8
n8n 中文网amn8n.com

使用Bright Data和Gemini AI提取并总结Wikipedia数据

中级

这是一个Other, AI领域的自动化工作流，包含 12 个节点。主要使用 Set, HttpRequest, ManualTrigger, ChainLlm, ChainSummarization 等节点，结合人工智能技术实现智能自动化。 使用Bright Data和Gemini AI提取和总结Wikipedia数据

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

使用的节点 (12)

Set
StickyNote
HttpRequest
ManualTrigger
ChainLlm
ChainSummarization
LmChatGoogleGemini

分类

其他
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": "sczRNO4u1HYc5YV7",
  "meta": {
    "instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
    "templateCredsSetupCompleted": true
  },
  "name": "使用 Bright Data 和 Gemini AI 提取并总结 Wikipedia 数据",
  "tags": [
    {
      "id": "Kujft2FOjmOVQAmJ",
      "name": "Engineering",
      "createdAt": "2025-04-09T01:31:00.558Z",
      "updatedAt": "2025-04-09T01:31:00.558Z"
    },
    {
      "id": "ddPkw7Hg5dZhQu2w",
      "name": "AI",
      "createdAt": "2025-04-13T05:38:08.053Z",
      "updatedAt": "2025-04-13T05:38:08.053Z"
    }
  ],
  "nodes": [
    {
      "id": "0f4b4939-6356-4672-ae61-8d1daf66a168",
      "name": "当点击\"测试工作流\"时",
      "type": "n8n-nodes-base.manualTrigger",
      "position": [
        340,
        -440
      ],
      "parameters": {},
      "typeVersion": 1
    },
    {
      "id": "167e060a-c36c-462a-826c-81ef379c824b",
      "name": "用于摘要的 Google Gemini 聊天模型",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1520,
        -60
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-flash-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "YeO7dHZnuGBVQKVZ",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "a51f2634-8b59-4feb-be39-674e8f198714",
      "name": "Google Gemini 聊天模型2",
      "type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
      "position": [
        1000,
        -240
      ],
      "parameters": {
        "options": {},
        "modelName": "models/gemini-2.0-pro-exp"
      },
      "credentials": {
        "googlePalmApi": {
          "id": "YeO7dHZnuGBVQKVZ",
          "name": "Google Gemini(PaLM) Api account"
        }
      },
      "typeVersion": 1
    },
    {
      "id": "a1ec001f-6e97-4efb-91d9-9a037fbf472c",
      "name": "摘要 Webhook 通知器",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        1860,
        -280
      ],
      "parameters": {
        "url": "https://webhook.site/ce41e056-c097-48c8-a096-9b876d3abbf7",
        "options": {},
        "sendBody": true,
        "bodyParameters": {
          "parameters": [
            {
              "name": "summary",
              "value": "={{ $json.response.text }}"
            }
          ]
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "f4dd93b5-2a33-4ac7-a0c9-9e0956bea363",
      "name": "便签",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        340,
        -820
      ],
      "parameters": {
        "width": 400,
        "height": 300,
        "content": "## 注意"
      },
      "typeVersion": 1
    },
    {
      "id": "9bd6f913-c526-4e54-81f8-8885a0fe974f",
      "name": "便签1",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        780,
        -820
      ],
      "parameters": {
        "width": 500,
        "height": 300,
        "content": "## LLM 使用情况"
      },
      "typeVersion": 1
    },
    {
      "id": "30008ce4-4de2-43c5-bb03-94db58262f86",
      "name": "Wikipedia 网络请求",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
        780,
        -440
      ],
      "parameters": {
        "url": "https://api.brightdata.com/request",
        "method": "POST",
        "options": {},
        "sendBody": true,
        "sendHeaders": true,
        "authentication": "genericCredentialType",
        "bodyParameters": {
          "parameters": [
            {
              "name": "zone",
              "value": "={{ $json.zone }}"
            },
            {
              "name": "url",
              "value": "={{ $json.url }}"
            },
            {
              "name": "format",
              "value": "raw"
            }
          ]
        },
        "genericAuthType": "httpHeaderAuth",
        "headerParameters": {
          "parameters": [
            {}
          ]
        }
      },
      "credentials": {
        "httpHeaderAuth": {
          "id": "kdbqXuxIR8qIxF7y",
          "name": "Header Auth account"
        }
      },
      "typeVersion": 4.2
    },
    {
      "id": "28656a7d-4bd8-41c8-8471-50d19d88e7f2",
      "name": "LLM 数据提取器",
      "type": "@n8n/n8n-nodes-langchain.chainLlm",
      "position": [
        1000,
        -440
      ],
      "parameters": {
        "text": "={{ $json.data }}",
        "messages": {
          "messageValues": [
            {
              "message": "You are an expert Data Formatter. Make sure to format the data in a human readable manner. Please output the human readable content without your own thoughts"
            }
          ]
        },
        "promptType": "define",
        "hasOutputParser": true
      },
      "typeVersion": 1.6
    },
    {
      "id": "7045af3b-9e74-42ef-92f0-f8d3266f2890",
      "name": "简洁摘要生成器",
      "type": "@n8n/n8n-nodes-langchain.chainSummarization",
      "position": [
        1440,
        -280
      ],
      "parameters": {
        "options": {
          "summarizationMethodAndPrompts": {
            "values": {
              "prompt": "Write a concise summary of the following:\n\n\n\"{text}\"\n"
            }
          }
        },
        "chunkingMode": "advanced"
      },
      "typeVersion": 2
    },
    {
      "id": "0cc843c1-252a-4c18-9856-5c7dfc732072",
      "name": "使用 Bright Data Zone 设置 Wikipedia URL",
      "type": "n8n-nodes-base.set",
      "notes": "Set the URL which you are interested to scrap the data",
      "position": [
        560,
        -440
      ],
      "parameters": {
        "options": {},
        "assignments": {
          "assignments": [
            {
              "id": "1c132dd6-31e4-453b-a8cf-cad9845fe55b",
              "name": "url",
              "type": "string",
              "value": "https://en.wikipedia.org/wiki/Cloud_computing?product=unlocker&method=api"
            },
            {
              "id": "0fa387df-2511-4228-b6aa-237cceb3e9c7",
              "name": "zone",
              "type": "string",
              "value": "web_unlocker1"
            }
          ]
        }
      },
      "notesInFlow": true,
      "typeVersion": 3.4
    },
    {
      "id": "6cb9930f-1924-4762-8150-f5cd0e063348",
      "name": "便签2",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        940,
        -500
      ],
      "parameters": {
        "color": 4,
        "width": 380,
        "height": 420,
        "content": "## 基础 LLM 链数据提取器"
      },
      "typeVersion": 1
    },
    {
      "id": "47811535-bce5-4946-aaa6-baef87db1100",
      "name": "便签3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
        1400,
        -340
      ],
      "parameters": {
        "color": 5,
        "width": 340,
        "height": 420,
        "content": "## 摘要链"
      },
      "typeVersion": 1
    }
  ],
  "active": false,
  "pinData": {},
  "settings": {
    "executionOrder": "v1"
  },
  "versionId": "5b5e78fb-6e5a-4b92-838c-6c4060618e9c",
  "connections": {
    "LLM Data Extractor": {
      "main": [
        [
          {
            "node": "Concise Summary Generator",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Wikipedia Web Request": {
      "main": [
        [
          {
            "node": "LLM Data Extractor",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Concise Summary Generator": {
      "main": [
        [
          {
            "node": "Summary Webhook Notifier",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model2": {
      "ai_languageModel": [
        [
          {
            "node": "LLM Data Extractor",
            "type": "ai_languageModel",
            "index": 0
          }
        ]
      ]
    },
    "When clicking ‘Test workflow’": {
      "main": [
        [
          {
            "node": "Set Wikipedia URL with Bright Data Zone",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Set Wikipedia URL with Bright Data Zone": {
      "main": [
        [
          {
            "node": "Wikipedia Web Request",
            "type": "main",
            "index": 0
          }
        ]
      ]
    },
    "Google Gemini Chat Model For Summarization": {
      "ai_languageModel": [
        [
          {
            "node": "Concise Summary Generator",
            "type": "ai_languageModel",
            "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？

中级 - 其他, 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

使用Bright Data进行品牌内容提取、摘要与情感分析
使用Bright Data和Google Gemini提取和分析品牌内容
Set
Function
Http Request
+7
23 节点Ranjan Dailata
人工智能
使用Gemini AI和Bright Data提取和总结必应Copilot搜索结果
使用Gemini AI和Bright Data提取和总结必应Copilot搜索结果
If
Set
Wait
+9
19 节点Ranjan Dailata
人工智能
使用Bright Data和Google Gemini提取并汇总Indeed公司信息
使用Bright Data和Google Gemini提取和汇总Indeed公司信息
Set
Markdown
Http Request
+7
15 节点Ranjan Dailata
人力资源
使用Bright Data和Google Gemini提取并总结Yelp商家评论
使用Bright Data和Google Gemini提取和总结Yelp商家评论
Set
Merge
Http Request
+6
12 节点Ranjan Dailata
人工智能
通过Bright Data提取、总结和分析亚马逊产品降价情况
使用Bright Data和Google Gemini提取、总结和分析亚马逊降价信息
Set
Wait
Merge
+14
26 节点Ranjan Dailata
人工智能
Google趋势数据提取，使用Bright Data和Google Gemini进行摘要生成
使用Bright Data和Google Gemini的Google趋势数据提取与摘要生成
Set
Gmail
Function
+8
16 节点Ranjan Dailata
工程
工作流信息
难度等级
中级
节点数量12
分类2
节点类型7
难度说明

适合有一定经验的用户，包含 6-15 个节点的中等复杂度工作流

作者
Ranjan Dailata

Ranjan Dailata

@ranjancse
外部链接
在 n8n.io 查看 →

分享此工作流