法律案例研究提取器，使用Bright Data MCP和Google Gemini的数据挖掘器
高级
这是一个AI, IT Ops领域的自动化工作流，包含 22 个节点。主要使用 Set, Code, Wait, Function, McpClient 等节点，结合人工智能技术实现智能自动化。 法律案例研究提取器，使用Bright Data MCP和Google Gemini的数据挖掘器
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 (22)
工作流预览
可视化展示节点连接关系，支持缩放和平移
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": "Qgx75aQeRKXKtqm7",
"meta": {
"instanceId": "885b4fb4a6a9c2cb5621429a7b972df0d05bb724c20ac7dac7171b62f1c7ef40",
"templateCredsSetupCompleted": true
},
"name": "法律案例研究提取器，使用 Bright Data MCP 和 Google Gemini 的数据挖掘器",
"tags": [
{
"id": "ZOwtAMLepQaGW76t",
"name": "Building Blocks",
"createdAt": "2025-04-13T15:23:40.462Z",
"updatedAt": "2025-04-13T15:23:40.462Z"
},
{
"id": "ddPkw7Hg5dZhQu2w",
"name": "AI",
"createdAt": "2025-04-13T05:38:08.053Z",
"updatedAt": "2025-04-13T05:38:08.053Z"
}
],
"nodes": [
{
"id": "9e9a27ce-b95c-4ecd-b3c4-97aba420ce45",
"name": "点击“测试工作流”时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-520,
140
],
"parameters": {},
"typeVersion": 1
},
{
"id": "3f9e30b5-7eb3-454d-a831-07be51f7a326",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
40
],
"parameters": {
"color": 4,
"width": 440,
"height": 320,
"content": "## Bright Data 法律案例研究爬虫"
},
"typeVersion": 1
},
{
"id": "8f1934bf-ccec-4b25-b6cc-7607dcbdf798",
"name": "列出 Bright Data 的所有工具",
"type": "n8n-nodes-mcp.mcpClient",
"position": [
-300,
140
],
"parameters": {},
"credentials": {
"mcpClientApi": {
"id": "JtatFSfA2kkwctYa",
"name": "MCP Client (STDIO) account"
}
},
"typeVersion": 1
},
{
"id": "f8c4804a-85ad-462c-913d-e0bc5242bc74",
"name": "为 LinkedIn 公司信息提取创建二进制数据",
"type": "n8n-nodes-base.function",
"position": [
2440,
60
],
"parameters": {
"functionCode": "items[0].binary = {\n data: {\n data: new Buffer(JSON.stringify(items[0].json, null, 2)).toString('base64')\n }\n};\nreturn items;"
},
"typeVersion": 1
},
{
"id": "c616db9f-fcf3-4f9d-b60f-a16c9da89456",
"name": "便签 2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1220,
-180
],
"parameters": {
"width": 440,
"height": 120,
"content": "## 免责声明"
},
"typeVersion": 1
},
{
"id": "048c1093-ea88-441c-98fa-a2d003ab6b8d",
"name": "设置法律案例研究 URL",
"type": "n8n-nodes-base.set",
"position": [
-20,
140
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "214e61a0-3587-453f-baf5-eac013990857",
"name": "url",
"type": "string",
"value": "https://www.courtlistener.com/?q=IT%20laws%20for%20cyber%20crime&type=o&order_by=dateFiled%20desc&stat_Published=on"
},
{
"id": "45014942-0a2e-4f46-b395-f82f97bfa93e",
"name": "webhook_url",
"type": "string",
"value": "https://webhook.site/7b5380a0-0544-48dc-be43-0116cb2d52c2"
},
{
"id": "bf011e1f-7032-49db-8f25-31ec4c35b9c5",
"name": "base_url",
"type": "string",
"value": "https://www.courtlistener.com"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "8dc0a8cd-e4d9-4252-9dd2-94ee95d698e9",
"name": "用于法律案例研究的 Bright Data MCP 客户端",
"type": "n8n-nodes-mcp.mcpClient",
"notes": "Scrape a single webpage URL with advanced options for content extraction and get back the results in MarkDown language.",
"position": [
200,
140
],
"parameters": {
"toolName": "scrape_as_html",
"operation": "executeTool",
"toolParameters": "={\n \"url\": \"{{ $json.url }}\"\n} "
},
"credentials": {
"mcpClientApi": {
"id": "JtatFSfA2kkwctYa",
"name": "MCP Client (STDIO) account"
}
},
"notesInFlow": true,
"typeVersion": 1
},
{
"id": "f3ea0d19-703b-4f99-955c-122162065363",
"name": "案例提取器",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
600,
140
],
"parameters": {
"text": "=Extract the content in a structured format. Here's the content : {{ $json.result.content[0].text }}",
"messages": {
"messageValues": [
{
"message": "You are an expert structured data extractor"
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"retryOnFail": true,
"typeVersion": 1.6
},
{
"id": "a3fe5ce7-3a91-459d-8ef8-17a06fbef12a",
"name": "结构化输出解析器",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
800,
360
],
"parameters": {
"jsonSchemaExample": "[{\n\"Id\": \"\",\n\"Link\" : \"\",\n\"Title\": \n\"United States v. IXCOLGONZALEZ\"\n}]"
},
"typeVersion": 1.2
},
{
"id": "5fd6d0a3-46ca-4184-bc5c-dfc0966f0538",
"name": "遍历项目",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1320,
140
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "74a02ac0-859d-4611-aeb0-021a654c92b8",
"name": "循环内用于法律案例研究的 Bright Data MCP 客户端",
"type": "n8n-nodes-mcp.mcpClient",
"notes": "Scrape a single webpage URL with advanced options for content extraction and get back the results in MarkDown language.",
"position": [
1860,
160
],
"parameters": {
"toolName": "scrape_as_html",
"operation": "executeTool",
"toolParameters": "={\n \"url\": \"{{ $('Set the Legal Case Research URL').item.json.base_url }}/{{ $json.Link }}\"\n} "
},
"credentials": {
"mcpClientApi": {
"id": "JtatFSfA2kkwctYa",
"name": "MCP Client (STDIO) account"
}
},
"notesInFlow": true,
"typeVersion": 1
},
{
"id": "beb67c30-dd39-4c7d-94f8-853410dec09b",
"name": "循环内 HTML 到文本数据提取",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2080,
160
],
"parameters": {
"text": "=Extract html to textual content {{ $json.result.content[0].text }}",
"promptType": "define"
},
"retryOnFail": true,
"typeVersion": 1.6
},
{
"id": "b7fc74e5-4165-4b1a-9c0a-27565302c0e1",
"name": "循环内 HTML 到文本数据提取的 Webhook 通知",
"type": "n8n-nodes-base.httpRequest",
"position": [
2440,
260
],
"parameters": {
"url": "={{ $('Set the Legal Case Research URL').item.json.webhook_url }}",
"options": {},
"sendBody": true,
"contentType": "multipart-form-data",
"bodyParameters": {
"parameters": [
{
"name": "case_content",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "07b78de1-fdc8-4233-a231-37258fa5d1f0",
"name": "将案例内容写入磁盘",
"type": "n8n-nodes-base.readWriteFile",
"position": [
2700,
60
],
"parameters": {
"options": {},
"fileName": "=d:\\Case-{{ $('Loop Over Items').item.json['Id'] }}.json",
"operation": "write"
},
"typeVersion": 1
},
{
"id": "ff687082-9e3d-4043-9aa6-29e3029499d4",
"name": "用于案例数据提取的 Google Gemini 聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
580,
360
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "0057d772-732e-4e47-8ab8-eebe140df692",
"name": "输出案例集合的代码",
"type": "n8n-nodes-base.code",
"position": [
980,
140
],
"parameters": {
"jsCode": "\nreturn $input.first().json.output"
},
"typeVersion": 2
},
{
"id": "c843170b-e360-4eea-853c-ef38c9f3affe",
"name": "循环内用于 HTML 到文本数据提取的 Google Gemini 聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
2100,
360
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"id": "YeO7dHZnuGBVQKVZ",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "90f4670a-1fca-4826-9017-64a31f29cbc2",
"name": "便签 1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1220,
0
],
"parameters": {
"color": 5,
"width": 1660,
"height": 520,
"content": "## Bright Data 法律案例研究爬虫"
},
"typeVersion": 1
},
{
"id": "58aac68b-2598-465b-ab3c-f5c0ebcdb595",
"name": "便签 4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
-260
],
"parameters": {
"color": 5,
"width": 440,
"height": 220,
"content": "## LLM 使用情况"
},
"typeVersion": 1
},
{
"id": "14bbbc73-06cd-4513-b9e6-2aebb5009c3d",
"name": "便签 5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-580,
-860
],
"parameters": {
"color": 7,
"width": 400,
"height": 400,
"content": "## 徽标"
},
"typeVersion": 1
},
{
"id": "96d74c50-074e-4b83-9422-ff2ce56bd55d",
"name": "便签 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-580,
-360
],
"parameters": {
"width": 400,
"height": 320,
"content": "## 注意"
},
"typeVersion": 1
},
{
"id": "08c6a217-5773-4ebc-ba6e-326de99e90e5",
"name": "等待",
"type": "n8n-nodes-base.wait",
"position": [
1580,
160
],
"webhookId": "65c9fcd3-2c82-4bdd-80b6-271d65b7f61a",
"parameters": {
"amount": 10
},
"typeVersion": 1.1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "03af01f8-7276-4c3c-a610-6532f0d51ef7",
"connections": {
"Wait": {
"main": [
[
{
"node": "Bright Data MCP Client For Legal Case Research Within Loop",
"type": "main",
"index": 0
}
]
]
},
"Case Extractor": {
"main": [
[
{
"node": "Code to output the collection of cases",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Wait",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "Case Extractor",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"List all tools for Bright Data": {
"main": [
[
{
"node": "Set the Legal Case Research URL",
"type": "main",
"index": 0
}
]
]
},
"Write the case content to disk": {
"main": [
[]
]
},
"Set the Legal Case Research URL": {
"main": [
[
{
"node": "Bright Data MCP Client For Legal Case Research",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Test workflow’": {
"main": [
[
{
"node": "List all tools for Bright Data",
"type": "main",
"index": 0
}
]
]
},
"Code to output the collection of cases": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"HTML to Textual Data Extract Within Loop": {
"main": [
[
{
"node": "Create a binary data for LinkedIn company info extract",
"type": "main",
"index": 0
},
{
"node": "Webhook Notification for HTML to Textual Data Extract Within the Loop",
"type": "main",
"index": 0
}
]
]
},
"Bright Data MCP Client For Legal Case Research": {
"main": [
[
{
"node": "Case Extractor",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model For Case Data Extract": {
"ai_languageModel": [
[
{
"node": "Case Extractor",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Create a binary data for LinkedIn company info extract": {
"main": [
[
{
"node": "Write the case content to disk",
"type": "main",
"index": 0
}
]
]
},
"Bright Data MCP Client For Legal Case Research Within Loop": {
"main": [
[
{
"node": "HTML to Textual Data Extract Within Loop",
"type": "main",
"index": 0
}
]
]
},
"Webhook Notification for HTML to Textual Data Extract Within the Loop": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model for HTML to Textual Data Extract Within the Loop": {
"ai_languageModel": [
[
{
"node": "HTML to Textual Data Extract Within Loop",
"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？
高级 - 人工智能, IT 运维
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 MCP 和 OpenAI 的食谱推荐引擎
基于 Bright Data MCP 和 OpenAI 4o mini 的食谱推荐引擎
Set
Code
Wait
+10
23 节点Ranjan Dailata
其他
使用Bright Data和Google Gemini的Google Maps企业抓取和线索丰富
使用Bright Data和Google Gemini的Google Maps企业抓取和线索丰富工具
Set
Code
Wait
+11
29 节点Ranjan Dailata
潜在客户开发
使用Bright Data MCP服务器和Google Gemini进行LinkedIn网页抓取
使用Bright Data MCP服务器和Google Gemini提取和转换LinkedIn数据
Set
Code
Merge
+9
20 节点Ranjan Dailata
人工智能
使用Bright Data和OpenAI 4o mini的自动化简历职位匹配引擎
Bright Data MCP与OpenAI 4o mini的自动化简历职位匹配引擎
Set
Function
Split Out
+10
22 节点Ranjan Dailata
人力资源
AI代理驱动的ProductHunt数据提取和搜索（使用Bright Data和Google Gemini）
使用Bright Data MCP和Google Gemini AI提取和搜索ProductHunt数据
Set
Function
Mcp Client
+10
21 节点Ranjan Dailata
人工智能
Brave搜索结构化数据提取（Bright Data MCP + Google Gemini）
使用Bright Data MCP和Google Gemini从Brave搜索中提取结构化数据
Set
Switch
Function
+9
24 节点Ranjan Dailata
人工智能
工作流信息
难度等级
高级
节点数量22
分类2
节点类型13
作者
Ranjan Dailata
@ranjancseA Professional based out of India specialized in handling AI-powered automations. Contact me at ranjancse@gmail.com
外部链接
在 n8n.io 查看 →
分享此工作流