Lokaler KI-Kaggle-Wettbewerbsassistent mit Qdrant RAG und Ollama erstellen
Experte
Dies ist ein Engineering, AI-Bereich Automatisierungsworkflow mit 23 Nodes. Hauptsächlich werden Set, Merge, Switch, Markdown, ReadWriteFile und andere Nodes verwendet, kombiniert mit KI-Technologie für intelligente Automatisierung. Baue einen lokalen AI Kaggle-Wettbewerbs-Assistenten mit Qdrant RAG und Ollama
Voraussetzungen
- •Qdrant-Serververbindungsdaten
Verwendete Nodes (23)
Kategorie
Workflow-Vorschau
Visualisierung der Node-Verbindungen, mit Zoom und Pan
Workflow exportieren
Kopieren Sie die folgende JSON-Konfiguration und importieren Sie sie in n8n
{
"meta": {
"instanceId": "13a0050774c7f2acc1474b06f046215039c01087a78215e5a78461e6efc6cb1a",
"templateCredsSetupCompleted": true
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"nodes": [
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"name": "Lokaler Datei-Trigger",
"type": "n8n-nodes-base.localFileTrigger",
"position": [
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"parameters": {
"path": "C:\\\\ipynb\\\\loadme",
"events": [
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"options": {
"usePolling": true,
"followSymlinks": true,
"awaitWriteFinish": true
},
"triggerOn": "folder"
},
"typeVersion": 1
},
{
"id": "893f1157-6c00-4b8e-b726-462ab371fadf",
"name": "Standard-Datenlader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
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300
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "9a9bfcee-1966-415c-a59f-552e1f35aae9",
"name": "Rekursiver Zeichentext-Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
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"parameters": {
"options": {},
"chunkSize": 40,
"chunkOverlap": 10
},
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{
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"name": "Einstellungen",
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"position": [
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"parameters": {
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"assignments": {
"assignments": [
{
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"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": "Zusammenführen",
"type": "n8n-nodes-base.merge",
"position": [
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],
"parameters": {},
"typeVersion": 2.1
},
{
"id": "db4de019-755e-4b91-ac70-f30825f14033",
"name": "Dateityp abrufen",
"type": "n8n-nodes-base.switch",
"position": [
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],
"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": "Datei importieren",
"type": "n8n-nodes-base.readWriteFile",
"position": [
-2840,
80
],
"parameters": {
"options": {},
"fileSelector": "={{ $json.path }}{{ $json.filename }}"
},
"typeVersion": 1
},
{
"id": "c14a711f-29ab-475f-aeff-3a070c797537",
"name": "Aus TEXT extrahieren",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-2440,
80
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "22ff782e-c612-4928-9033-111cf516d07e",
"name": "Zusammenfassungskette",
"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": "Haftnotiz",
"type": "n8n-nodes-base.stickyNote",
"position": [
-3100,
-180
],
"parameters": {
"color": 7,
"width": 995,
"height": 554,
"content": "## Step 1. Watch Folder and Import New Documents\n[Read more about Local File Trigger](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.localfiletrigger)\n\nWith n8n's local file trigger, we're able to trigger the workflow when files are created in our target folder. We still have to import them however as the trigger will only give the file's path. The \"Extract From\" node is used to get at the file's contents."
},
"typeVersion": 1
},
{
"id": "a51cc8ac-e310-4825-adc6-fc57c68c09aa",
"name": "Haftnotiz1",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 7,
"width": 824,
"height": 770,
"content": "## Step 2. Summarise and Vectorise Document Contents\n[Learn more about using the Qdrant VectorStore](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.vectorstoreqdrant)\n\nCapturing the document into our vector store is intended for a technique we'll use later known as Retrieval Augumented Generation or \"RAG\" for short. For our scenario, this allows our LLM to retrieve context more efficiently which produces better respsonses."
},
"typeVersion": 1
},
{
"id": "6d59dc6a-692a-4752-a811-8b3033898fa4",
"name": "Qdrant Vector Store",
"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": "Embeddings 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 Summarizer",
"type": "@n8n/n8n-nodes-langchain.lmOllama",
"position": [
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140
],
"parameters": {
"model": "ALIENTELLIGENCE/contentsummarizer:latest",
"options": {}
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "0a2954cc-bec6-4750-ae75-6362761e41b6",
"name": "Bei Chat-Nachrichtenempfang",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
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],
"webhookId": "9dd3e051-58a3-4c46-bd41-58c001f009f9",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "1ebe053c-0e26-44c6-b543-756ad551b99d",
"name": "KI-Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
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],
"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": "Vector Store Tool",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
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],
"parameters": {
"name": "previous_entry",
"description": "={{ $('When chat message received').item.json.chatInput }}"
},
"typeVersion": 1
},
{
"id": "fbae9bc0-6ea4-4a26-ad76-eb84bc5d06c2",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
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],
"parameters": {
"contextWindowLength": 15
},
"typeVersion": 1.3
},
{
"id": "2f567628-fd1d-406b-aec7-46684bd6f5e6",
"name": "Qdrant Vector Store2",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
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],
"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 Chat Model3",
"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": "Embeddings Ollama2",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
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],
"parameters": {
"model": "mxbai-embed-large:latest"
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
"typeVersion": 1
},
{
"id": "a1c71691-8e41-4633-a1ab-4991833fb7c6",
"name": "Ollama Chat Model4",
"type": "@n8n/n8n-nodes-langchain.lmChatOllama",
"position": [
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],
"parameters": {
"model": "qwen3:8b",
"options": {}
},
"credentials": {
"ollamaApi": {
"id": "jBqODDnXWJw9rGcS",
"name": "Ollama account"
}
},
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}
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"2f567628-fd1d-406b-aec7-46684bd6f5e6": {
"ai_vectorStore": [
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"fbae9bc0-6ea4-4a26-ad76-eb84bc5d06c2": {
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},
"0a2954cc-bec6-4750-ae75-6362761e41b6": {
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[
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},
"9a9bfcee-1966-415c-a59f-552e1f35aae9": {
"ai_textSplitter": [
[
{
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"type": "ai_textSplitter",
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}
]
]
}
}
}Häufig gestellte Fragen
Wie verwende ich diesen Workflow?
Kopieren Sie den obigen JSON-Code, erstellen Sie einen neuen Workflow in Ihrer n8n-Instanz und wählen Sie "Aus JSON importieren". Fügen Sie die Konfiguration ein und passen Sie die Anmeldedaten nach Bedarf an.
Für welche Szenarien ist dieser Workflow geeignet?
Experte - Engineering, Künstliche Intelligenz
Ist es kostenpflichtig?
Dieser Workflow ist völlig kostenlos. Beachten Sie jedoch, dass Drittanbieterdienste (wie OpenAI API), die im Workflow verwendet werden, möglicherweise kostenpflichtig sind.
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