Dokumente in Lernnotizen aufteilen
Dies ist ein Other, AI-Bereich Automatisierungsworkflow mit 42 Nodes. Hauptsächlich werden Set, Wait, Merge, Switch, SplitOut und andere Nodes verwendet, kombiniert mit KI-Technologie für intelligente Automatisierung. Dokumentenzerlegung in Lernnotizen mit template-basiertem MistralAI und Qdrant
- •Qdrant-Serververbindungsdaten
Verwendete Nodes (42)
Kategorie
{
"meta": {
"instanceId": "26ba763460b97c249b82942b23b6384876dfeb9327513332e743c5f6219c2b8e"
},
"nodes": [
{
"id": "a3af309b-d24c-42fe-8bcd-f330927c7a3c",
"name": "Lokaler Datei-Trigger",
"type": "n8n-nodes-base.localFileTrigger",
"position": [
140,
260
],
"parameters": {
"path": "/home/node/storynotes/context",
"events": [
"add"
],
"options": {
"usePolling": true,
"followSymlinks": true
},
"triggerOn": "folder"
},
"typeVersion": 1
},
{
"id": "048f9d67-6519-4dea-97df-aaddfefbfea2",
"name": "Standard-Datenlader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1300,
720
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "project",
"value": "={{ $('Settings').item.json.project }}"
},
{
"name": "filename",
"value": "={{ $('Settings').item.json.filename }}"
}
]
}
},
"jsonData": "={{ $json.data }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "9e9047c9-4428-4afb-8c74-d6eb1075a65a",
"name": "Rekursiver Zeichentext-Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1300,
860
],
"parameters": {
"options": {},
"chunkSize": 2000
},
"typeVersion": 1
},
{
"id": "e42e3f82-6cd9-40c4-9da2-8f87ee5b3956",
"name": "Embeddings Mistral Cloud",
"type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
"position": [
1180,
720
],
"parameters": {
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "578c63db-4f6e-4341-ab0d-111debd519be",
"name": "Mistral Cloud Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
2660,
840
],
"parameters": {
"model": "open-mixtral-8x7b",
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "c34adb3e-1fb9-4248-ae83-2bac34c8b0a4",
"name": "Mistral Cloud Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
1200,
400
],
"parameters": {
"model": "open-mixtral-8x7b",
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "98e6dcc0-1e3a-4119-b657-0949f34ba525",
"name": "Eingehendes Dokument vorbereiten",
"type": "n8n-nodes-base.set",
"position": [
900,
420
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "da64ffde-1e8f-478d-baea-59fc05e6d3ce",
"name": "data",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "ab88cf9a-d310-4bef-9280-8b23729e7cc9",
"name": "Einstellungen",
"type": "n8n-nodes-base.set",
"position": [
320,
260
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "df327b01-961c-4a49-8455-58c3fbff111a",
"name": "project",
"type": "string",
"value": "={{ $json.path.split('/').slice(0, 4)[3] }}"
},
{
"id": "6b7d26f9-3a38-417e-85d0-4e9d42476465",
"name": "path",
"type": "string",
"value": "={{ $json.path }}"
},
{
"id": "bb4471c7-d894-4739-99a6-4be247794ffa",
"name": "filename",
"type": "string",
"value": "={{ $json.path.split('/').last() }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "35c6b678-e6e9-4adf-a904-909fa2401d5e",
"name": "Zusammenführen",
"type": "n8n-nodes-base.merge",
"position": [
1600,
420
],
"parameters": {
"mode": "chooseBranch"
},
"typeVersion": 2.1
},
{
"id": "0fa13be8-8500-486c-a1c6-cc1df00a4947",
"name": "Dokumenttypen abrufen",
"type": "n8n-nodes-base.set",
"position": [
2000,
420
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "{\n \"docs\": [\n {\n \"filename\": \"study_guide.md\",\n \"title\": \"Study Guide\",\n \"description\": \"A Study Guide is a consolidated resource designed to aid learning. This guide includes three key elements: * A short answer quiz accompanied by an answer key to test comprehension. * A curated list of long-form essay questions to encourage deeper analysis and synthesis of the material. * A glossary of key terms to reinforce understanding of important concepts.\"\n },\n {\n \"filename\": \"timeline.md\",\n \"title\": \"Timeline\",\n \"description\": \"A Timeline organizes all significant events described in the sources you have uploaded in chronological order. This ordered list makes it easier to understand the sequence of events and their connection to the broader context of your sources. In addition to the list of events, the Timeline also provides a “cast of characters,” which comprises short biographical sketches of all the important people mentioned in your uploaded sources. These short biographies can help you quickly grasp the roles of various individuals involved in the events described by the Timeline.\"\n },\n {\n \"filename\": \"briefing_doc.md\",\n \"title\": \"Briefing Doc\",\n \"description\": \"A Briefing Doc identifies and presents the most important facts and insights from the sources in an easy-to-understand outline format. This format is designed to provide a concise overview of the key takeaways from the uploaded materials.\"\n }\n ]\n}\n"
},
"executeOnce": true,
"typeVersion": 3.3
},
{
"id": "e3469368-f214-4549-844e-7febfbbf0202",
"name": "Dokumenttypen aufteilen",
"type": "n8n-nodes-base.splitOut",
"position": [
2160,
420
],
"parameters": {
"options": {},
"fieldToSplitOut": "docs"
},
"typeVersion": 1
},
{
"id": "df401e9e-2f70-4079-969b-6b61142fca37",
"name": "Für jeden Dokumenttyp...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
2340,
420
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "c334b546-8e11-424d-bdd5-006e7086f24b",
"name": "Item-List-Ausgabeparser",
"type": "@n8n/n8n-nodes-langchain.outputParserItemList",
"position": [
2840,
840
],
"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "4267c2b5-f1cd-4df7-84ee-be01a643a1c1",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
3200,
840
],
"parameters": {},
"typeVersion": 1
},
{
"id": "abf833ec-8a6d-4e13-a526-0ea6b80d578f",
"name": "Embeddings Mistral Cloud1",
"type": "@n8n/n8n-nodes-langchain.embeddingsMistralCloud",
"position": [
3200,
1060
],
"parameters": {
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "a0e50185-6662-4b11-9922-59e8b06e4967",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
3200,
940
],
"parameters": {
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "storynotes",
"cachedResultName": "storynotes"
}
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "20c5766a-d3ce-4c01-a76b-facf1a00abc2",
"name": "Mistral Cloud Chat Model2",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
3100,
840
],
"parameters": {
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "f049b7af-07f3-47e5-9476-68d73a387978",
"name": "Aufteilen",
"type": "n8n-nodes-base.splitOut",
"position": [
2960,
680
],
"parameters": {
"options": {},
"fieldToSplitOut": "response"
},
"typeVersion": 1
},
{
"id": "39042ae0-e17f-46cd-84be-728868950d84",
"name": "Aggregieren",
"type": "n8n-nodes-base.aggregate",
"position": [
3400,
680
],
"parameters": {
"options": {},
"fieldsToAggregate": {
"fieldToAggregate": [
{
"fieldToAggregate": "response.text"
}
]
}
},
"typeVersion": 1
},
{
"id": "e3b900c8-515d-4ac7-88fa-c364134ba9f9",
"name": "Mistral Cloud Chat Model3",
"type": "@n8n/n8n-nodes-langchain.lmChatMistralCloud",
"position": [
3540,
840
],
"parameters": {
"model": "open-mixtral-8x7b",
"options": {}
},
"credentials": {
"mistralCloudApi": {
"id": "EIl2QxhXAS9Hkg37",
"name": "Mistral Cloud account"
}
},
"typeVersion": 1
},
{
"id": "efb26a5d-6a61-44b2-ad99-6d1f8b48998d",
"name": "Erkunden",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
3100,
680
],
"parameters": {
"text": "={{ $json.response }}",
"promptType": "define"
},
"typeVersion": 1.3
},
{
"id": "302b7523-898e-47af-8941-aa5f8a58fd9c",
"name": "2 Sekunden",
"type": "n8n-nodes-base.wait",
"position": [
3880,
1060
],
"webhookId": "ec58ab18-03c5-4b58-bc2e-24415a236c72",
"parameters": {},
"typeVersion": 1.1
},
{
"id": "007857b0-c12c-4c57-b07f-db30526cd747",
"name": "Generierte Dokumente abrufen",
"type": "n8n-nodes-base.set",
"position": [
2680,
240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "b38546b2-47c4-4967-a2d7-98aebd589e95",
"name": "data",
"type": "string",
"value": "={{ $json.text }}"
},
{
"id": "a263519a-aa05-410a-b4f0-f5e22cc5058c",
"name": "path",
"type": "string",
"value": "={{ $('Prep For AI').item.json.path }}"
},
{
"id": "ec1687d6-0ea9-460f-b9d4-ae4a7e229e12",
"name": "filename",
"type": "string",
"value": "={{ $('Prep For AI').item.json.name }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "36fac35f-df10-41ab-96a7-3a5e67f9d8df",
"name": "Generieren",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
3540,
680
],
"parameters": {
"text": "=## Document\n{{ $json.text.join('\\n') }}",
"messages": {
"messageValues": [
{
"message": "=Your job is to create a {{ $('For Each Doc Type...').item.json.title }} for the given document. {{ $('For Each Doc Type...').item.json.description }}\n\nGenerate a {{ $('For Each Doc Type...').item.json.title }} for the given document. If questions are generated, generate the answers alongside them. Format your response in markdown; use \"#\" to format headings, use \"*\" to format lists."
}
]
},
"promptType": "define"
},
"typeVersion": 1.4
},
{
"id": "b9a79cb0-bcc1-4d73-af93-5f8d7e2258a9",
"name": "Für KI vorbereiten",
"type": "n8n-nodes-base.set",
"position": [
1760,
420
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "5c864125-c884-4d33-b0ed-e3eecd354196",
"name": "id",
"type": "string",
"value": "={{ $('Settings').first().json.filename.hash() }}"
},
{
"id": "93ac14c1-ae97-4ef2-a66f-6c1110f3b0fc",
"name": "project",
"type": "string",
"value": "={{ $('Settings').first().json.project }}"
},
{
"id": "fafd16b9-0002-4f7c-89d0-29788f8ec472",
"name": "path",
"type": "string",
"value": "={{ $('Settings').first().json.path }}"
},
{
"id": "5a5860ba-918b-4fb8-b18c-96c1cd22091a",
"name": "name",
"type": "string",
"value": "={{ $('Settings').first().json.filename }}"
},
{
"id": "1a1caf65-85d8-4f74-a3be-503ccfc0b2c9",
"name": "summary",
"type": "string",
"value": "={{ $('Summarization Chain').first().json.response.text }}"
}
]
}
},
"typeVersion": 3.3
},
{
"id": "e40c7e99-9813-4f06-92bb-dfb2839f1037",
"name": "Zu Binär",
"type": "n8n-nodes-base.convertToFile",
"position": [
2860,
240
],
"parameters": {
"options": {},
"operation": "toText",
"sourceProperty": "={{ $json.data }}"
},
"typeVersion": 1.1
},
{
"id": "b55df916-7a51-4114-91b8-18a3c6ba2c56",
"name": "In Ordner exportieren",
"type": "n8n-nodes-base.readWriteFile",
"position": [
3020,
240
],
"parameters": {
"options": {},
"fileName": "={{\n $('Get Generated Documents').item.json.path.replace(\n $('Get Generated Documents').item.json.path.split('/').last(),\n $('Get Generated Documents').item.json.filename.substring(0,21) + '...' + $('Split Out Doc Types').item.json.title + '.md'\n )\n}}",
"operation": "write"
},
"typeVersion": 1
},
{
"id": "8490664e-0ca5-4839-ad03-d3f9706c99a3",
"name": "Dateityp ermitteln",
"type": "n8n-nodes-base.switch",
"position": [
480,
420
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "pdf",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.fileType }}",
"rightValue": "pdf"
}
]
},
"renameOutput": true
},
{
"outputKey": "docx",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "3a5f509d-46fe-490c-95f0-35124873c63e",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.fileType }}",
"rightValue": "docx"
}
]
},
"renameOutput": true
},
{
"outputKey": "everything else",
"conditions": {
"options": {
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "75188d2f-4bea-44ea-a579-9b9a1bd1ea93",
"operator": {
"type": "object",
"operation": "exists",
"singleValue": true
},
"leftValue": "={{ $json }}",
"rightValue": ""
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3
},
{
"id": "386f7aac-f3b9-4565-907f-687d48b00c52",
"name": "Datei importieren",
"type": "n8n-nodes-base.readWriteFile",
"position": [
320,
420
],
"parameters": {
"options": {},
"fileSelector": "={{ $json.path }}"
},
"typeVersion": 1
},
{
"id": "6ade93d5-61c3-450a-b78c-e210c18c0e70",
"name": "Aus PDF extrahieren",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
260
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "f413e139-3f9c-438f-8e82-824c38f09c6b",
"name": "Aus DOCX extrahieren",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
420
],
"parameters": {
"options": {},
"operation": "ods"
},
"typeVersion": 1
},
{
"id": "455fadea-f5c7-4bea-983f-b06da4e57510",
"name": "Aus TEXT extrahieren",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
580
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "b2586011-4985-4075-b51c-90301b1a8cf9",
"name": "Zusammenfassungskette",
"type": "@n8n/n8n-nodes-langchain.chainSummarization",
"position": [
1200,
260
],
"parameters": {
"options": {},
"chunkSize": 4000
},
"typeVersion": 2
},
{
"id": "1502e72c-e97e-4148-8138-01818ab5b104",
"name": "Haftnotiz",
"type": "n8n-nodes-base.stickyNote",
"position": [
60,
85.80882007954312
],
"parameters": {
"color": 7,
"width": 995.1475972814769,
"height": 694.0931000693263,
"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": "7b3afc2c-3fb8-4589-9475-78f5617009cc",
"name": "Haftnotiz1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1080,
82.96464765818223
],
"parameters": {
"color": 7,
"width": 824.3300768713589,
"height": 949.8141899605673,
"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": "74aabb02-ca5d-41ad-b84f-92d66428b774",
"name": "Haftnotiz2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1940,
156.7963650826494
],
"parameters": {
"color": 7,
"width": 591.09953935829,
"height": 485.0226378812345,
"content": "## Step 3. Loop through Templates\n\nWe'll ask the LLM to help us generate 3 types of notes from the imported source document. These notes are intended to breakdown the content for faster study. Our templates for this demo are:\n(1) **Study guide**\n(2) **Briefing document**\n(3) **Timeline**"
},
"typeVersion": 1
},
{
"id": "b96f899d-4a44-491c-b164-a42feba129eb",
"name": "Haftnotiz3",
"type": "n8n-nodes-base.stickyNote",
"position": [
2560,
480
],
"parameters": {
"color": 7,
"width": 1500.7886103732135,
"height": 806.6560661824452,
"content": "## Step 4. Use AI Agents to Query and Generate Template Documents\n[Read more about using the Question & Answer Retrieval Chain](https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.chainretrievalqa)\n\nn8n allows us to easily use a chain of LLMs as agents which can work together to handle any task!\nHere the agents generate questions to explore the content of the source document and use the answers to generate the template. "
},
"typeVersion": 1
},
{
"id": "77fda269-6877-422f-b6e6-4346bde862db",
"name": "Haftnotiz4",
"type": "n8n-nodes-base.stickyNote",
"position": [
2560,
67.64523011966037
],
"parameters": {
"color": 7,
"width": 771.8710855215123,
"height": 384.22073222791266,
"content": "## Step 5. Export Generated Templates To Folder\n[Learn more about writing to the local filesystem](https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.filesreadwrite)\n\nFinally, the AI generated documents can now be exported to disk. This workflow makes it easy to generate any kind of document from various source material and can be used for training and sales."
},
"typeVersion": 1
},
{
"id": "08839972-f0f4-4144-bf27-810664cbf828",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
1200,
560
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "storynotes",
"cachedResultName": "storynotes"
}
},
"credentials": {
"qdrantApi": {
"id": "NyinAS3Pgfik66w5",
"name": "QdrantApi account"
}
},
"typeVersion": 1
},
{
"id": "7e216411-83ee-4b82-9e00-285d4f2d3224",
"name": "Haftnotiz5",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
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"content": "## Try It Out! \n\n### This workflow automates generating notes from a source document.\n* It watches a target folder to pick up new files.\n* When a new file is detected, it saves the contents of the file in a vectorstore.\n* multiple AI agents guided by a templates list, generate the predetermined notes.\n* These notes are then export alongside the original source file for the user.\n\n\n### Need Help?\nJoin the [Discord](https://discord.com/invite/XPKeKXeB7d) or ask in the [Forum](https://community.n8n.io/)!\n\nHappy Hacking!"
},
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},
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"name": "Interview",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
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"text": "=## document summary\n {{ $('Prep For AI').item.json.summary }}",
"messages": {
"messageValues": [
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}
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}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 - Sonstiges, 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.
Verwandte Workflows
Jimleuk
@jimleukFreelance consultant based in the UK specialising in AI-powered automations. I work with select clients tackling their most challenging projects. For business enquiries, send me an email at hello@jimle.uk LinkedIn: https://www.linkedin.com/in/jimleuk/ X/Twitter: https://x.com/jimle_uk
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