InfyOm Technologies This workflow corresponds to n8n.io template #6212 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger 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": "klsAKNm0Q07J8RXY",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Build RAG Chatbot for Your Website Using Supabase",
"tags": [],
"nodes": [
{
"id": "c2c34da8-27e8-4881-a3fd-3b5f6987e0bf",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
500,
-580
],
"parameters": {
"mode": "webhook",
"public": true,
"options": {}
},
"typeVersion": 1.1
},
{
"id": "31f5d600-1694-4d50-a581-0880f65a7755",
"name": "HTML Extract",
"type": "n8n-nodes-base.htmlExtract",
"position": [
-460,
-540
],
"parameters": {
"options": {},
"extractionValues": {
"values": [
{
"key": "title",
"cssSelector": "title, h1"
},
{
"key": "content",
"cssSelector": "p, article, .content, .post-content, main"
},
{
"key": "meta_description",
"attribute": "content",
"cssSelector": "meta[name='description']",
"returnValue": "attribute"
},
{
"key": "links",
"attribute": "href",
"cssSelector": "a[href]",
"returnValue": "attribute"
}
]
}
},
"typeVersion": 1
},
{
"id": "d48d0d67-cfb6-4058-96e2-4198b40650bc",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
20,
-360
],
"parameters": {
"loader": "jsonLoader",
"options": {
"metadata": {
"metadataValues": [
{
"name": "website_url",
"value": "={{ $('Enter Website Url').item.json['Website Url'] }}"
}
]
}
},
"dataType": "binary"
},
"typeVersion": 1
},
{
"id": "32b79786-e8d1-4083-80e9-4d2eada2c46b",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
120,
-160
],
"parameters": {
"options": {},
"chunkSize": 3000,
"chunkOverlap": 500
},
"typeVersion": 1
},
{
"id": "b7541584-131f-4868-b8dd-053ab509ab1b",
"name": "Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
-40,
-540
],
"parameters": {
"mode": "insert",
"options": {},
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
},
"embeddingBatchSize": 100
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "7b923d0f-c803-4b3c-afd8-ee680793c955",
"name": "Enter Website Url",
"type": "n8n-nodes-base.formTrigger",
"position": [
-820,
-540
],
"parameters": {
"options": {},
"formTitle": "Form",
"formFields": {
"values": [
{
"fieldLabel": "Website Url",
"placeholder": "Enter Website Url"
}
]
},
"formDescription": "Enter Website Url"
},
"typeVersion": 2.2
},
{
"id": "9e961fc5-b937-4f0e-b9f5-8ada4f3598f5",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-180,
-680
],
"parameters": {
"color": 3,
"width": 520,
"height": 100,
"content": "## Generating Embeddings from Website Content"
},
"typeVersion": 1
},
{
"id": "7f4feec8-62a8-47a0-a8d6-9bce01261b1a",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
380,
-680
],
"parameters": {
"width": 300,
"height": 80,
"content": "## User-Initiated Question"
},
"typeVersion": 1
},
{
"id": "1c11199e-d217-4f2d-8afe-913f902ef47c",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
700,
-680
],
"parameters": {
"color": 3,
"width": 540,
"height": 80,
"content": "## Vector-Based Answer Retrieval"
},
"typeVersion": 1
},
{
"id": "ddb37d99-4e8d-4eef-83da-6b3088360062",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-840,
-680
],
"parameters": {
"width": 640,
"height": 100,
"content": "## Website Data Extraction\n\n"
},
"typeVersion": 1
},
{
"id": "eeffcae9-2e9c-48f2-ad13-2b75b7218fe2",
"name": "Website Data Scrapping",
"type": "n8n-nodes-base.httpRequest",
"maxTries": 3,
"position": [
-640,
-540
],
"parameters": {
"url": "={{ $json['Website Url'] }}",
"options": {
"timeout": 30000,
"redirect": {
"redirect": {
"maxRedirects": 5
}
}
}
},
"retryOnFail": true,
"typeVersion": 4.1
},
{
"id": "df8e6032-41f2-4f95-8906-7150ce565d8a",
"name": "Convert to File",
"type": "n8n-nodes-base.convertToFile",
"position": [
-320,
-540
],
"parameters": {
"options": {},
"operation": "toJson"
},
"typeVersion": 1.1
},
{
"id": "697c29c6-6bcd-4748-a874-3c754f9b034e",
"name": "Embeddings Cohere",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
-100,
-360
],
"parameters": {},
"credentials": {
"cohereApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "d8d3a79a-1ac2-42dd-b383-e39bbcbded75",
"name": "Question & Answer Retrieve",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
820,
-580
],
"parameters": {
"options": {
"systemMessage": "=# Overview \nYou are an AI agent that responds strictly using information from stored document embeddings in a supabase vector store. \n\n## Context \n- The agent is designed to provide accurate, document-based answers. \n- It must avoid using external knowledge or assumptions. \n\n## Instructions \n1. Analyze the user's question. \n2. Retrieve relevant content from the document embeddings using supabase. \n3. Construct a response only from the matching document chunks.\n## Tools \n- Vector store containing embedded documents\n- Embeddings Cohere \n\n## SOP (Standard Operating Procedure) \n1. Receive user query. \n2. Search the vector store for relevant document chunks. \n3. Extract and synthesize an answer using only that content. \n4. If no matches, issue fallback message. \n\n## Final Notes \n- Do not rely on prior training or external facts. \n- The response must remain strictly grounded in the documents. "
}
},
"typeVersion": 1.8
},
{
"id": "65d51d86-cae3-4cdb-9d95-025962195953",
"name": "Data From Supabase Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase",
"position": [
1000,
-360
],
"parameters": {
"mode": "retrieve-as-tool",
"topK": null,
"options": {},
"toolName": "documents_knowledge_base",
"tableName": {
"__rl": true,
"mode": "list",
"value": "documents",
"cachedResultName": "documents"
},
"toolDescription": "work with documents data in Supabase vector store",
"includeDocumentMetadata": false
},
"credentials": {
"supabaseApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "6ef28803-e21f-4733-a6a9-d894b97dbb35",
"name": "Embeddings With Cohere",
"type": "@n8n/n8n-nodes-langchain.embeddingsCohere",
"position": [
1020,
-140
],
"parameters": {},
"credentials": {
"cohereApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "4796d1f3-ab76-4ed4-86b3-96bca2aae4e9",
"name": "Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryPostgresChat",
"position": [
900,
-320
],
"parameters": {
"tableName": "chat_memory",
"contextWindowLength": 3
},
"credentials": {
"postgres": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "2ce21a19-e586-4cc6-88ed-b1957b04a06a",
"name": "OpenAI Chat Modell",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
760,
-340
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "1fe65722-2127-4e4c-8af6-d8c967259653",
"connections": {
"Chat Memory": {
"ai_memory": [
[
{
"node": "Question & Answer Retrieve",
"type": "ai_memory",
"index": 0
}
]
]
},
"HTML Extract": {
"main": [
[
{
"node": "Convert to File",
"type": "main",
"index": 0
}
]
]
},
"Convert to File": {
"main": [
[
{
"node": "Supabase Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Cohere": {
"ai_embedding": [
[
{
"node": "Supabase Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Enter Website Url": {
"main": [
[
{
"node": "Website Data Scrapping",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Modell": {
"ai_languageModel": [
[
{
"node": "Question & Answer Retrieve",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Supabase Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Embeddings With Cohere": {
"ai_embedding": [
[
{
"node": "Data From Supabase Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Website Data Scrapping": {
"main": [
[
{
"node": "HTML Extract",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question & Answer Retrieve",
"type": "main",
"index": 0
}
]
]
},
"Data From Supabase Vector Store": {
"ai_tool": [
[
{
"node": "Question & Answer Retrieve",
"type": "ai_tool",
"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.
cohereApiopenAiApipostgressupabaseApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Many websites lack a smart, searchable interface. Visitors often leave due to unanswered questions. This workflow transforms any website into a Retrieval-Augmented Generation (RAG) chatbot—automatically extracting content, creating embeddings, and enabling real-time,…
Source: https://n8n.io/workflows/6212/ — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow acts as a 24/7 sales agent, engaging leads across WhatsApp, Instagram, Facebook, Telegram, and your website. It intelligently transcribes audio messages, answers questions using a knowle
Chat Trigger, Memory Postgres Chat, Tool Workflow +20
• Create a Google Drive folder to watch. • Connect your Google Drive account in n8n and authorize access. • Point the Google Drive Trigger node to this folder (new/modified files trigger the flow).
Agent, Chat Trigger, Memory Buffer Window +14
The workflow operates through a three-step process that handles incoming chat messages with intelligent tool orchestration: Message Trigger: The node triggers whenever a user message arrives and passe
Chat Trigger, Memory Postgres Chat, OpenAI Embeddings +16
Advanced Ai Demo Presented At Ai Developers 14 Meetup. Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.
Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14
Advanced Ai Demo (Presented At Ai Developers #14 Meetup). Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.
Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14