title and metadata description will be used to seperate data in vector DB that will be used for RAG.\n\n##Document\n{{ $('Document Data').item.json.data }}",
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.6
    },
    {
      "id": "8eddba99-f77b-4b85-b2ef-0aa68762b851",
      "name": "Diviser en morceaux",
      "type": "n8n-nodes-base.code",
      "position": [
        2620,
        -120
      ],
      "parameters": {
        "jsCode": "const chunkSize = 1000;\nconst chunkOverlap = 200;\n//const text = $node[\"ABC\"].json.data.replace(/\\n/, '');\nconst text = $items(\"Document Data\")[0].json.data.replace(/\\n/, '');\n\nconst chunks = [];\nlet remainingText = text;\n\nwhile (remainingText.length > 0) {\n    let splitPoint;\n\n    // Try splitting at paragraph level first\n    splitPoint = remainingText.lastIndexOf(\"\\n\\n\", chunkSize);\n    \n    // If no paragraph split, try splitting at sentence level\n    if (splitPoint === -1) {\n        splitPoint = remainingText.lastIndexOf(\". \", chunkSize);\n    }\n\n    // If no sentence split, try splitting at word level\n    if (splitPoint === -1) {\n        splitPoint = remainingText.lastIndexOf(\" \", chunkSize);\n    }\n\n    // If still no split point, force cut at chunkSize\n    if (splitPoint === -1 || splitPoint < chunkSize * 0.5) {  \n        splitPoint = chunkSize; // Hard split if no good split point\n    }\n\n    // Extract chunk and adjust remaining text with overlap\n    let chunk = remainingText.substring(0, splitPoint).trim();\n    chunks.push(chunk);\n\n    // Move the pointer forward while keeping the overlap\n    remainingText = remainingText.substring(Math.max(0, splitPoint - chunkOverlap)).trim();\n\n    // Break if remaining text is too small to form another chunk\n    if (remainingText.length < chunkSize * 0.2) {\n        chunks.push(remainingText);\n        break;\n    }\n}\n\nreturn { chunks };"
      },
      "typeVersion": 2
    },
    {
      "id": "2cd80097-3f90-441a-b0ef-08b43c30981b",
      "name": "Modèle de chat OpenAI",
      "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
      "position": [
        1900,
        -740
      ],
      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
        },
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "MGwGMKEkdcjzlYCw",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "03ae06b7-c804-4e35-bac5-17836123771a",
      "name": "Magasin vectoriel Supabase",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
      "position": [
        2200,
        -700
      ],
      "parameters": {
        "mode": "retrieve-as-tool",
        "topK": 20,
        "options": {},
        "toolName": "documents",
        "tableName": {
          "__rl": true,
          "mode": "list",
          "value": "documents",
          "cachedResultName": "documents"
        },
        "toolDescription": "Work with your data in Supabase Vector Store"
      },
      "credentials": {
        "supabaseApi": {
          "id": "XijKo1j1EsG3XUhz",
          "name": "Supabase account"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "c22f1100-86b0-4d49-b0b5-baa3f997c0a0",
      "name": "Agent IA",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1940,
        -960
      ],
      "parameters": {
        "text": "=User Input:\n{{ $json.chatInput }}",
        "options": {
          "systemMessage": "=You're an internal company knowledge assistants. Your job is to answer questions using the files in the Vector Database. Here's how to do it:\n\nCheck Vector Database First\n- Search for similar test chunks using RAG.\n- If you find matches, use them to answer the question. \n- If you find relevant matches and need additional context to support the answer, prioritize retrieving it from documents that share the same metadata \"file_id\" & \"title\" before exploring other sources.\"\n\nNo Answer Found?\n- Clearly say: \"I couldn't find this in the databases\"\n- Never guess or invent answers.\n\nExample Response:\n\"Netflix grew revenue in Streaming services by 20% in 2024.\""
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "8ff3b246-2078-4c69-934a-fde989c5dbda",
      "name": "Embeddings OpenAI1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        2280,
        -520
      ],
      "parameters": {
        "options": {}
      },
      "credentials": {
        "openAiApi": {
          "id": "MGwGMKEkdcjzlYCw",
          "name": "OpenAi account"
        }
      },
      "typeVersion": 1.2
    },
    {
      "id": "4fc4edb9-b5a1-4d4b-b571-cf665cf4eae1",
      "name": "À la réception d'un message de chat",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        1700,
        -960
      ],
      "webhookId": "2d4c41cb-c8b7-49c7-a8eb-ebd3dd9ff46d",
      "parameters": {
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "3fc29837-d177-4af0-a842-57705ef8b97d",
      "name": "Mémoire simple",
      "type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
      "position": [
        2060,
        -700
      ],
      "parameters": {
        "contextWindowLength": 10
      },
      "typeVersion": 1.3
    },
    {
      "id": "27ca3852-baac-41fa-a8bc-620dd36fac2d",
      "name": "Note adhésive2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1520,
        -1080
      ],
      "parameters": {
        "color": 4,
        "width": 1180,
        "height": 760,
        "content": "## AI Chat Agent - Chat with your documents stored in Supabase Vector Store\nour personal AI assistant that can answer questions about all your uploaded documents. Ask anything in plain English - it searches through your document database, finds relevant information, and gives you accurate answers with source references. It remembers your conversation and never makes up information."
      },
      "typeVersion": 1
    },
    {
      "id": "ee2b6e9d-a49b-435e-bc38-7d3a4e989ca5",
      "name": "Note adhésive7",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        340,
        -1080
      ],
      "parameters": {
        "color": 5,
        "width": 460,
        "height": 320,
        "content": "## 📄 AI-Powered RAG Document Processing & Chatbot with Google Drive, Supabase, OpenAI\n\n**What This Workflow Does:**\n- Watches a Google Drive folder for new documents\n- Extracts text from PDF/CSV files\n- Enhances the text using AI (Google Gemini)\n- Chunks and embeds the text using OpenAI\n- Stores vector data in Supabase for semantic search\n- Enables users to chat with documents using OpenAI"
      },
      "typeVersion": 1
    },
    {
      "id": "f533c790-36d0-4f7e-a004-1ab64fdbd046",
      "name": "Note adhésive8",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -480,
        -260
      ],
      "parameters": {
        "color": 5,
        "width": 660,
        "height": 1500,
        "content": "## 📋 Data Processing & RAG Workflow\n\n### 📁 Step 1: Google Drive Trigger  \nWatches a specific Google Drive folder for new file uploads. Triggers every minute.\n\n### 🔁 Step 2: Loop Over Uploaded Files  \nEnsures each uploaded file is processed one-by-one to avoid bulk processing issues.\n\n### 🆔 Step 3: Set File ID  \nExtracts and stores the Google Drive file ID for reference in later steps.\n\n### 📥 Step 4: Download File  \nDownloads the file (PDF, CSV, or Google Docs). Google Docs are auto-converted to PDF.\n\n### 🧭 Step 5: File Type Switch  \nRoutes file based on type:\n- PDF → Go to PDF Text Extractor\n- CSV → Go to CSV Extractor\n\n### 📄 Step 6A: Extract Text from PDF  \nExtracts raw text content from the PDF file.\n\n### 📊 Step 6B: Extract Text from CSV  \nExtracts and formats CSV data into a readable string.\n\n### 📦 Step 7: Format Document Data  \nWraps extracted text into JSON: `{\"data\": \"text content\"}`.\n\n### 🧠 Step 8: Generate Metadata (Gemini)  \nUses Gemini AI to create a document title and description.\n\n### ✂️ Step 9: Split into Chunks  \nSplits the document text into 1000-character chunks with 200-character overlap, intelligently at paragraph/sentence/word level.\n\n### 🔄 Step 10: Split Out Chunks  \nConverts array of chunks into individual workflow items.\n\n### 🚫 Step 11: Limit to 20 Chunks  \nLimits processing to first 20 chunks to control API and processing load.\n\n### 🧠 Step 12: Enhance Chunk Context (Gemini)  \nEach chunk is enhanced for better clarity, continuity, and context using Gemini.\n\n### 🧩 Step 13: Summarize  \nCombines enhanced chunks using `###SPLIT###` separator for further processing.\n\n### 🧷 Step 14: Re-Split Enhanced Chunks  \nSplits combined text back into chunks using `###SPLIT###` separator.\n\n### 🧬 Step 15: Generate Embeddings (OpenAI)  \nConverts text chunks into vector embeddings for semantic search.\n\n### 🗂️ Step 16: Add Metadata  \nEach chunk is wrapped with metadata (file ID, title, description) for vector DB.\n\n### 🧠 Step 17: Store in Supabase Vector DB  \nStores embeddings in Supabase vector store for later semantic retrieval.\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "2090caca-8fff-461b-a9b5-5460b8978c3c",
      "name": "Note adhésive6",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1520,
        -1640
      ],
      "parameters": {
        "color": 5,
        "width": 1160,
        "height": 520,
        "content": "\n## SETUP REQUIRED  \n**Author:** [Billy Christi](https://n8n.io/creators/billy/)\n\n---\n\n### 📚 Database: Supabase Table `documents`\n\nTo manage and store embeddings and metadata, we use a Supabase table with the following schema:\n\n```sql\nCREATE TABLE public.documents (\n  id bigserial PRIMARY KEY,         -- Unique identifier\n  content text,                     -- Textual content of the document\n  metadata jsonb,                   -- Additional metadata (e.g., title, source)\n  embedding vector                  -- Vector embeddings (for similarity search, etc.)\n);\n````\n### 🔐 Required Credentials & Keys\n✅ **Google Drive Credential** \n✅ **Supabase API Key & Project URL** \n✅ **OpenAI API Key**\n✅ **Google Gemini API Key**\n\n\n"
      },
      "typeVersion": 1
    },
    {
      "id": "989d4cef-fae3-4163-a51e-58cf9aafac61",
      "name": "Note adhésive9",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        820,
        -1080
      ],
      "parameters": {
        "color": 5,
        "width": 660,
        "height": 760,
        "content": "\n## 🤖 CHAT INTERFACE WORKFLOW\n\n### 💬 Step 1: Chat Trigger  \nListens for user chat input (e.g., question about a document).\n\n### 🧠 Step 2: Simple Memory  \nStores last 10 messages to maintain conversation context.\n\n### 🔍 Step 3: Vector Search (Supabase)  \nSearches Supabase vector DB for relevant chunks related to the user's question.\n\n### 🧬 Step 4: Convert Question to Embedding  \nUses OpenAI to embed the question text for similarity search.\n\n### 🗨️ Step 5: Generate Response (OpenAI GPT-4o-mini)  \nUses context + question to generate a natural language answer.\n\n### 🧠 Step 6: AI Agent  \nFinal agent logic:\n- Searches vector DB first\n- Answers based on found chunks\n- Attributes response to correct document\n- Admits if answer not found (no hallucination)"
      },
      "typeVersion": 1
    },
    {
      "id": "8d2661d4-3bdc-456e-bba0-766d89ab3ece",
      "name": "Note adhésive3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2860,
        20
      ],
      "parameters": {
        "color": 5,
        "width": 340,
        "height": 300,
        "content": "📌 **Understanding the Limit Node**\nThe **Limit Node** restricts the number of records or items processed in your workflow. This is especially useful for:\n✅ **Testing** your setup with a smaller dataset\n✅ **Controlling resource usage** during processing\n\n💡 **You can**:\n* **Adjust the limit value** to suit your workflow's needs\n* **Remove the Limit Node** altogether to process all available data"
      },
      "typeVersion": 1
    }
  ],
  "pinData": {},
  "connections": {
    "7d2eb334-a73c-4777-b4a9-757356201311": {
      "main": [
        [
          {
            "node": "e12a0a90-a1d7-4de0-901a-58ce35b95675",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "af92c035-f1f0-47e4-b542-c13a7386892b": {
      "main": [
        [
          {
            "node": "3a3a9bee-fd2c-48fb-a946-850bdd574b21",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "42a44446-4cd9-4b8f-bcc8-dc912954e515",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "c22f1100-86b0-4d49-b0b5-baa3f997c0a0": {
      "main": [
        []
      ]
    },
    "de223e76-1d95-41a9-b1ff-961d8b744044": {
      "main": [
        [
          {
            "node": "7d2eb334-a73c-4777-b4a9-757356201311",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "d8f88a96-bed6-461a-ae5f-6924c655ba84": {
      "main": [
        [
          {
            "node": "1a622d78-d5f1-498d-8882-1cbc9166e4b4",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "af31206c-94e6-48fe-895e-f8a000486457": {
      "main": [
        [
          {
            "node": "580b1192-be67-46f1-aea4-ae1aaeab10ea",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "d6f500ba-c0f3-4cc8-af60-e2c38307582c": {
      "main": [
        [
          {
            "node": "d5ab29ee-d9a6-48c6-a2d5-20374cda6296",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "580b1192-be67-46f1-aea4-ae1aaeab10ea": {
      "main": [
        [
          {
            "node": "af92c035-f1f0-47e4-b542-c13a7386892b",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "3fc29837-d177-4af0-a842-57705ef8b97d": {
      "ai_memory": [
        [
          {
            "node": "c22f1100-86b0-4d49-b0b5-baa3f997c0a0",
            "type": "ai_memory",
            "index": 0
          }
        ]
      ]
    },
    "f13af0a4-02e8-41b4-9ad8-55443604f031": {
      "main": [
        [],
        [
          {
            "node": "af31206c-94e6-48fe-895e-f8a000486457",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "e12a0a90-a1d7-4de0-901a-58ce35b95675": {
      "main": [
        [
          {
            "node": "d8f88a96-bed6-461a-ae5f-6924c655ba84",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "42a44446-4cd9-4b8f-bcc8-dc912954e515": {
      "main": [
        [
          {
            "node": "d6f500ba-c0f3-4cc8-af60-e2c38307582c",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "3a3a9bee-fd2c-48fb-a946-850bdd574b21": {
      "main": [
        [
          {
            "node": "d6f500ba-c0f3-4cc8-af60-e2c38307582c",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "820dda10-a369-4415-a739-16f197d0b1ad": {
      "ai_embedding": [
        [
          {
            "node": "1a622d78-d5f1-498d-8882-1cbc9166e4b4",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "2cd80097-3f90-441a-b0ef-08b43c30981b": {
      "ai_languageModel": [
        [
          {
            "node": "c22f1100-86b0-4d49-b0b5-baa3f997c0a0",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "8eddba99-f77b-4b85-b2ef-0aa68762b851": {
      "main": [
        [
          {
            "node": "de223e76-1d95-41a9-b1ff-961d8b744044",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "8ff3b246-2078-4c69-934a-fde989c5dbda": {
      "ai_embedding": [
        [
          {
            "node": "03ae06b7-c804-4e35-bac5-17836123771a",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "2141dbdd-4fce-4306-a852-581b138864e4": {
      "ai_document": [
        [
          {
            "node": "1a622d78-d5f1-498d-8882-1cbc9166e4b4",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "03ae06b7-c804-4e35-bac5-17836123771a": {
      "ai_tool": [
        [
          {
            "node": "c22f1100-86b0-4d49-b0b5-baa3f997c0a0",
            "type": "ai_tool",
            "index": 0
          }
        ]
      ]
    },
    "97304af3-3a09-4724-8e87-7fff0ece6eca": {
      "ai_textSplitter": [
        [
          {
            "node": "2141dbdd-4fce-4306-a852-581b138864e4",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    },
    "0c8ba080-daab-493d-8c7d-4f1c7e9fb1e7": {
      "ai_languageModel": [
        [
          {
            "node": "e12a0a90-a1d7-4de0-901a-58ce35b95675",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "1a2575ef-8792-4389-84ea-a016c5ba535e": {
      "ai_outputParser": [
        [
          {
            "node": "d5ab29ee-d9a6-48c6-a2d5-20374cda6296",
            "type": "ai_outputParser",
            "index": 0
          }
        ]
      ]
    },
    "51d8e196-8752-4fe7-a182-c4a00d3caefa": {
      "ai_languageModel": [
        [
          {
            "node": "d5ab29ee-d9a6-48c6-a2d5-20374cda6296",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "4fc4edb9-b5a1-4d4b-b571-cf665cf4eae1": {
      "main": [
        [
          {
            "node": "c22f1100-86b0-4d49-b0b5-baa3f997c0a0",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "af8b85b1-1cfa-4b76-b94e-48213ae232d8": {
      "main": [
        [
          {
            "node": "f13af0a4-02e8-41b4-9ad8-55443604f031",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "d5ab29ee-d9a6-48c6-a2d5-20374cda6296": {
      "main": [
        [
          {
            "node": "8eddba99-f77b-4b85-b2ef-0aa68762b851",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}