This workflow follows the Chat Trigger → HTTP Request 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 →
{
"id": "5NAbfX550LJsfz6f",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Make OpenAI Citation for File Retrieval RAG",
"tags": [
{
"id": "urxRtGxxLObZWPvX",
"name": "sample",
"createdAt": "2024-09-13T02:43:13.014Z",
"updatedAt": "2024-09-13T02:43:13.014Z"
},
{
"id": "nMXS3c9l1WqDwWF5",
"name": "assist",
"createdAt": "2024-12-23T16:09:38.737Z",
"updatedAt": "2024-12-23T16:09:38.737Z"
}
],
"nodes": [
{
"id": "b9033511-3421-467a-9bfa-73af01b99c4f",
"name": "Aggregate",
"type": "n8n-nodes-base.aggregate",
"position": [
740,
120
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "a61dd9d3-4faa-4878-a6f3-ba8277279002",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
980,
-320
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "2daabca5-37ec-4cad-9157-29926367e1a7",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
220,
320
],
"parameters": {
"color": 3,
"width": 840,
"height": 80,
"content": "## Within N8N, there will be a chat button to test"
},
"typeVersion": 1
},
{
"id": "bf4485b1-cd94-41c8-a183-bf1b785f2761",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-440,
-520
],
"parameters": {
"color": 5,
"width": 500,
"height": 720,
"content": "## Make OpenAI Citation for File Retrieval RAG\n\n## Use case\n\nIn this example, we will ensure that all texts from the OpenAI assistant search for citations and sources in the vector store files. We can also format the output for Markdown or HTML tags.\n\nThis is necessary because the assistant sometimes generates strange characters, and we can also use dynamic references such as citations 1, 2, 3, for example.\n\n## What this workflow does\n\nIn this workflow, we will use an OpenAI assistant created within their interface, equipped with a vector store containing some files for file retrieval.\n\nThe assistant will perform the file search within the OpenAI infrastructure and will return the content with citations.\n\n- We will make an HTTP request to retrieve all the details we need to format the text output.\n\n## Setup\n\nInsert an OpenAI Key\n\n## How to adjust it to your needs\n\nAt the end of the workflow, we have a block of code that will format the output, and there we can add Markdown tags to create links. Optionally, we can transform the Markdown formatting into HTML.\n\n\nby Davi Saranszky Mesquita\nhttps://www.linkedin.com/in/mesquitadavi/"
},
"typeVersion": 1
},
{
"id": "539a4e40-9745-4a26-aba8-2cc2b0dd6364",
"name": "Create a simple Trigger to have the Chat button within N8N",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"notes": "https://www.npmjs.com/package/@n8n/chat",
"position": [
260,
-520
],
"parameters": {
"options": {}
},
"notesInFlow": true,
"typeVersion": 1.1
},
{
"id": "aa5b2951-df32-43ac-9939-83b02d818e73",
"name": "OpenAI Assistant with Vector Store",
"type": "@n8n/n8n-nodes-langchain.openAi",
"position": [
580,
-520
],
"parameters": {
"options": {
"preserveOriginalTools": false
},
"resource": "assistant",
"assistantId": {
"__rl": true,
"mode": "list",
"value": "asst_QAfdobVCVCMJz8LmaEC7nlId",
"cachedResultName": "Teste"
}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.7
},
{
"id": "1817b673-6cb3-49aa-9f38-a5876eb0e6fa",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
560,
-680
],
"parameters": {
"width": 300,
"content": "## Setup\n\n- Configure OpenAI Key\n\n### In this step, we will use an assistant created within the OpenAI platform that contains a vector store a.k.a file retrieval"
},
"typeVersion": 1
},
{
"id": "16429226-e850-4698-b419-fd9805a03fb7",
"name": "Get ALL Thread Content",
"type": "n8n-nodes-base.httpRequest",
"position": [
1260,
-520
],
"parameters": {
"url": "=https://api.openai.com/v1/threads/{{ $json.threadId }}/messages",
"options": {},
"sendHeaders": true,
"authentication": "predefinedCredentialType",
"headerParameters": {
"parameters": [
{
"name": "OpenAI-Beta",
"value": "assistants=v2"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "e8c88b08-5be2-4f7e-8b17-8cf804b3fe9f",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1160,
-620
],
"parameters": {
"content": "### Retrieving all thread content is necessary because the OpenAI tool does not retrieve all citations upon request."
},
"typeVersion": 1
},
{
"id": "0f51e09f-2782-4e2d-b797-d4d58fcabdaf",
"name": "Split all message iterations from a thread",
"type": "n8n-nodes-base.splitOut",
"position": [
220,
-300
],
"parameters": {
"options": {},
"fieldToSplitOut": "data"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "4d569993-1ce3-4b32-beaf-382feac25da9",
"name": "Split all content from a single message",
"type": "n8n-nodes-base.splitOut",
"position": [
460,
-300
],
"parameters": {
"options": {},
"fieldToSplitOut": "content"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "999e1c2b-1927-4483-aac1-6e8903f7ed25",
"name": "Split all citations from a single message",
"type": "n8n-nodes-base.splitOut",
"position": [
700,
-300
],
"parameters": {
"options": {},
"fieldToSplitOut": "text.annotations"
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "98af62f5-adb0-4e07-a146-fc2f13b851ce",
"name": "Retrieve file name from a file ID",
"type": "n8n-nodes-base.httpRequest",
"onError": "continueRegularOutput",
"position": [
220,
120
],
"parameters": {
"url": "=https://api.openai.com/v1/files/{{ $json.file_citation.file_id }}",
"options": {},
"sendQuery": true,
"authentication": "predefinedCredentialType",
"queryParameters": {
"parameters": [
{
"name": "limit",
"value": "1"
}
]
},
"nodeCredentialType": "openAiApi"
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 4.2,
"alwaysOutputData": true
},
{
"id": "b11f0d3d-bdc4-4845-b14b-d0b0de214f01",
"name": "Regularize output",
"type": "n8n-nodes-base.set",
"position": [
480,
120
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "2dcaafee-5037-4a97-942a-bcdd02bc2ad9",
"name": "id",
"type": "string",
"value": "={{ $json.id }}"
},
{
"id": "b63f967d-ceea-4aa8-98b9-91f5ab21bfe8",
"name": "filename",
"type": "string",
"value": "={{ $json.filename }}"
},
{
"id": "f611e749-054a-441d-8610-df8ba42de2e1",
"name": "text",
"type": "string",
"value": "={{ $('Split all citations from a single message').item.json.text }}"
}
]
}
},
"typeVersion": 3.4,
"alwaysOutputData": true
},
{
"id": "0e999a0e-76ed-4897-989b-228f075e9bfb",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
440,
-60
],
"parameters": {
"width": 200,
"height": 220,
"content": "### A file retrieval request contains a lot of information, and we want only the text that will be substituted and the file name.\n\n- id\n- filename\n- text\n"
},
"typeVersion": 1
},
{
"id": "53c79a6c-7543-435f-b40e-966dff0904d4",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
700,
-60
],
"parameters": {
"width": 200,
"height": 220,
"content": "### With the last three splits, we may have many citations and texts to substitute. By doing an aggregation, it will be possible to handle everything as a single request."
},
"typeVersion": 1
},
{
"id": "381fb6d6-64fc-4668-9d3c-98aaa43a45ca",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
960,
-60
],
"parameters": {
"height": 220,
"content": "### This simple code will take all the previous files and citations and alter the original text, formatting the output. In this way, we can use Markdown tags to create links, or if you prefer, we can add an HTML transformation node."
},
"typeVersion": 1
},
{
"id": "d0cbb943-57ab-4850-8370-1625610a852a",
"name": "Optional Markdown to HTML",
"type": "n8n-nodes-base.markdown",
"disabled": true,
"position": [
1220,
120
],
"parameters": {
"html": "={{ $json.output }}",
"options": {},
"destinationKey": "output"
},
"typeVersion": 1
},
{
"id": "589e2418-5dec-47d0-ba08-420d84f09da7",
"name": "Finnaly format the output",
"type": "n8n-nodes-base.code",
"position": [
980,
120
],
"parameters": {
"mode": "runOnceForEachItem",
"jsCode": "let saida = $('OpenAI Assistant with Vector Store').item.json.output;\n\nfor (let i of $input.item.json.data) {\n saida = saida.replaceAll(i.text, \" _(\"+ i.filename+\")_ \");\n}\n\n$input.item.json.output = saida;\nreturn $input.item;"
},
"typeVersion": 2
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "0e621a5a-d99d-4db3-9ae4-ea98c31467e9",
"connections": {
"Aggregate": {
"main": [
[
{
"node": "Finnaly format the output",
"type": "main",
"index": 0
}
]
]
},
"Regularize output": {
"main": [
[
{
"node": "Aggregate",
"type": "main",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "OpenAI Assistant with Vector Store",
"type": "ai_memory",
"index": 0
}
]
]
},
"Get ALL Thread Content": {
"main": [
[
{
"node": "Split all message iterations from a thread",
"type": "main",
"index": 0
}
]
]
},
"Finnaly format the output": {
"main": [
[
{
"node": "Optional Markdown to HTML",
"type": "main",
"index": 0
}
]
]
},
"Retrieve file name from a file ID": {
"main": [
[
{
"node": "Regularize output",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Assistant with Vector Store": {
"main": [
[
{
"node": "Get ALL Thread Content",
"type": "main",
"index": 0
}
]
]
},
"Split all content from a single message": {
"main": [
[
{
"node": "Split all citations from a single message",
"type": "main",
"index": 0
}
]
]
},
"Split all citations from a single message": {
"main": [
[
{
"node": "Retrieve file name from a file ID",
"type": "main",
"index": 0
}
]
]
},
"Split all message iterations from a thread": {
"main": [
[
{
"node": "Split all content from a single message",
"type": "main",
"index": 0
}
]
]
},
"Create a simple Trigger to have the Chat button within N8N": {
"main": [
[
{
"node": "OpenAI Assistant with Vector Store",
"type": "main",
"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.
openAiApi
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How this works
This workflow enables seamless retrieval-augmented generation (RAG) for your documents, allowing you to query uploaded files with accurate, context-aware responses from OpenAI while automatically generating citations to verify sources. It's ideal for researchers, content creators, or teams handling knowledge bases who need reliable AI assistance without manual fact-checking. The key step involves the OpenAI Assistant with Vector Store node, which processes file embeddings and delivers cited outputs, enhanced by a chat trigger for intuitive interaction and memory buffer for conversation history.
Use this when building a chat-based Q&A system over specific documents, such as internal reports or research papers, where traceability matters. Avoid it for real-time data queries or non-text files like images, as it focuses on static retrieval. Common variations include adding HTTP Request nodes to fetch external files dynamically or integrating multiple vector stores for broader knowledge sources.
About this workflow
Make OpenAI Citation for File Retrieval RAG. Uses memoryBufferWindow, stickyNote, chatTrigger, openAi. Chat trigger; 19 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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