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Workflow n8n

Automatisation Google Gemini avec n8n : traitement de formulaires

Ce workflow n8n est conçu pour automatiser le traitement des soumissions de formulaires en intégrant des modèles de langage avancés comme Google Gemini. Dans un contexte où les entreprises cherchent à améliorer leur efficacité opérationnelle, ce workflow permet de traiter et d'analyser les données soumises par les utilisateurs, tout en offrant des réponses personnalisées basées sur l'intelligence artificielle. Les cas d'usage incluent la gestion des retours clients, l'analyse des feedbacks et la génération de contenu automatisé.

  • Étape 1 : Le workflow démarre avec le déclencheur 'On form submission4', qui capte les soumissions de formulaires.
  • Étape 2 : Les données sont ensuite traitées en lot grâce au noeud 'Loop Over Items1'.
  • Étape 3 : Une condition est appliquée à l'aide du noeud 'If2' pour déterminer le chemin à suivre selon les données soumises.
  • Étape 4 : Les informations sont agrégées via le noeud 'Aggregate1', permettant une synthèse des réponses.
  • Étape 5 : Le modèle de langage Google Gemini est ensuite utilisé pour générer des réponses adaptées aux utilisateurs, en passant par plusieurs noeuds de traitement comme 'Google Gemini Chat Model5' et 'AI Agent'. Enfin, les résultats sont formatés et envoyés par email via le noeud 'Gmail'. Les bénéfices business incluent une réduction significative du temps de traitement des demandes, une amélioration de la satisfaction client grâce à des réponses rapides et pertinentes, ainsi qu'une optimisation des ressources humaines, permettant aux équipes de se concentrer sur des tâches à plus forte valeur ajoutée.
Tags clés :automatisationGoogle Geminiworkflown8nintelligence artificielle
🎯Pour qui ?🧩Problème résolu📋Étapes du flux🛠Guide de personnalisation
Catégorie: Webhook · Tags: automatisation, Google Gemini, workflow, n8n, intelligence artificielle❤ 0

Workflow n8n Google Gemini, intelligence artificielle : vue d'ensemble

Schéma des nœuds et connexions de ce workflow n8n, généré à partir du JSON n8n.

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Workflow n8n Google Gemini, intelligence artificielle : détail des nœuds

  • On form submission4

    Ce noeud déclenche le workflow lors de la soumission d'un formulaire.

  • Loop Over Items1

    Ce noeud permet de traiter les éléments en les divisant en plusieurs lots.

  • If2

    Ce noeud évalue des conditions pour déterminer le chemin à suivre dans le workflow.

  • Aggregate1

    Ce noeud agrège des données en fonction des champs spécifiés.

  • Google Gemini Chat Model5

    Ce noeud utilise le modèle de chat Google Gemini pour générer des réponses.

  • split the binary item

    Ce noeud exécute un code JavaScript pour traiter un élément binaire.

  • Parsing the document

    Ce noeud effectue une requête HTTP pour analyser un document.

  • Check the parsing status

    Ce noeud vérifie l'état de l'analyse d'un document via une requête HTTP.

  • Provide the markdown

    Ce noeud fournit un contenu en markdown via une requête HTTP.

  • Google Gemini Chat Model6

    Ce noeud utilise à nouveau le modèle de chat Google Gemini pour générer des réponses.

  • Markdown

    Ce noeud transforme du texte en markdown selon les options spécifiées.

  • Gmail

    Ce noeud envoie un email via Gmail avec les paramètres fournis.

  • Code

    Ce noeud exécute un code JavaScript pour effectuer des traitements spécifiques.

  • Convert to File

    Ce noeud convertit des données en fichier selon les options spécifiées.

  • AI Agent

    Ce noeud utilise un agent AI pour traiter du texte selon un type de prompt.

  • Google Gemini Chat Model

    Ce noeud utilise le modèle de chat Google Gemini pour générer des réponses.

  • Information Extractor

    Ce noeud extrait des informations d'un texte en utilisant des attributs spécifiés.

  • Google Gemini Chat Model1

    Ce noeud utilise le modèle de chat Google Gemini pour générer des réponses.

  • Convert to File4

    Ce noeud convertit des données en fichier selon les options spécifiées.

  • Pinecone Vector Store

    Ce noeud interagit avec le magasin de vecteurs Pinecone selon les options fournies.

  • Embeddings Mistral Cloud

    Ce noeud génère des embeddings via Mistral Cloud selon les options spécifiées.

  • Default Data Loader

    Ce noeud charge des données par défaut pour un type de document spécifié.

  • Recursive Character Text Splitter

    Ce noeud divise un texte en segments en utilisant un séparateur de caractères récursif.

  • When chat message received

    Ce noeud déclenche le workflow lors de la réception d'un message de chat.

  • Question and Answer Chain

    Ce noeud exécute une chaîne de questions et réponses pour traiter des informations.

  • Google Gemini Chat Model2

    Ce noeud utilise le modèle de chat Google Gemini pour générer des réponses.

  • Vector Store Retriever

    Ce noeud récupère des données à partir d'un magasin de vecteurs.

  • Pinecone Vector Store1

    Ce noeud interagit avec le magasin de vecteurs Pinecone selon les options fournies.

  • Embeddings Mistral Cloud1

    Ce noeud génère des embeddings via Mistral Cloud selon les options spécifiées.

  • AI Agent1

    Ce noeud utilise un agent AI pour traiter du texte selon un type de prompt.

  • Google Gemini Chat Model3

    Ce noeud utilise le modèle de chat Google Gemini pour générer des réponses.

  • Sticky Note

    Ce noeud crée une note autocollante avec le contenu spécifié.

  • Translator Agent

    Ce noeud utilise un agent AI pour traiter du texte selon un type de prompt.

  • Analyzer Agent

    Ce noeud utilise un agent AI pour traiter du texte selon un type de prompt.

  • Sticky Note1

    Ce noeud crée une note autocollante avec le contenu spécifié.

  • Sticky Note2

    Ce noeud crée une note autocollante avec le contenu spécifié.

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{
  "id": "cGNK44mkCzIh4113",
  "meta": {
    "instanceId": "44c282b5a828cd0d7dda8a13c9168fe32406aaef7e8faa5a847408311387e400"
  },
  "name": "My workflow 3",
  "tags": [],
  "nodes": [
    {
      "id": "4db348cf-bd5a-408e-b212-d75b792460b4",
      "name": "On form submission4",
      "type": "n8n-nodes-base.formTrigger",
      "position": [
        -1720,
        20
      ],
      "webhookId": "34a4ae98-8eb8-486b-8d7e-dd5fdde15cd5",
      "parameters": {
        "options": {},
        "formTitle": "form which gets multiple files",
        "formFields": {
          "values": [
            {
              "fieldType": "file",
              "fieldLabel": "file1",
              "requiredField": true
            },
            {
              "fieldType": "file",
              "fieldLabel": "file2"
            },
            {
              "fieldLabel": "provide your mail Id",
              "requiredField": true
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "6a1f197f-310e-4eb1-926f-60cfbae60a49",
      "name": "Loop Over Items1",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        -380,
        20
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3,
      "alwaysOutputData": false
    },
    {
      "id": "7eb210e4-687c-4e9d-b2e7-50d0b85da8dc",
      "name": "If2",
      "type": "n8n-nodes-base.if",
      "position": [
        700,
        100
      ],
      "parameters": {
        "options": {},
        "conditions": {
          "options": {
            "version": 2,
            "leftValue": "",
            "caseSensitive": true,
            "typeValidation": "strict"
          },
          "combinator": "and",
          "conditions": [
            {
              "id": "1edbcd59-130d-4053-9db3-cb8dec068fe0",
              "operator": {
                "name": "filter.operator.equals",
                "type": "string",
                "operation": "equals"
              },
              "leftValue": "={{ $json.status }}",
              "rightValue": "SUCCESS"
            }
          ]
        }
      },
      "typeVersion": 2.2
    },
    {
      "id": "e76b9523-3f87-4ad3-87df-1e4e93ead090",
      "name": "Aggregate1",
      "type": "n8n-nodes-base.aggregate",
      "position": [
        0,
        0
      ],
      "parameters": {
        "options": {},
        "fieldsToAggregate": {
          "fieldToAggregate": [
            {
              "fieldToAggregate": "markdown"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "21234dcf-52dc-4ae0-975e-36a1a18ed456",
      "name": "Google Gemini Chat Model5",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1060,
        180
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "52fcaca7-c49d-4004-96a3-0094ed0e510f",
      "name": "split the binary item",
      "type": "n8n-nodes-base.code",
      "position": [
        -1000,
        20
      ],
      "parameters": {
        "jsCode": "// Get the input data\nconst items = $input.all()\n\n// Initialize an array to hold the split items\nconst splitItems = [];\n\n// Iterate over each item\nitems.forEach(item => {\n  // Check if the item has binary data\n  if (item.binary) {\n    // Iterate over each binary field\n    for (const [key, value] of Object.entries(item.binary)) {\n      // Create a new item for each binary file\n      splitItems.push({\n        json: {},\n        binary: {\n          data: value\n        }\n      });\n    }\n  }\n});\n\n// Return the split items\nreturn splitItems;"
      },
      "typeVersion": 2,
      "alwaysOutputData": true
    },
    {
      "id": "4660eef4-de62-4b13-9f51-05000b1afa33",
      "name": "Parsing the document",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        260,
        100
      ],
      "parameters": {
        "url": "https://api.cloud.llamaindex.ai/api/parsing/upload",
        "method": "POST",
        "options": {
          "redirect": {
            "redirect": {}
          }
        },
        "sendBody": true,
        "contentType": "multipart-form-data",
        "sendHeaders": true,
        "bodyParameters": {
          "parameters": [
            {
              "name": "=file",
              "parameterType": "formBinaryData",
              "inputDataFieldName": "=data"
            }
          ]
        },
        "headerParameters": {
          "parameters": [
            {
              "name": "accept",
              "value": "application/json"
            },
            {
              "name": "Authorization",
              "value": "Bearer $secret token"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "07e76215-b9d4-4adb-b8f3-f8c8615abb56",
      "name": "Check the parsing status",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        480,
        100
      ],
      "parameters": {
        "url": "=https://api.cloud.llamaindex.ai/api/parsing/job/{{ $json.id }}",
        "options": {},
        "sendHeaders": true,
        "headerParameters": {
          "parameters": [
            {
              "name": "accept",
              "value": "application/json"
            },
            {
              "name": "Authorization",
              "value": "Bearer $secret token"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "3909a632-7002-4d60-a53b-3f73e4958c27",
      "name": "Provide the markdown",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        1180,
        400
      ],
      "parameters": {
        "url": "=https://api.cloud.llamaindex.ai/api/parsing/job/{{ $json.id }}/result/markdown",
        "options": {},
        "sendHeaders": true,
        "headerParameters": {
          "parameters": [
            {
              "name": "accept",
              "value": "application/json"
            },
            {
              "name": "Authorization",
              "value": "Bearer $secret token"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "89c25b95-8bc6-4bb5-82d2-1f870416c4af",
      "name": "Google Gemini Chat Model6",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1600,
        300
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "8ddf6e94-6da0-4ef9-a6dc-0db8967914a6",
      "name": "Markdown",
      "type": "n8n-nodes-base.markdown",
      "position": [
        2140,
        0
      ],
      "parameters": {
        "mode": "markdownToHtml",
        "options": {},
        "markdown": "={{ $json.output }}",
        "destinationKey": "html"
      },
      "typeVersion": 1
    },
    {
      "id": "59c33f95-6f8f-4992-8421-dc3a0b668861",
      "name": "Gmail",
      "type": "n8n-nodes-base.gmail",
      "position": [
        4540,
        0
      ],
      "webhookId": "35fdc2a2-b8f8-4217-be0b-66ed98a548f1",
      "parameters": {
        "sendTo": "={{ $('On form submission4').item.json['provide your mail Id'] }}",
        "message": "=Hi user,\nThe below document contains the detailed analysis of the provided document.\n\nYou can also use the below link to interact with the assistant regarding your doubts on the analysis\nhttps://pavithranvh28.app.n8n.cloud/webhook/8c5c9e83-f595-4e4b-b45c-544a9a0840c4/chat\n\n\n",
        "options": {
          "attachmentsUi": {
            "attachmentsBinary": [
              {}
            ]
          }
        },
        "subject": "Analysis of the documents provided",
        "emailType": "text"
      },
      "typeVersion": 2.1
    },
    {
      "id": "b9bb8338-d52e-4f5b-bd2f-d517851b6014",
      "name": "Code",
      "type": "n8n-nodes-base.code",
      "position": [
        3200,
        0
      ],
      "parameters": {
        "jsCode": "const items = $input.first().json.html;\n\n// Ensure items is an array\nconst htmlArray = Array.isArray(items) ? items : [items];\n\nfunction htmlToFormattedText(html) {\n    // Replace heading tags (h1-h6) with bold text\n    html = html.replace(/<h[1-6]>(.*?)<\\/h[1-6]>/gi, \"\\n**$1**\\n\");\n\n    // Replace paragraph tags with spacing\n    html = html.replace(/<p>(.*?)<\\/p>/gi, \"\\n$1\\n\");\n\n    // Replace line breaks with newline characters\n    html = html.replace(/<br\\s*\\/?>/gi, \"\\n\");\n\n    // Remove all other HTML tags\n    html = html.replace(/<[^>]+>/g, \"\").trim();\n\n    // Remove extra newlines\n    return html.replace(/\\n{2,}/g, \"\\n\").trim();\n}\n\nconst updatedItems = htmlArray.map((item) => {\n    const htmlContent = item?.json?.html || item;\n    const textContent = htmlToFormattedText(htmlContent);\n    return { textContent };\n});\n\nreturn updatedItems;\n"
      },
      "typeVersion": 2
    },
    {
      "id": "e8176d99-3625-47a5-8989-80fdce053ba7",
      "name": "Convert to File",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        3840,
        0
      ],
      "parameters": {
        "options": {},
        "operation": "toText",
        "sourceProperty": "textContent"
      },
      "typeVersion": 1.1
    },
    {
      "id": "9fb1a6a0-c49f-48f5-93bc-f0c6e9b8a138",
      "name": "AI Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        2600,
        460
      ],
      "parameters": {
        "text": "={{ $json.output }}",
        "options": {
          "systemMessage": "You are a helpful assistant.\nObjective:\nThe agent must process the input content to enhance readability, apply structured formatting, and bold necessary text elements while preserving the original meaning.\n\nProcessing Rules:\nApply Text Formatting:\n\nConvert any text enclosed with * (asterisks) into bold.\nStructurize the Content:\n\nOrganize the content using clear section headers.\nSeparate sections with line breaks for readability.\nEnsure proper indentation and bullet point usage for clarity.\nMaintain Clarity & Coherence:\n\nReformat the text without changing the core meaning.\nRemove redundancy while ensuring key details remain intact.\nText File Compatibility:\n\nResponse NEEDS TO BE A TEXT FILE"
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "9995921a-ca41-40c5-9159-350908ca8213",
      "name": "Google Gemini Chat Model",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        2780,
        740
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "bd281ef9-bc33-4b3a-9d3f-41d00521b14e",
      "name": "Information Extractor",
      "type": "@n8n/n8n-nodes-langchain.informationExtractor",
      "position": [
        2820,
        880
      ],
      "parameters": {
        "text": "={{ $json.output }}",
        "options": {
          "systemPromptTemplate": "You are an expert extraction algorithm.\nOnly extract relevant information from the text.\nIf you do not know the value of an attribute asked to extract, you may omit the attribute's value."
        },
        "attributes": {
          "attributes": [
            {
              "name": "Project Overview",
              "description": "overview of the content extracted"
            },
            {
              "name": "System and prerequisites",
              "description": "=which contains the information about the system and the prerequisites needed"
            }
          ]
        }
      },
      "typeVersion": 1
    },
    {
      "id": "2f1c4efb-6885-48c9-b2a6-a13d2e9b4f66",
      "name": "Google Gemini Chat Model1",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        3140,
        1100
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "b7a0276a-d253-43a8-a7f3-fb3b83599d7f",
      "name": "Convert to File4",
      "type": "n8n-nodes-base.convertToFile",
      "position": [
        1840,
        740
      ],
      "parameters": {
        "options": {},
        "operation": "toText",
        "sourceProperty": "output"
      },
      "typeVersion": 1.1
    },
    {
      "id": "7441e1ff-1966-4535-abaa-ee565db787de",
      "name": "Pinecone Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
        2080,
        980
      ],
      "parameters": {
        "mode": "insert",
        "options": {},
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "samuraichamploo",
          "cachedResultName": "samuraichamploo"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "82d9d9fb-6f8c-4c86-9287-d5e7e73f58a7",
      "name": "Embeddings Mistral Cloud",
      "type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
      "position": [
        2140,
        1200
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "3a3332f7-3fda-4898-999e-c5020c0ea02e",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        2240,
        1280
      ],
      "parameters": {
        "options": {},
        "dataType": "binary"
      },
      "typeVersion": 1
    },
    {
      "id": "4a625913-5c98-4075-9022-058e863af326",
      "name": "Recursive Character Text Splitter",
      "type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
      "position": [
        2320,
        1460
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "5e38e72b-390f-433f-a638-522537bf1369",
      "name": "When chat message received",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
        -1820,
        660
      ],
      "webhookId": "8c5c9e83-f595-4e4b-b45c-544a9a0840c4",
      "parameters": {
        "public": true,
        "options": {}
      },
      "typeVersion": 1.1
    },
    {
      "id": "77ea1fd6-73a9-42f6-835f-b945ce7fd294",
      "name": "Question and Answer Chain",
      "type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
      "position": [
        -1400,
        680
      ],
      "parameters": {
        "options": {
          "systemPromptTemplate": "You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n----------------\nContext: {context}"
        }
      },
      "typeVersion": 1.5
    },
    {
      "id": "d71b0efe-7e27-44f8-beb4-370c02ef1d5f",
      "name": "Google Gemini Chat Model2",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        -1380,
        1020
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "eb967c9d-415f-4992-bacd-517b7dddd6bf",
      "name": "Vector Store Retriever",
      "type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
      "position": [
        -1220,
        900
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "690eacb5-1d47-430b-8914-4c474833be0b",
      "name": "Pinecone Vector Store1",
      "type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
      "position": [
        -900,
        1060
      ],
      "parameters": {
        "options": {},
        "pineconeIndex": {
          "__rl": true,
          "mode": "list",
          "value": "samuraichamploo",
          "cachedResultName": "samuraichamploo"
        }
      },
      "typeVersion": 1.1
    },
    {
      "id": "6d289625-ca46-49d7-8ee2-5996dc645ebe",
      "name": "Embeddings Mistral Cloud1",
      "type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
      "position": [
        -780,
        1460
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 1
    },
    {
      "id": "dcc8bb20-e5f3-428e-93be-dc4081e1463c",
      "name": "AI Agent1",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        -460,
        680
      ],
      "parameters": {
        "text": "={{ $json.response }}",
        "options": {
          "systemMessage": "You are a helpful assistant.rephrase the prompt provided and Provide ONLY the response in a text format"
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "777b4e63-7a0a-42fa-9069-83ab006e19a9",
      "name": "Google Gemini Chat Model3",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        -400,
        980
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-1.5-flash"
      },
      "typeVersion": 1
    },
    {
      "id": "f5d69ecd-9cdc-4bef-a8ae-7477dfc3f7c7",
      "name": "Sticky Note",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -1900,
        440
      ],
      "parameters": {
        "width": 360,
        "content": "## I'm a note \nThe below workflow is a chatbot workflow which will be triggered when a user types his/her prompt related to document the user provided for analysis on the chatbot link which was ent to the user via mail."
      },
      "typeVersion": 1
    },
    {
      "id": "7140d564-a636-4937-b2c6-811b48dde851",
      "name": "Translator Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1060,
        0
      ],
      "parameters": {
        "text": "={{ $('Aggregate1').item.json.markdown }}",
        "options": {
          "systemMessage": "You are a helpful assistant. Kindly check the prompt fed and  find the language of the  prompt you receive  and if the prompt is in other language except english translate it and provide that as a response and also ATTACH THE REMAINING PROMPT WHICH IS IN ENGLISH WITH THE RESPONSE"
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "496d49f1-5b7f-48ab-b759-2facd8fade8d",
      "name": "Analyzer Agent",
      "type": "@n8n/n8n-nodes-langchain.agent",
      "position": [
        1420,
        0
      ],
      "parameters": {
        "text": "={{ $json.output }}",
        "options": {
          "systemMessage": "=You are a helpful assistant. \n1️⃣ Comprehensive Prompt Analysis\n\nCarefully examine the entire prompt provided by the user.\nEnsure all details are considered before formulating a response.\n2️⃣ Interactive and Clear Breakdown\n\nStructure the response in a well-organized manner with clear topic separation.\nPresent insights in a way that enhances understanding and usability.\n3️⃣ Duplicate Check and Handling\n\nIdentify and highlight any repeated information within the prompt.\nIf duplicates exist, consolidate the relevant details to avoid redundancy.\n4️⃣ Reliable and Actionable Resolutions\n\nProvide resolutions that are dependable and practical.\nEnsure the solutions align with the context and user’s intent.\nWhere applicable, offer alternative approaches for flexibility.\nAlso make sure not to add too much of star or hash to indicate the difference"
        },
        "promptType": "define"
      },
      "typeVersion": 1.8
    },
    {
      "id": "4218f320-c580-41ae-91ca-134bd2cc8128",
      "name": "Sticky Note1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        2880,
        640
      ],
      "parameters": {
        "content": "## I'm a note \nThese two subflows are for trial purpose"
      },
      "typeVersion": 1
    },
    {
      "id": "bc0a3e52-1259-4651-bbe4-9ba727d8e46a",
      "name": "Sticky Note2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1420,
        700
      ],
      "parameters": {
        "content": "## I'm a note \nThis subflow is responsible for storing the translated as well as the analyzed contents into the vector database to feed as a knowledge to the chatbot"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "7c6e0b7c-dfbb-45e5-bc16-038ff6175cdc",
  "connections": {
    "If2": {
      "main": [
        [
          {
            "node": "Provide the markdown",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Check the parsing status",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Code": {
      "main": [
        [
          {
            "node": "Convert to File",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "AI Agent": {
      "main": [
        []
      ]
    },
    "Markdown": {
      "main": [
        [
          {
            "node": "Code",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Aggregate1": {
      "main": [
        [
          {
            "node": "Translator Agent",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Analyzer Agent": {
      "main": [
        [
          {
            "node": "Markdown",
            "type": "main",
            "index": 0
          },
          {
            "node": "AI Agent",
            "type": "main",
            "index": 0
          },
          {
            "node": "Information Extractor",
            "type": "main",
            "index": 0
          },
          {
            "node": "Convert to File4",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Convert to File": {
      "main": [
        [
          {
            "node": "Gmail",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Convert to File4": {
      "main": [
        [
          {
            "node": "Pinecone Vector Store",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Items1": {
      "main": [
        [
          {
            "node": "Aggregate1",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "Parsing the document",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Translator Agent": {
      "main": [
        [
          {
            "node": "Analyzer Agent",
            "type": "main",
            "index": 0
          },
          {
            "node": "Convert to File4",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Default Data Loader": {
      "ai_document": [
        [
          {
            "node": "Pinecone Vector Store",
            "type": "ai_document",
            "index": 0
          }
        ]
      ]
    },
    "On form submission4": {
      "main": [
        [
          {
            "node": "split the binary item",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Parsing the document": {
      "main": [
        [
          {
            "node": "Check the parsing status",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Provide the markdown": {
      "main": [
        [
          {
            "node": "Loop Over Items1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "split the binary item": {
      "main": [
        [
          {
            "node": "Loop Over Items1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Pinecone Vector Store1": {
      "ai_vectorStore": [
        [
          {
            "node": "Vector Store Retriever",
            "type": "ai_vectorStore",
            "index": 0
          }
        ]
      ]
    },
    "Vector Store Retriever": {
      "ai_retriever": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "ai_retriever",
            "index": 0
          }
        ]
      ]
    },
    "Check the parsing status": {
      "main": [
        [
          {
            "node": "If2",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Mistral Cloud": {
      "ai_embedding": [
        [
          {
            "node": "Pinecone Vector Store",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Embeddings Mistral Cloud1": {
      "ai_embedding": [
        [
          {
            "node": "Pinecone Vector Store1",
            "type": "ai_embedding",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model1": {
      "ai_languageModel": [
        [
          {
            "node": "Information Extractor",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model2": {
      "ai_languageModel": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model3": {
      "ai_languageModel": [
        [
          {
            "node": "AI Agent1",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model5": {
      "ai_languageModel": [
        [
          {
            "node": "Translator Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model6": {
      "ai_languageModel": [
        [
          {
            "node": "Analyzer Agent",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "Question and Answer Chain": {
      "main": [
        [
          {
            "node": "AI Agent1",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "When chat message received": {
      "main": [
        [
          {
            "node": "Question and Answer Chain",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Recursive Character Text Splitter": {
      "ai_textSplitter": [
        [
          {
            "node": "Default Data Loader",
            "type": "ai_textSplitter",
            "index": 0
          }
        ]
      ]
    }
  }
}

Workflow n8n Google Gemini, intelligence artificielle : pour qui est ce workflow ?

Ce workflow s'adresse aux entreprises de taille moyenne à grande, notamment celles qui gèrent un volume important de soumissions de formulaires. Les équipes marketing et service client, ainsi que les professionnels de l'IT, trouveront un intérêt particulier dans cette automatisation n8n, qui nécessite un niveau technique intermédiaire pour sa mise en œuvre.

Workflow n8n Google Gemini, intelligence artificielle : problème résolu

Ce workflow résout le problème du traitement manuel des soumissions de formulaires, qui peut être long et sujet à des erreurs. En automatisant ce processus, il élimine les frustrations liées aux délais de réponse et réduit le risque d'erreurs humaines. Les utilisateurs bénéficient d'une gestion plus rapide et efficace des demandes, ainsi que d'une analyse approfondie des données soumises, ce qui leur permet de mieux comprendre les besoins de leurs clients.

Workflow n8n Google Gemini, intelligence artificielle : étapes du workflow

Étape 1 : Le workflow commence par le déclencheur 'On form submission4', qui capte les soumissions.

  • Étape 1 : Les données sont ensuite traitées en lot avec 'Loop Over Items1'.
  • Étape 2 : Une condition est vérifiée via 'If2' pour déterminer le traitement approprié.
  • Étape 3 : Les réponses sont agrégées grâce à 'Aggregate1'.
  • Étape 4 : Le modèle Google Gemini est utilisé pour générer des réponses via plusieurs noeuds comme 'Google Gemini Chat Model5'.
  • Étape 5 : Les résultats sont formatés avec 'Markdown' et envoyés par email via 'Gmail'.

Workflow n8n Google Gemini, intelligence artificielle : guide de personnalisation

Pour personnaliser ce workflow, commencez par ajuster le déclencheur 'On form submission4' pour qu'il corresponde à votre formulaire spécifique. Modifiez les paramètres dans les noeuds Google Gemini pour adapter les réponses générées selon vos besoins. Vous pouvez également personnaliser les messages envoyés par email dans le noeud 'Gmail' en modifiant le sujet et le contenu. Enfin, pensez à intégrer d'autres outils ou services selon votre écosystème, en utilisant des noeuds HTTP Request pour des appels API externes.