This workflow follows the Chainllm → Chainsummarization recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"nodes": [
{
"id": "a3af309b-d24c-42fe-8bcd-f330927c7a3c",
"name": "Local File 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": "Default Data Loader",
"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": "Recursive Character Text 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": {
"name": "<your credential>"
}
},
"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": {
"name": "<your credential>"
}
},
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "98e6dcc0-1e3a-4119-b657-0949f34ba525",
"name": "Prep Incoming Doc",
"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": "Settings",
"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": "Merge",
"type": "n8n-nodes-base.merge",
"position": [
1600,
420
],
"parameters": {
"mode": "chooseBranch"
},
"typeVersion": 2.1
},
{
"id": "0fa13be8-8500-486c-a1c6-cc1df00a4947",
"name": "Get Doc Types",
"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 \u201ccast of characters,\u201d 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": "Split Out Doc Types",
"type": "n8n-nodes-base.splitOut",
"position": [
2160,
420
],
"parameters": {
"options": {},
"fieldToSplitOut": "docs"
},
"typeVersion": 1
},
{
"id": "df401e9e-2f70-4079-969b-6b61142fca37",
"name": "For Each Doc Type...",
"type": "n8n-nodes-base.splitInBatches",
"position": [
2340,
420
],
"parameters": {
"options": {}
},
"typeVersion": 3
},
{
"id": "c334b546-8e11-424d-bdd5-006e7086f24b",
"name": "Item List Output Parser",
"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": {
"name": "<your credential>"
}
},
"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": {
"name": "<your credential>"
}
},
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f049b7af-07f3-47e5-9476-68d73a387978",
"name": "Split Out",
"type": "n8n-nodes-base.splitOut",
"position": [
2960,
680
],
"parameters": {
"options": {},
"fieldToSplitOut": "response"
},
"typeVersion": 1
},
{
"id": "39042ae0-e17f-46cd-84be-728868950d84",
"name": "Aggregate",
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "efb26a5d-6a61-44b2-ad99-6d1f8b48998d",
"name": "Discover",
"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": "2secs",
"type": "n8n-nodes-base.wait",
"position": [
3880,
1060
],
"parameters": {},
"typeVersion": 1.1
},
{
"id": "007857b0-c12c-4c57-b07f-db30526cd747",
"name": "Get Generated Documents",
"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": "Generate",
"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": "Prep For AI",
"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": "To Binary",
"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": "Export to Folder",
"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": "Get FileType",
"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": "Import File",
"type": "n8n-nodes-base.readWriteFile",
"position": [
320,
420
],
"parameters": {
"options": {},
"fileSelector": "={{ $json.path }}"
},
"typeVersion": 1
},
{
"id": "6ade93d5-61c3-450a-b78c-e210c18c0e70",
"name": "Extract from PDF",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
260
],
"parameters": {
"options": {},
"operation": "pdf"
},
"typeVersion": 1
},
{
"id": "f413e139-3f9c-438f-8e82-824c38f09c6b",
"name": "Extract from DOCX",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
420
],
"parameters": {
"options": {},
"operation": "ods"
},
"typeVersion": 1
},
{
"id": "455fadea-f5c7-4bea-983f-b06da4e57510",
"name": "Extract from TEXT",
"type": "n8n-nodes-base.extractFromFile",
"position": [
680,
580
],
"parameters": {
"options": {},
"operation": "text"
},
"typeVersion": 1
},
{
"id": "b2586011-4985-4075-b51c-90301b1a8cf9",
"name": "Summarization Chain",
"type": "@n8n/n8n-nodes-langchain.chainSummarization",
"position": [
1200,
260
],
"parameters": {
"options": {},
"chunkSize": 4000
},
"typeVersion": 2
},
{
"id": "1502e72c-e97e-4148-8138-01818ab5b104",
"name": "Sticky Note",
"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": "Sticky Note1",
"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": "Sticky Note2",
"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": "Sticky Note3",
"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": "Sticky Note4",
"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": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "7e216411-83ee-4b82-9e00-285d4f2d3224",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
-360,
80
],
"parameters": {
"width": 390.63004227317265,
"height": 401.0080676370763,
"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!"
},
"typeVersion": 1
},
{
"id": "f2c363d3-a2bf-4468-ad54-f26649ce6ab8",
"name": "Interview",
"type": "@n8n/n8n-nodes-langchain.chainLlm",
"position": [
2660,
680
],
"parameters": {
"text": "=## document summary\n {{ $('Prep For AI').item.json.summary }}",
"messages": {
"messageValues": [
{
"message": "=Given the following document summary, what questions would you ask to create a {{ $('For Each Doc Type...').item.json.title }} for the document? Generate 5 questions."
}
]
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 1.4
},
{
"id": "ce3da55d-8c22-40bb-8781-63c2e6bcb824",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
1960,
380
],
"parameters": {
"width": 172.26820279743384,
"height": 295.46359440513226,
"content": "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### \ud83d\udca1Add your own templates here!\n"
},
"typeVersion": 1
}
],
"connections": {
"2secs": {
"main": [
[
{
"node": "For Each Doc Type...",
"type": "main",
"index": 0
}
]
]
},
"Merge": {
"main": [
[
{
"node": "Prep For AI",
"type": "main",
"index": 0
}
]
]
},
"Discover": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Generate": {
"main": [
[
{
"node": "2secs",
"type": "main",
"index": 0
}
]
]
},
"Settings": {
"main": [
[
{
"node": "Import File",
"type": "main",
"index": 0
}
]
]
},
"Aggregate": {
"main": [
[
{
"node": "Generate",
"type": "main",
"index": 0
}
]
]
},
"Interview": {
"main": [
[
{
"node": "Split Out",
"type": "main",
"index": 0
}
]
]
},
"Split Out": {
"main": [
[
{
"node": "Discover",
"type": "main",
"index": 0
}
]
]
},
"To Binary": {
"main": [
[
{
"node": "Export to Folder",
"type": "main",
"index": 0
}
]
]
},
"Import File": {
"main": [
[
{
"node": "Get FileType",
"type": "main",
"index": 0
}
]
]
},
"Prep For AI": {
"main": [
[
{
"node": "Get Doc Types",
"type": "main",
"index": 0
}
]
]
},
"Get FileType": {
"main": [
[
{
"node": "Extract from PDF",
"type": "main",
"index": 0
}
],
[
{
"node": "Extract from DOCX",
"type": "main",
"index": 0
}
],
[
{
"node": "Extract from TEXT",
"type": "main",
"index": 0
}
]
]
},
"Get Doc Types": {
"main": [
[
{
"node": "Split Out Doc Types",
"type": "main",
"index": 0
}
]
]
},
"Extract from PDF": {
"main": [
[
{
"node": "Prep Incoming Doc",
"type": "main",
"index": 0
}
]
]
},
"Extract from DOCX": {
"main": [
[
{
"node": "Prep Incoming Doc",
"type": "main",
"index": 0
}
]
]
},
"Extract from TEXT": {
"main": [
[
{
"node": "Prep Incoming Doc",
"type": "main",
"index": 0
}
]
]
},
"Prep Incoming Doc": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
},
{
"node": "Summarization Chain",
"type": "main",
"index": 0
}
]
]
},
"Local File Trigger": {
"main": [
[
{
"node": "Settings",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 1
}
]
]
},
"Split Out Doc Types": {
"main": [
[
{
"node": "For Each Doc Type...",
"type": "main",
"index": 0
}
]
]
},
"Summarization Chain": {
"main": [
[
{
"node": "Merge",
"type": "main",
"index": 0
}
]
]
},
"For Each Doc Type...": {
"main": [
[
{
"node": "Get Generated Documents",
"type": "main",
"index": 0
}
],
[
{
"node": "Interview",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Discover",
"type": "ai_retriever",
"index": 0
}
]
]
},
"Get Generated Documents": {
"main": [
[
{
"node": "To Binary",
"type": "main",
"index": 0
}
]
]
},
"Item List Output Parser": {
"ai_outputParser": [
[
{
"node": "Interview",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Embeddings Mistral Cloud": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model": {
"ai_languageModel": [
[
{
"node": "Interview",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings Mistral Cloud1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model1": {
"ai_languageModel": [
[
{
"node": "Summarization Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model2": {
"ai_languageModel": [
[
{
"node": "Discover",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Mistral Cloud Chat Model3": {
"ai_languageModel": [
[
{
"node": "Generate",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
mistralCloudApiqdrantApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow enables you to process new files added to a local directory by automatically loading their content, splitting it into manageable chunks, and generating embeddings using Mistral Cloud for efficient semantic search or retrieval tasks. It's ideal for developers or data analysts handling document-heavy projects who need an event-driven system to trigger AI-powered analysis without manual intervention. The key step involves the recursive character text splitter, which breaks down the document into smaller segments before embedding them via Mistral Cloud, ensuring compatibility with downstream language model queries.
Use this workflow when you want real-time processing of incoming local files for AI applications like question-answering over documents, especially in low-volume setups where cloud uploads aren't necessary. Avoid it for high-throughput scenarios or non-text files, as it focuses on textual content and may require custom tweaks for images or binaries. Common variations include adding a vector store node for persistent embeddings or integrating with external APIs for enhanced output parsing.
About this workflow
Localfile Wait. Uses localFileTrigger, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, embeddingsMistralCloud. Event-driven trigger; 42 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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