视觉RAG与图像嵌入，使用Cohere Command-A和Embed v4
高级
这是一个Document Extraction, AI RAG领域的自动化工作流，包含 38 个节点。主要使用 If, Set, SplitOut, Qdrant, Aggregate 等节点。 视觉RAG与图像嵌入，使用Cohere Command-A和Embed v4
前置要求
- •Qdrant 服务器连接信息
- •可能需要目标 API 的认证凭证
使用的节点 (38)
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
{
"meta": {
"instanceId": "408f9fb9940c3cb18ffdef0e0150fe342d6e655c3a9fac21f0f644e8bedabcd9",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "7874a910-e303-410c-a4a2-7c76aca3af2d",
"name": "点击\"执行工作流\"时",
"type": "n8n-nodes-base.manualTrigger",
"position": [
16,
80
],
"parameters": {},
"typeVersion": 1
},
{
"id": "92a51a45-ae82-4b16-b077-98d3c872c26a",
"name": "技术与创新报告 2025",
"type": "n8n-nodes-base.set",
"position": [
208,
80
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={\n \"url\": [\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172521/n8n-workflows/tir2025_en_19_hgxqxn.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172521/n8n-workflows/tir2025_en_20_avxf85.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172521/n8n-workflows/tir2025_en_21_uathyn.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172521/n8n-workflows/tir2025_en_23_gt5s2p.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172522/n8n-workflows/tir2025_en_24_msru5l.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172522/n8n-workflows/tir2025_en_25_xgrgp8.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172522/n8n-workflows/tir2025_en_26_vmjhpg.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172522/n8n-workflows/tir2025_en_89_naihym.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172522/n8n-workflows/tir2025_en_90_id0nsj.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172527/n8n-workflows/tir2025_en_91_ks3iav.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172527/n8n-workflows/tir2025_en_101_h2miyl.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172527/n8n-workflows/tir2025_en_103_eaimeh.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172527/n8n-workflows/tir2025_en_102_y6oyls.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172528/n8n-workflows/tir2025_en_104_z5fklh.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172530/n8n-workflows/tir2025_en_105_m3b26i.png\",\n \"https://res.cloudinary.com/daglih2g8/image/upload/v1754172531/n8n-workflows/tir2025_en_106_wm6kva.png\"\n ]\n}\n"
},
"typeVersion": 3.4
},
{
"id": "c18e1bb4-0db2-4adf-93f2-1fac1916f245",
"name": "下载页面",
"type": "n8n-nodes-base.httpRequest",
"position": [
1152,
0
],
"parameters": {
"url": "={{ $json.url }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "9748db97-abb5-47dd-969e-e04cd67a1284",
"name": "拆分 URL",
"type": "n8n-nodes-base.splitOut",
"position": [
384,
80
],
"parameters": {
"options": {},
"fieldToSplitOut": "url"
},
"typeVersion": 1
},
{
"id": "60d4d5ce-2e52-4bc1-a15a-337cdc9830d3",
"name": "使用 Cohere Embed 4 进行图像嵌入",
"type": "n8n-nodes-base.httpRequest",
"position": [
1552,
0
],
"parameters": {
"url": "https://api.cohere.com/v2/embed",
"method": "POST",
"options": {},
"jsonBody": "={\n \"model\": \"embed-v4.0\",\n \"input_type\": \"image\",\n \"embedding_types\": [\"float\"],\n \"images\": [\"data:image/png;base64,{{ $json.data }}\"]\n }",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "accept",
"value": "application/json"
},
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"nodeCredentialType": "cohereApi"
},
"credentials": {
"cohereApi": {
"id": "uakbZrf6hh3F6T7h",
"name": "CohereApi account"
}
},
"typeVersion": 4.2
},
{
"id": "8c5f2689-69cd-4e83-9228-a70812a7d6f1",
"name": "准备点",
"type": "n8n-nodes-base.set",
"position": [
1840,
0
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ba2dbe6f-d433-4ed8-9727-e4258017b79a",
"name": "id",
"type": "string",
"value": "={{ $json.id }}"
},
{
"id": "fc97e129-5213-46fd-85aa-b01ccd77d69d",
"name": "url",
"type": "string",
"value": "={{ $('Page Ref').item.json.url }}"
},
{
"id": "e6b1d194-49fb-4b1b-8f8c-046209ed5b4b",
"name": "embedding",
"type": "array",
"value": "={{ $json.embeddings.float[0] }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "2e8f4164-2d54-41d1-9b70-c5331881f15f",
"name": "聚合点",
"type": "n8n-nodes-base.aggregate",
"position": [
2016,
0
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData",
"destinationFieldName": "points"
},
"typeVersion": 1
},
{
"id": "ad731a29-521f-4910-a958-f24214ff6bc6",
"name": "插入点",
"type": "n8n-nodes-qdrant.qdrant",
"position": [
2208,
80
],
"parameters": {
"wait": false,
"points": "={{\n$json.points.map(item => ({\n id: item.id,\n payload: {\n content: item.url,\n metadata: {}\n },\n vector: item.embedding\n})).toJsonString()\n}}\n",
"resource": "point",
"operation": "upsertPoints",
"collectionName": {
"__rl": true,
"mode": "list",
"value": "visionRagExample",
"cachedResultName": "visionRagExample"
},
"requestOptions": {}
},
"credentials": {
"qdrantRestApi": {
"id": "Px8bPm0Qb8kjI7AA",
"name": "localhost"
}
},
"typeVersion": 1
},
{
"id": "50dcf17e-ce40-4005-af14-5439039825ea",
"name": "批次 5",
"type": "n8n-nodes-base.splitInBatches",
"position": [
672,
80
],
"parameters": {
"options": {},
"batchSize": 5
},
"typeVersion": 3
},
{
"id": "57a943a4-6bbd-42c8-9dd2-6835be9a3b89",
"name": "页面引用",
"type": "n8n-nodes-base.noOp",
"position": [
896,
0
],
"parameters": {},
"typeVersion": 1
},
{
"id": "b577a452-fb00-4180-89d1-d70bc2cfcd0a",
"name": "当收到聊天消息时",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
304,
704
],
"webhookId": "85f868f6-12db-48e6-956a-5da8dd9c4e8a",
"parameters": {
"public": true,
"options": {
"responseMode": "responseNodes"
}
},
"typeVersion": 1.3
},
{
"id": "e17b4fd7-afbf-4a66-982a-d5c65a0fc41a",
"name": "AI 代理",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
480,
704
],
"parameters": {
"options": {
"systemMessage": "You are a helpful assistant.\n",
"returnIntermediateSteps": true
}
},
"typeVersion": 2.2
},
{
"id": "82acff19-7cab-4be3-979d-995293068344",
"name": "是否有工具调用？",
"type": "n8n-nodes-base.if",
"position": [
1024,
704
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f133b8e9-85ee-4974-83db-8eae6889f7da",
"operator": {
"type": "object",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json.intermediateSteps.find(step => step.action.tool === \"Technology_Innovation_Report_Tool\") }}",
"rightValue": ""
}
]
}
},
"typeVersion": 2.2
},
{
"id": "8ec020b5-381f-4652-ac39-9b0067267506",
"name": "响应聊天",
"type": "@n8n/n8n-nodes-langchain.chat",
"position": [
2688,
704
],
"parameters": {
"message": "={{ $json.message.content[0].text }}\n\n### Sources\n{{\n[\n $('Aggregate Results').item.json.data.map(_ => ` `).join('|'),\n $('Aggregate Results').item.json.data.map(_ => `-`).join('|'),\n $('Aggregate Results').item.json.data\n .map((data,idx) => `[})](${data.document.pageContent})`)\n .join('|')\n]\n .map(line => `|${line}|`)\n .join('\\n')\n}}",
"options": {
"memoryConnection": true
},
"waitUserReply": false
},
"typeVersion": 1
},
{
"id": "82cc9ac3-df2a-4e4e-b86e-cf3f4c1f2529",
"name": "简单记忆",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
2752,
848
],
"parameters": {
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "e553c8bc-93e7-4cf3-a286-76128d2ab8da",
"name": "响应聊天1",
"type": "@n8n/n8n-nodes-langchain.chat",
"position": [
1216,
1056
],
"parameters": {
"message": "={{ $json.output }}",
"options": {
"memoryConnection": true
},
"waitUserReply": false
},
"typeVersion": 1
},
{
"id": "5c278da9-f384-49bd-9202-4d952a46f933",
"name": "简单记忆1",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1280,
1200
],
"parameters": {
"sessionKey": "={{ $('When chat message received').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "5cc09f69-8b48-45c3-83c6-bab66816e655",
"name": "技术创新报告工具",
"type": "@n8n/n8n-nodes-langchain.toolCode",
"position": [
688,
864
],
"parameters": {
"jsCode": "return \"ok\"",
"schemaType": "manual",
"description": "从《2025年技术与创新报告：包容性人工智能促进发展》PDF中查询相关文档。作为输入，请详细描述用户请求的上下文和具体内容。",
"inputSchema": "{\n\"type\": \"object\",\n \"required\": [\"query\"],\n\"properties\": {\n \"query\": {\n \"type\": \"string\",\n \"description\": \"The user's question\"\n }\n }\n}",
"specifyInputSchema": true
},
"typeVersion": 1.3
},
{
"id": "5ca66760-172e-48a5-93e1-5007026ffc9a",
"name": "通过 Command-A-Vision 进行图像理解",
"type": "n8n-nodes-base.httpRequest",
"position": [
2240,
704
],
"parameters": {
"url": "https://api.cohere.com/v2/chat",
"method": "POST",
"options": {},
"jsonBody": "={{\n{\n \"model\": \"command-a-vision-07-2025\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": $('Get Query').item.json.query\n },\n ...$json.data.map(data => ({\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": data.document.pageContent,\n \"detail\": \"auto\"\n }\n }))\n ]\n }\n ]\n}\n}}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "accept",
"value": "application/json"
}
]
},
"nodeCredentialType": "cohereApi"
},
"credentials": {
"cohereApi": {
"id": "uakbZrf6hh3F6T7h",
"name": "CohereApi account"
}
},
"typeVersion": 4.2
},
{
"id": "2d93eb6c-1b39-415a-a7f4-f1f02b43ff33",
"name": "通过 Command-R 进行聊天模型",
"type": "@n8n/n8n-nodes-langchain.lmChatCohere",
"position": [
480,
864
],
"parameters": {
"model": "command-r",
"options": {}
},
"credentials": {
"cohereApi": {
"id": "uakbZrf6hh3F6T7h",
"name": "CohereApi account"
}
},
"typeVersion": 1
},
{
"id": "ae8ccb5c-8b3b-4f43-991f-c1b4d189d460",
"name": "获取查询",
"type": "n8n-nodes-base.set",
"position": [
1216,
704
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1d43f919-4eca-419a-abbb-d14f52a44c04",
"name": "tool",
"type": "string",
"value": "={{ $json.intermediateSteps[0].action.tool }}"
},
{
"id": "3f9ba213-a2a5-4dc1-b84f-efba7870a1e9",
"name": "query",
"type": "string",
"value": "={{ $json.intermediateSteps[0].action.toolInput.query }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "ead3ee0e-3f25-4710-90d9-6ecffd70202d",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-48,
-192
],
"parameters": {
"color": 7,
"width": 608,
"height": 480,
"content": "## 1. 下载报告页面扫描件"
},
"typeVersion": 1
},
{
"id": "5d89e72b-1509-4171-a22d-13fe3774ff29",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1072,
-288
],
"parameters": {
"color": 7,
"width": 672,
"height": 528,
"content": "## 2. 使用 Cohere Embed v4 进行图像嵌入"
},
"typeVersion": 1
},
{
"id": "24bff51a-41bc-450c-865a-2782a141a11e",
"name": "将图像转换为 Base64",
"type": "n8n-nodes-base.extractFromFile",
"position": [
1344,
0
],
"parameters": {
"options": {},
"operation": "binaryToPropery"
},
"typeVersion": 1
},
{
"id": "9db29630-8f4f-4494-bf07-5ab8a4ff2ad9",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1776,
-288
],
"parameters": {
"color": 7,
"width": 624,
"height": 592,
"content": "## 3. 将向量存储在 Qdrant 向量存储中"
},
"typeVersion": 1
},
{
"id": "74153b2f-ae2b-4c18-94a3-5ba762824341",
"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
2432,
-272
],
"parameters": {
"width": 368,
"height": 368,
"content": "### 创建示例 Qdrant 集合"
},
"typeVersion": 1
},
{
"id": "425b74ea-0597-4890-ac1e-3b313761d161",
"name": "创建集合",
"type": "n8n-nodes-qdrant.qdrant",
"position": [
2560,
-80
],
"parameters": {
"vectors": "{\n \"vectors\": {\n \"distance\": \"Cosine\",\n \"size\": 1536\n }\n}",
"operation": "createCollection",
"shardNumber": {},
"collectionName": "visionRagExample",
"requestOptions": {},
"replicationFactor": {},
"writeConsistencyFactor": {}
},
"credentials": {
"qdrantRestApi": {
"id": "Px8bPm0Qb8kjI7AA",
"name": "localhost"
}
},
"typeVersion": 1
},
{
"id": "e007c3d5-8e0b-4ceb-b552-178ef42e5b4a",
"name": "便签4",
"type": "n8n-nodes-base.stickyNote",
"position": [
192,
384
],
"parameters": {
"color": 7,
"width": 704,
"height": 656,
"content": "## 4. 让我们构建一个视觉 RAG 代理"
},
"typeVersion": 1
},
{
"id": "422b6c6d-7158-4975-880c-b5b64de545d3",
"name": "便签5",
"type": "n8n-nodes-base.stickyNote",
"position": [
928,
480
],
"parameters": {
"color": 7,
"width": 688,
"height": 432,
"content": "## 5. 如果调用工具则切换到视觉模型"
},
"typeVersion": 1
},
{
"id": "1a894089-0271-4dc3-9f05-00c5b2e10125",
"name": "便签6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1648,
416
],
"parameters": {
"color": 7,
"width": 832,
"height": 576,
"content": "## 6. 使用 Cohere Command-A 视觉模型进行图像理解"
},
"typeVersion": 1
},
{
"id": "701a229e-5356-4afd-ab78-c503d71015fa",
"name": "获取相关图像",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1744,
704
],
"parameters": {
"mode": "load",
"prompt": "={{ $json.query }}",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "visionRagExample",
"cachedResultName": "visionRagExample"
},
"includeDocumentMetadata": false
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1.3
},
{
"id": "b95a5a73-0a1b-4aee-99af-e45c0dda53a2",
"name": "嵌入",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
1824,
848
],
"parameters": {
"modelName": "=embed-v4.0"
},
"credentials": {
"cohereApi": {
"id": "uakbZrf6hh3F6T7h",
"name": "CohereApi account"
}
},
"typeVersion": 1
},
{
"id": "3354b318-3b5f-412c-bfe4-75ad2a12a0a2",
"name": "聚合结果",
"type": "n8n-nodes-base.aggregate",
"position": [
2048,
704
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "1f831ccd-1e5c-4b67-9e83-b184f6a91a38",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
2512,
480
],
"parameters": {
"color": 7,
"width": 560,
"height": 512,
"content": "## 7. 将响应与图像一起发送给用户"
},
"typeVersion": 1
},
{
"id": "5257f5b4-9870-4b23-a582-4f19f2c4d4d1",
"name": "便签8",
"type": "n8n-nodes-base.stickyNote",
"position": [
1072,
944
],
"parameters": {
"color": 7,
"width": 528,
"height": 416,
"content": "## 5.1. 否则，作为普通代理响应"
},
"typeVersion": 1
},
{
"id": "df38d0d9-488a-4077-b574-d9dc75c9a245",
"name": "快速确认",
"type": "@n8n/n8n-nodes-langchain.chat",
"position": [
1424,
704
],
"parameters": {
"message": "Please wait while I search the document...",
"options": {},
"waitUserReply": false
},
"typeVersion": 1
},
{
"id": "4c36acd3-ab20-4acb-b912-d977c6f477ab",
"name": "便签9",
"type": "n8n-nodes-base.stickyNote",
"position": [
-656,
-320
],
"parameters": {
"width": 496,
"height": 1152,
"content": "## 使用 Cohere Command-A 和 Embed v4 进行视觉 RAG 和图像嵌入"
},
"typeVersion": 1
},
{
"id": "ddfa986d-986d-4f1d-9edc-1553fc368ffd",
"name": "便签 10",
"type": "n8n-nodes-base.stickyNote",
"position": [
-656,
864
],
"parameters": {
"width": 496,
"height": 288,
"content": ""
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"Batch 5": {
"main": [
[],
[
{
"node": "Page Ref",
"type": "main",
"index": 0
}
]
]
},
"AI Agent": {
"main": [
[
{
"node": "If has Tool Call?",
"type": "main",
"index": 0
}
]
]
},
"Page Ref": {
"main": [
[
{
"node": "Download Page",
"type": "main",
"index": 0
}
]
]
},
"Get Query": {
"main": [
[
{
"node": "Quick Confirmation",
"type": "main",
"index": 0
}
]
]
},
"Embeddings": {
"ai_embedding": [
[
{
"node": "Get Relevant Images",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Download Page": {
"main": [
[
{
"node": "Convert Image to Base64",
"type": "main",
"index": 0
}
]
]
},
"Insert Points": {
"main": [
[
{
"node": "Batch 5",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "Respond to Chat",
"type": "ai_memory",
"index": 0
}
]
]
},
"Prepare Points": {
"main": [
[
{
"node": "Aggregate Points",
"type": "main",
"index": 0
}
]
]
},
"Simple Memory1": {
"ai_memory": [
[
{
"node": "Respond to Chat1",
"type": "ai_memory",
"index": 0
}
]
]
},
"Split Out Urls": {
"main": [
[
{
"node": "Batch 5",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Points": {
"main": [
[
{
"node": "Insert Points",
"type": "main",
"index": 0
}
]
]
},
"Aggregate Results": {
"main": [
[
{
"node": "Image Understanding via Command-A-Vision",
"type": "main",
"index": 0
}
]
]
},
"If has Tool Call?": {
"main": [
[
{
"node": "Get Query",
"type": "main",
"index": 0
}
],
[
{
"node": "Respond to Chat1",
"type": "main",
"index": 0
}
]
]
},
"Quick Confirmation": {
"main": [
[
{
"node": "Get Relevant Images",
"type": "main",
"index": 0
}
]
]
},
"Get Relevant Images": {
"main": [
[
{
"node": "Aggregate Results",
"type": "main",
"index": 0
}
]
]
},
"Convert Image to Base64": {
"main": [
[
{
"node": "Image Embeddings with Cohere Embed 4",
"type": "main",
"index": 0
}
]
]
},
"Chat Model via Command-R": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Technology Innovation Report Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Image Embeddings with Cohere Embed 4": {
"main": [
[
{
"node": "Prepare Points",
"type": "main",
"index": 0
}
]
]
},
"When clicking ‘Execute workflow’": {
"main": [
[
{
"node": "Technology and Innovation Report 2025",
"type": "main",
"index": 0
}
]
]
},
"Technology and Innovation Report 2025": {
"main": [
[
{
"node": "Split Out Urls",
"type": "main",
"index": 0
}
]
]
},
"Image Understanding via Command-A-Vision": {
"main": [
[
{
"node": "Respond to Chat",
"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 - 文档提取, AI RAG 检索增强
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。
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工作流信息
难度等级
高级
节点数量38
分类2
节点类型19
作者
Jimleuk
@jimleukFreelance AI Automation Engineer based in London, UK. Since 2024, my n8n templates have documented my journey into applied AI and have helped hundreds of businesses and organisations get up to speed with AI automation. Today, I continue to explore use-cases as AI evolves and occasionally upload templates which I find novel and interesting. Subscribe to the RSS Feed: https://cdn.subworkflow.ai/n8n-templates/rss.xml
外部链接
在 n8n.io 查看 →
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