🦙👁️👁️ Encontrar el mejor modelo visual local de Ollama mediante comparación

Avanzado

Este es unAIflujo de automatización del dominio deautomatización que contiene 19 nodos.Utiliza principalmente nodos como Set, SplitOut, GoogleDocs, GoogleDrive, HttpRequest, combinando tecnología de inteligencia artificial para lograr automatización inteligente. Comparación de modelos de visión locales de Ollama para análisis de imágenes usando Google Docs

Requisitos previos
  • Credenciales de API de Google Drive
  • Pueden requerirse credenciales de autenticación para la API de destino
Vista previa del flujo de trabajo
Visualización de las conexiones entre nodos, con soporte para zoom y panorámica
Exportar flujo de trabajo
Copie la siguiente configuración JSON en n8n para importar y usar este flujo de trabajo
{
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  "meta": {
    "instanceId": "31e69f7f4a77bf465b805824e303232f0227212ae922d12133a0f96ffeab4fef",
    "templateCredsSetupCompleted": true
  },
  "name": "🦙👁️👁️ Find the Best Local Ollama Vision Models by Comparison",
  "tags": [],
  "nodes": [
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      "position": [
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      "parameters": {
        "color": 4,
        "width": 340,
        "height": 340,
        "content": "## 👁️ Analyze Image with Local Ollama LLM\n"
      },
      "typeVersion": 1
    },
    {
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      "parameters": {
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        "width": 300,
        "height": 300,
        "content": "## 👍Try Me!"
      },
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    },
    {
      "id": "3a56f75b-4836-4c37-a246-83ef6507c581",
      "name": "Al hacer clic en 'Probar flujo de trabajo'",
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      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "bb4570c7-269c-4d28-85d4-183ca2fabb89",
      "name": "Solicitud Ollama LLM",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        1200,
        1280
      ],
      "parameters": {
        "url": "http://127.0.0.1:11434/api/chat",
        "method": "POST",
        "options": {},
        "jsonBody": "={{ $json.body }}",
        "sendBody": true,
        "sendHeaders": true,
        "specifyBody": "json",
        "headerParameters": {
          "parameters": [
            {
              "name": "Content-Type",
              "value": " application/json"
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      "typeVersion": 4.2
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    {
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      "name": "Crear cuerpo de solicitud",
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        "assignments": {
          "assignments": [
            {
              "id": "be9a8e21-9bb6-4588-a77a-61bc2def0648",
              "name": "body",
              "type": "string",
              "value": "={\n  \"model\": \"{{ $json.models }}\",\n  \"messages\": [\n    {\n      \"role\": \"user\",\n      \"content\": \"{{ $json.user_prompt }}\",\n      \"images\": [\"{{ $('List of Vision Models').item.json.data }}\"]\n    }\n  ],\n  \"stream\": false\n}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "f3119aff-62bc-4ffc-abe9-835dea105d76",
      "name": "Bucle sobre modelos Ollama",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
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      "parameters": {
        "options": {}
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      "typeVersion": 3
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    {
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      "name": "Crear objetos de resultado",
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      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "780086e5-2733-435a-90b5-fd10946ddd7a",
              "name": "result",
              "type": "object",
              "value": "={{ $json }}"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "ac0d3ada-8890-4945-aedb-fd6be4ffc020",
      "name": "Prompt de imagen general",
      "type": "n8n-nodes-base.set",
      "position": [
        620,
        1280
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "be9a8e21-9bb6-4588-a77a-61bc2def0648",
              "name": "user_prompt",
              "type": "string",
              "value": "=Analyze this image in exhaustive detail using this structure:\\n\\n1. **Comprehensive Inventory**\\n- List all visible objects with descriptors (size, color, position)\\n- Group related items hierarchically (primary subject → secondary elements → background)\\n- Note object conditions (intact/broken, new/aged)\\n\\n2. **Contextual Analysis**\\n- Identify probable setting/location with supporting evidence\\n- Determine time period/season through visual cues\\n- Analyze lighting conditions and shadow orientation\\n\\n3. **Spatial Relationships**\\n- Map object positions using grid coordinates (front/center/back, left/right)\\n- Describe size comparisons between elements\\n- Note overlapping/occluded objects\\n\\n4. **Textual Elements**\\n- Extract ALL text with font characteristics\\n- Identify logos/brands with confidence estimates\\n- Translate non-native text with cultural context\\n\\nFormat response in markdown with clear section headers and bullet points."
            }
          ]
        },
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "b7faafca-a179-43b2-8318-29b4659d424f",
      "name": "Prompt de hoja de cálculo inmobiliaria",
      "type": "n8n-nodes-base.set",
      "disabled": true,
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "be9a8e21-9bb6-4588-a77a-61bc2def0648",
              "name": "user_prompt",
              "type": "string",
              "value": "=Analyze this spreadsheet image in exhaustive detail using this structure:\\n\\n1. **Table Structure**\\n- Identify all column headers (months) in order\\n- List all row labels exactly as shown\\n- Note any table titles, footnotes, or metadata\\n\\n2. **Data Extraction**\\n- Extract all numeric values with precise formatting (decimals, currency symbols)\\n- Maintain exact numbers for Listings, Sales, Months of Inventory\\n- Preserve currency formatting for Avg. Price values\\n- Include DOM values from separate section\\n\\n3. **Markdown Representation**\\n- Convert the entire spreadsheet into a perfectly formatted markdown table\\n- Maintain alignment of all columns and rows\\n- Preserve all relationships between data points\\n\\n4. **Data Analysis**\\n- Identify trends across months for each metric\\n- Note highest and lowest values in each category\\n- Calculate percentage changes between months where relevant\\n\\nFormat response with a complete markdown table first, followed by brief analysis of the real estate market data shown."
            }
          ]
        },
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "5c28053b-a44e-494b-ad03-27d5b217f6b3",
      "name": "Lista de modelos visuales",
      "type": "n8n-nodes-base.set",
      "position": [
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      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
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              "name": "models",
              "type": "array",
              "value": "=[\"granite3.2-vision\",\"llama3.2-vision\",\"gemma3:27b\"]"
            }
          ]
        },
        "includeOtherFields": true
      },
      "typeVersion": 3.4
    },
    {
      "id": "58c2fcd2-0ac4-4684-a30f-37650cc8dac1",
      "name": "Obtener cadena Base64",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "operation": "binaryToPropery"
      },
      "typeVersion": 1
    },
    {
      "id": "b60c0589-397c-445b-a084-a791bef95b15",
      "name": "Descargar archivo de imagen desde Google Drive",
      "type": "n8n-nodes-base.googleDrive",
      "position": [
        920,
        740
      ],
      "parameters": {
        "fileId": {
          "__rl": true,
          "mode": "id",
          "value": "={{ $json.id }}"
        },
        "options": {},
        "operation": "download"
      },
      "credentials": {
        "googleDriveOAuth2Api": {
          "id": "UhdXGYLTAJbsa0xX",
          "name": "Google Drive account"
        }
      },
      "typeVersion": 3
    },
    {
      "id": "55a8f511-fdb5-4830-837a-104cbf6c6167",
      "name": "Dividir lista de modelos visuales para bucle",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        1640,
        740
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "models"
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      "typeVersion": 1
    },
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      "name": "Nota adhesiva",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        620,
        640
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 300,
        "content": "## ⬇️Download Image from Google Drive"
      },
      "typeVersion": 1
    },
    {
      "id": "6ed8925d-b031-4052-9009-91e2e7d8f360",
      "name": "Nota adhesiva 3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1360,
        640
      ],
      "parameters": {
        "color": 7,
        "width": 460,
        "height": 300,
        "content": "## 📜Create List of Local Ollama Vision Models"
      },
      "typeVersion": 1
    },
    {
      "id": "ae383e4f-21e6-479f-97e0-029f43dacc56",
      "name": "Nota adhesiva 4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        280,
        980
      ],
      "parameters": {
        "color": 7,
        "width": 1200,
        "height": 720,
        "content": "## 🦙👁️👁️ Process Image with Ollama Vision Models and Save Results to Google Drive"
      },
      "typeVersion": 1
    },
    {
      "id": "a27bcb6e-c6e8-4777-9887-428363256b4a",
      "name": "ID de imagen de documento Google",
      "type": "n8n-nodes-base.set",
      "position": [
        700,
        740
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "7d5a0385-4d8b-4f70-b3b0-4182bda29e5c",
              "name": "id",
              "type": "string",
              "value": "=[your-google-id]"
            }
          ]
        }
      },
      "typeVersion": 3.4
    },
    {
      "id": "8e6114f8-c724-40fd-9be3-253e3cb882fa",
      "name": "Guardar descripciones de imagen en Google Docs",
      "type": "n8n-nodes-base.googleDocs",
      "position": [
        840,
        1080
      ],
      "parameters": {
        "actionsUi": {
          "actionFields": [
            {
              "text": "=<{{ $json.result.model }}>\n{{ $json.result.message.content }}\n</{{ $json.result.model }}>\n\n",
              "action": "insert"
            }
          ]
        },
        "operation": "update",
        "documentURL": "[your-google-doc-id]"
      },
      "credentials": {
        "googleDocsOAuth2Api": {
          "id": "YWEHuG28zOt532MQ",
          "name": "Google Docs account"
        }
      },
      "typeVersion": 2
    },
    {
      "id": "abed9af8-0d50-413a-9e6d-c6100ddaf015",
      "name": "Nota adhesiva 5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -240,
        640
      ],
      "parameters": {
        "width": 480,
        "height": 1340,
        "content": "## 🦙👁️👁️ Find the Best Local Ollama Vision Models for Your Use Case\n\nProcess images using locally hosted Ollama Vision Models to extract detailed descriptions, contextual insights, and structured data. Save results directly to Google Docs for efficient collaboration.\n\n### Who is this for?\nThis workflow is ideal for developers, data analysts, and AI enthusiasts who need to process and analyze images using locally hosted Ollama Vision Language Models. It’s particularly useful for tasks requiring detailed image descriptions, contextual analysis, and structured data extraction.\n\n### What problem is this workflow solving? / Use Case\nThe workflow solves the challenge of extracting meaningful insights from images in exhaustive detail, such as identifying objects, analyzing spatial relationships, extracting textual elements, and providing contextual information. This is especially helpful for applications in real estate, marketing, engineering, and research.\n\n### What this workflow does\nThis workflow:\n1. Downloads an image file from Google Drive.\n2. Processes the image using multiple Ollama Vision Models (e.g., Granite3.2-Vision, Llama3.2-Vision).\n3. Generates detailed markdown-based descriptions of the image.\n4. Saves the output to a Google Docs file for easy sharing and further analysis.\n\n### Setup\n1. Ensure you have access to a local instance of Ollama.  https://ollama.com/\n2. Pull the Ollama vision models.\n3. Configure your Google Drive and Google Docs credentials in n8n.\n4. Provide the image file ID from Google Drive in the designated node.\n5. Update the list of Ollama vision models\n6. Test the workflow by clicking ‘Test Workflow’ to trigger the process.\n\n### How to customize this workflow to your needs\n- Replace the image source with another provider if needed (e.g., AWS S3 or Dropbox).\n- Modify the prompts in the \"General Image Prompt\" node to suit specific analysis requirements.\n- Add additional nodes for post-processing or integrating results into other platforms like Slack or HubSpot.\n\n## Key Features:\n- **Detailed Image Analysis**: Extracts comprehensive details about objects, spatial relationships, text elements, and contextual settings.\n- **Multi-Model Support**: Utilizes multiple vision models dynamically for optimal performance.\n- **Markdown Output**: Formats results in markdown for easy readability and documentation.\n- **Google Drive Integration**: Seamlessly downloads images and saves results to Google Docs.\n\n\n"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "a337e019-1c9a-4736-8dcd-4f12a9d989f4",
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}
Preguntas frecuentes

¿Cómo usar este flujo de trabajo?

Copie el código de configuración JSON de arriba, cree un nuevo flujo de trabajo en su instancia de n8n y seleccione "Importar desde JSON", pegue la configuración y luego modifique la configuración de credenciales según sea necesario.

¿En qué escenarios es adecuado este flujo de trabajo?

Avanzado - Inteligencia Artificial

¿Es de pago?

Este flujo de trabajo es completamente gratuito, puede importarlo y usarlo directamente. Sin embargo, tenga en cuenta que los servicios de terceros utilizados en el flujo de trabajo (como la API de OpenAI) pueden requerir un pago por su cuenta.

Información del flujo de trabajo
Nivel de dificultad
Avanzado
Número de nodos19
Categoría1
Tipos de nodos9
Descripción de la dificultad

Adecuado para usuarios avanzados, flujos de trabajo complejos con 16+ nodos

Autor
Joseph LePage

Joseph LePage

@joe

As an AI Automation consultant based in Canada, I partner with forward-thinking organizations to implement AI solutions that streamline operations and drive growth.

Enlaces externos
Ver en n8n.io

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Categorías

Categorías: 34