使用Qdrant RAG和Ollama构建本地AI Kaggle竞赛助手
高级
这是一个Engineering, AI领域的自动化工作流，包含 23 个节点。主要使用 Set, Merge, Switch, Markdown, ReadWriteFile 等节点，结合人工智能技术实现智能自动化。 使用Qdrant RAG和Ollama构建本地AI Kaggle竞赛助手
前置要求
- •Qdrant 服务器连接信息
使用的节点 (23)
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": "13a0050774c7f2acc1474b06f046215039c01087a78215e5a78461e6efc6cb1a",
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "70b42807-a6c6-4159-b278-e77311727798",
"name": "本地文件触发器",
"type": "n8n-nodes-base.localFileTrigger",
"position": [
-3060,
-40
],
"parameters": {
"path": "C:\\\\ipynb\\\\loadme",
"events": [
"add"
],
"options": {
"usePolling": true,
"followSymlinks": true,
"awaitWriteFinish": true
},
"triggerOn": "folder"
},
"typeVersion": 1
},
{
"id": "893f1157-6c00-4b8e-b726-462ab371fadf",
"name": "默认数据加载器",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
-1500,
300
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "9a9bfcee-1966-415c-a59f-552e1f35aae9",
"name": "递归字符文本分割器",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
-1360,
440
],
"parameters": {
"options": {},
"chunkSize": 40,
"chunkOverlap": 10
},
"typeVersion": 1
},
{
"id": "a7c971a5-39ac-4715-9e1b-a56af9713b06",
"name": "设置",
"type": "n8n-nodes-base.set",
"position": [
-3040,
180
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "6b7d26f9-3a38-417e-85d0-4e9d42476465",
"name": "path",
"type": "string",
"value": "=C:\\\\ipynb\\\\loadme\\\\"
},
{
"id": "bb4471c7-d894-4739-99a6-4be247794ffa",
"name": "filename",
"type": "string",
"value": "={{ $json.path.split('\\\\').last() }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "6384792b-de76-4e43-b26e-12c2d15c2dd2",
"name": "合并",
"type": "n8n-nodes-base.merge",
"position": [
-1740,
260
],
"parameters": {},
"typeVersion": 2.1
},
{
"id": "db4de019-755e-4b91-ac70-f30825f14033",
"name": "获取文件类型",
"type": "n8n-nodes-base.switch",
"position": [
-2620,
80
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "html",
"conditions": {
"options": {
"version": 1,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "75188d2f-4bea-44ea-a579-9b9a1bd1ea93",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.fileType }}",
"rightValue": "html"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3
},
{
"id": "4c56a14c-6c56-4cc1-b7fb-a09caa3d646d",
"name": "导入文件",
"type": "n8n-nodes-base.readWriteFile",
"position": [
-2840,
80
],
"parameters": {
"options": {},
"fileSelector": "={{ $json.path }}{{ $json.filename }}"
},
"typeVersion": 1
},
{
"id": "c14a711f-29ab-475f-aeff-3a070c797537",
"name": "从 TEXT 提取",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-2440,
80
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "22ff782e-c612-4928-9033-111cf516d07e",
"name": "摘要链",
"type": "@n8n/n8n-nodes-langchain.chainSummarization",
"position": [
-2040,
-20
],
"parameters": {
"options": {
"summarizationMethodAndPrompts": {
"values": {
"summarizationMethod": "refine"
}
}
},
"chunkSize": 4000
},
"typeVersion": 2
},
{
"id": "70fa17a5-3ec9-4a81-86bc-503581505ea1",
"name": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3100,
-180
],
"parameters": {
"color": 7,
"width": 995,
"height": 554,
"content": "## 步骤 1. 监视文件夹并导入新文档"
},
"typeVersion": 1
},
{
"id": "a51cc8ac-e310-4825-adc6-fc57c68c09aa",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-2060,
-200
],
"parameters": {
"color": 7,
"width": 824,
"height": 770,
"content": "## 步骤 2. 总结并向量化文档内容"
},
"typeVersion": 1
},
{
"id": "6d59dc6a-692a-4752-a811-8b3033898fa4",
"name": "Qdrant 向量存储",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
-1600,
60
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "test_rag"
}
},
"credentials": {
"qdrantApi": {
"id": "wqHGuxoW5RJJYSIl",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "f75f45cd-4aed-48a2-bb09-5db20b00a029",
"name": "Markdown",
"type": "n8n-nodes-base.markdown",
"position": [
-2260,
80
],
"parameters": {
"html": "={{ $json.data }}",
"options": {}
},
"typeVersion": 1
},
{
"id": "34fdd670-f568-4351-81c7-79fde68b8192",
"name": "嵌入 Ollama",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
-1560,
420
],
"parameters": {
"model": "mxbai-embed-large:latest"
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "4c4f71db-e496-4528-b0e5-dc5ffb27a2e8",
"name": "Ollama 摘要器",
"type": "@n8n/n8n-nodes-langchain.lmOllama",
"position": [
-1900,
140
],
"parameters": {
"model": "ALIENTELLIGENCE/contentsummarizer:latest",
"options": {}
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "0a2954cc-bec6-4750-ae75-6362761e41b6",
"name": "当收到聊天消息时",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-3020,
540
],
"webhookId": "9dd3e051-58a3-4c46-bd41-58c001f009f9",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "1ebe053c-0e26-44c6-b543-756ad551b99d",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
-2840,
540
],
"parameters": {
"options": {
"systemMessage": "This is a helpful and exacting data science LLM model and master Kaggle python programmer.\n\nIf Kaggle contest requirements are given from the chat input; first deeply research the problem.\n\nAccess the tool: \"previous_entry\" when preparing your background research.\n\nThen Ask any needed questions to clarify and understand the requirements necessary to build a program to address the challenge.\n\nReview your proposed program for errors and bugs.\n\nThen present the program.\n\nIf errors are returned; then iteratively debug with the chat user."
}
},
"typeVersion": 1.7
},
{
"id": "e042ec84-3bb6-466f-9957-0509a181d61b",
"name": "向量存储工具",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
-2580,
740
],
"parameters": {
"name": "previous_entry",
"description": "={{ $('When chat message received').item.json.chatInput }}"
},
"typeVersion": 1
},
{
"id": "fbae9bc0-6ea4-4a26-ad76-eb84bc5d06c2",
"name": "窗口缓冲内存",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
-2760,
780
],
"parameters": {
"contextWindowLength": 15
},
"typeVersion": 1.3
},
{
"id": "2f567628-fd1d-406b-aec7-46684bd6f5e6",
"name": "Qdrant 向量存储2",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
-2680,
920
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "test_rag",
"cachedResultName": "test_rag"
}
},
"credentials": {
"qdrantApi": {
"id": "wqHGuxoW5RJJYSIl",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "3aea837f-7676-45da-b6b1-fb2f6c5f8cd9",
"name": "Ollama 聊天模型3",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
-2900,
760
],
"parameters": {
"model": "qwen3:8b",
"options": {}
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "a9298132-e5b9-44a2-9928-a1adf7cf9fc4",
"name": "嵌入 Ollama2",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
-2660,
1080
],
"parameters": {
"model": "mxbai-embed-large:latest"
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "a1c71691-8e41-4633-a1ab-4991833fb7c6",
"name": "Ollama 聊天模型4",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
-2360,
900
],
"parameters": {
"model": "qwen3:8b",
"options": {}
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
}
],
"pinData": {},
"connections": {
"Merge": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Markdown": {
"main": [
[
{
"node": "Summarization Chain",
"type": "main",
"index": 0
},
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Settings": {
"main": [
[
{
"node": "Import File",
"type": "main",
"index": 0
}
]
]
},
"Import File": {
"main": [
[
{
"node": "Get FileType",
"type": "main",
"index": 0
}
]
]
},
"Get FileType": {
"main": [
[
{
"node": "Extract from TEXT",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Ollama": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from TEXT": {
"main": [
[
{
"node": "Markdown",
"type": "main",
"index": 0
}
]
]
},
"Ollama Summarizer": {
"ai_languageModel": [
[
{
"node": "Summarization Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Vector Store Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Embeddings Ollama2": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store2",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Local File Trigger": {
"main": [
[
{
"node": "Settings",
"type": "main",
"index": 0
}
]
]
},
"Ollama Chat Model3": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Ollama Chat Model4": {
"ai_languageModel": [
[
{
"node": "Vector Store Tool",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[]
]
},
"Summarization Chain": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store2": {
"ai_vectorStore": [
[
{
"node": "Vector Store Tool",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"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 - 工程, 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。
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