8
n8n 中文网amn8n.com

🦙👁️👁️ 通过比较找到最佳本地 Ollama 视觉模型

高级

这是一个AI领域的自动化工作流，包含 19 个节点。主要使用 Set, SplitOut, GoogleDocs, GoogleDrive, HttpRequest 等节点，结合人工智能技术实现智能自动化。 使用 Google Docs 比较本地 Ollama 视觉模型进行图像分析

前置要求
  • •Google Drive API 凭证
  • •可能需要目标 API 的认证凭证

使用的节点 (19)

Set
SplitOut
GoogleDocs
StickyNote
GoogleDrive
HttpRequest
ManualTrigger
SplitInBatches
ExtractFromFile

分类

人工智能
工作流预览
可视化展示节点连接关系，支持缩放和平移
React Flow
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
  "id": "keFEBUqHOrsib60G",
  "meta": {
    "instanceId": "31e69f7f4a77bf465b805824e303232f0227212ae922d12133a0f96ffeab4fef",
    "templateCredsSetupCompleted": true
  },
  "name": "🦙👁️👁️ 通过比较找到最佳本地 Ollama 视觉模型",
  "tags": [],
  "nodes": [
    {
      "id": "dd2f1201-a78a-4ea9-b5ff-7543673e8445",
      "name": "便签1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1080,
        1160
      ],
      "parameters": {
        "color": 4,
        "width": 340,
        "height": 340,
        "content": "## 👁️ 使用本地 Ollama LLM 分析图像"
      },
      "typeVersion": 1
    },
    {
      "id": "81975be4-1e40-41e9-b938-612270f80a92",
      "name": "便签2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        280,
        640
      ],
      "parameters": {
        "color": 4,
        "width": 300,
        "height": 300,
        "content": "## 👍试试看！"
      },
      "typeVersion": 1
    },
    {
      "id": "3a56f75b-4836-4c37-a246-83ef6507c581",
      "name": "当点击\"测试工作流\"时",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        380,
        740
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "bb4570c7-269c-4d28-85d4-183ca2fabb89",
      "name": "Ollama LLM 请求",
      "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"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "0a6e064d-9a67-4cd2-b3ed-247a1684c1fb",
      "name": "创建请求体",
      "type": "n8n-nodes-base.set",
      "position": [
        840,
        1280
      ],
      "parameters": {
        "options": {},
        "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": "循环遍历 Ollama 模型",
      "type": "n8n-nodes-base.splitInBatches",
      "position": [
        360,
        1180
      ],
      "parameters": {
        "options": {}
      },
      "typeVersion": 3
    },
    {
      "id": "1e26f493-881e-40a3-922d-5c8d6cb86374",
      "name": "创建结果对象",
      "type": "n8n-nodes-base.set",
      "position": [
        620,
        1080
      ],
      "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": "通用图像提示",
      "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": "房地产电子表格提示",
      "type": "n8n-nodes-base.set",
      "disabled": true,
      "position": [
        620,
        1480
      ],
      "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": "视觉模型列表",
      "type": "n8n-nodes-base.set",
      "position": [
        1440,
        740
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "86add667-cd96-4e1c-877a-c437f6b1e040",
              "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": "获取 Base64 字符串",
      "type": "n8n-nodes-base.extractFromFile",
      "position": [
        1140,
        740
      ],
      "parameters": {
        "options": {},
        "operation": "binaryToPropery"
      },
      "typeVersion": 1
    },
    {
      "id": "b60c0589-397c-445b-a084-a791bef95b15",
      "name": "从 Google Drive 下载图像文件",
      "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": "分割视觉模型列表以进行循环",
      "type": "n8n-nodes-base.splitOut",
      "position": [
        1640,
        740
      ],
      "parameters": {
        "options": {},
        "fieldToSplitOut": "models"
      },
      "typeVersion": 1
    },
    {
      "id": "8e48c8bd-15c9-4389-8698-77dc5ae698bc",
      "name": "便签",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        620,
        640
      ],
      "parameters": {
        "color": 7,
        "width": 700,
        "height": 300,
        "content": "## ⬇️从 Google Drive 下载图像"
      },
      "typeVersion": 1
    },
    {
      "id": "6ed8925d-b031-4052-9009-91e2e7d8f360",
      "name": "便签3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1360,
        640
      ],
      "parameters": {
        "color": 7,
        "width": 460,
        "height": 300,
        "content": "## 📜创建本地 Ollama 视觉模型列表"
      },
      "typeVersion": 1
    },
    {
      "id": "ae383e4f-21e6-479f-97e0-029f43dacc56",
      "name": "便签4",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        280,
        980
      ],
      "parameters": {
        "color": 7,
        "width": 1200,
        "height": 720,
        "content": "## 🦙👁️👁️ 使用 Ollama 视觉模型处理图像并将结果保存到 Google Drive"
      },
      "typeVersion": 1
    },
    {
      "id": "a27bcb6e-c6e8-4777-9887-428363256b4a",
      "name": "Google 文档图像 ID",
      "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": "将图像描述保存到 Google 文档",
      "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": "便签5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -240,
        640
      ],
      "parameters": {
        "width": 480,
        "height": 1340,
        "content": "## 🦙👁️👁️ 为您的用例找到最佳本地 Ollama 视觉模型"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "a337e019-1c9a-4736-8dcd-4f12a9d989f4",
  "connections": {
    "Get Base64 String": {
      "main": [
        [
          {
            "node": "List of Vision Models",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Ollama LLM Request": {
      "main": [
        [
          {
            "node": "Loop Over Ollama Models",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Create Request Body": {
      "main": [
        [
          {
            "node": "Ollama LLM Request",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Google Doc Image Id": {
      "main": [
        [
          {
            "node": "Download Image File from Google Drive",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "General Image Prompt": {
      "main": [
        [
          {
            "node": "Create Request Body",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Create Result Objects": {
      "main": [
        [
          {
            "node": "Save Image Descriptions to Google Docs",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "List of Vision Models": {
      "main": [
        [
          {
            "node": "Split List of Vision Models for Looping",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Loop Over Ollama Models": {
      "main": [
        [
          {
            "node": "Create Result Objects",
            "type": "main",
            "index": 0
          }
        ],
        [
          {
            "node": "General Image Prompt",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Real Estate Spreadsheet Prompt": {
      "main": [
        []
      ]
    },
    "When clicking ‘Test workflow’": {
      "main": [
        [
          {
            "node": "Google Doc Image Id",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Download Image File from Google Drive": {
      "main": [
        [
          {
            "node": "Get Base64 String",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Split List of Vision Models for Looping": {
      "main": [
        [
          {
            "node": "Loop Over Ollama Models",
            "type": "main",
            "index": 0
          }
        ]
      ]
    }
  }
}
常见问题

如何使用这个工作流？

复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。

这个工作流适合什么场景？

高级 - 人工智能

需要付费吗？

本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。

相关工作流推荐

💡🌐 基于Jina.ai的多页面网站爬虫
💡🌐 使用Jina.ai的多页面网站爬虫工具
Set
Xml
Code
+9
16 节点Joseph LePage
人工智能
🤖 面向您的文档+Google Drive+Gemini+Qdrant的AI驱动RAG聊天机器人
🤖 面向您的文档+Google Drive+Gemini+Qdrant的AI驱动RAG聊天机器人
If
Set
Wait
+21
50 节点Joseph LePage
人工智能
🎦🚀 YouTube 视频评论分析智能体
🎦🚀 YouTube 视频评论分析智能体
Set
Code
Gmail
+12
25 节点Joseph LePage
人工智能
（Duc）深度研究市场模板
集成PerplexityAI研究和OpenAI内容的多层级WordPress博客生成器
If
Set
Xml
+28
132 节点Daniel Ng
人工智能
WordPress博客自动化专业版（SEO主题）v2
WordPress自动博客专业版 - SEO主题内容自动化机器
If
Set
Xml
+21
63 节点Daniel Ng
人工智能
WordPress博客自动化专业版（深度研究）v1
WordPress自动博客专业版 - 含深度研究的内容自动化机器
If
Set
Xml
+24
77 节点Daniel Ng
人工智能
工作流信息
难度等级
高级
节点数量19
分类1
节点类型9
难度说明

适合高级用户，包含 16+ 个节点的复杂工作流

作者
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.

外部链接
在 n8n.io 查看 →

分享此工作流