This workflow follows the Chainretrievalqa → Retrievervectorstore 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": "TBiW9x7O4ijo4yOX",
"name": "Bitrix24 Open Chanel RAG Chatbot Application Workflow example with Webhook Integration",
"tags": [
{
"id": "2ziILYLz4IbTkFf5",
"name": "Tech demo",
"createdAt": "2025-02-17T08:43:26.445Z",
"updatedAt": "2025-02-17T08:43:26.445Z"
},
{
"id": "BedOB2iRpKR26bcZ",
"name": "Chatbot",
"createdAt": "2025-02-17T08:43:26.436Z",
"updatedAt": "2025-02-17T08:43:26.436Z"
},
{
"id": "DvSHJwHwuObn0cxx",
"name": "Open Channels",
"createdAt": "2025-03-04T07:27:28.499Z",
"updatedAt": "2025-03-04T07:27:28.499Z"
},
{
"id": "YJcjKoBRFN1HXH5e",
"name": "Bitrix24",
"createdAt": "2025-02-17T08:43:26.424Z",
"updatedAt": "2025-02-17T08:43:26.424Z"
}
],
"nodes": [
{
"id": "dbd7b2c0-2b27-4c23-beb7-eec128da0787",
"name": "Bitrix24 Handler",
"type": "n8n-nodes-base.webhook",
"position": [
-1280,
620
],
"parameters": {
"path": "bitrix24/openchannel-rag-bothandler.php",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 1
},
{
"id": "0ead4d82-4d9b-4392-af4c-2c315068b983",
"name": "Credentials",
"type": "n8n-nodes-base.set",
"position": [
-1040,
620
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "030f8f90-2669-4c20-9eab-c572c4b7c70c",
"name": "CLIENT_ID",
"type": "string",
"value": "local.67c8f9e81cb353.30162021"
},
{
"id": "de9bbb7a-b782-4540-b259-527625db8490",
"name": "CLIENT_SECRET",
"type": "string",
"value": "Db5943DCy4JhYq4oU0yNb21Hx8WimQeThczOYk03uJrVroc8R4"
},
{
"id": "86b7aff7-1e25-4b12-a366-23cf34e5a405",
"name": "application_token",
"type": "string",
"value": "={{ $json.body['auth[application_token]'] }}"
},
{
"id": "69bbcb1f-ba6e-42eb-be8a-ee0707ce997d",
"name": "domain",
"type": "string",
"value": "={{ $json.body['auth[domain]'] }}\n"
},
{
"id": "dc1b0515-f06a-4731-b0dc-912a8d04e56b",
"name": "access_token",
"type": "string",
"value": "={{ $json.body['auth[access_token]'] }}"
},
{
"id": "94fdeed8-9437-417e-9c2a-fa853620a340",
"name": "storageName",
"type": "string",
"value": "Shared drive"
},
{
"id": "8564e421-dfce-437c-a7c3-ac6a180594b8",
"name": "folderName",
"type": "string",
"value": "Open line chat bot documents"
}
]
},
"includeOtherFields": true
},
"typeVersion": 3.4
},
{
"id": "67b225b8-c2c2-4570-81cb-4c533ae75465",
"name": "Validate Token",
"type": "n8n-nodes-base.if",
"position": [
-820,
620
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "or",
"conditions": [
{
"id": "da73d0ba-6eeb-405e-89fe-9d041fd2e0cd",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.CLIENT_ID }}",
"rightValue": "={{ $json.application_token }}"
},
{
"id": "4ba90f7b-0299-4097-9ae7-6e4dee428a74",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "1",
"rightValue": "1"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "4fee1441-4e30-4070-b596-15e121ca7320",
"name": "Route Event",
"type": "n8n-nodes-base.switch",
"position": [
-620,
520
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "ONIMBOTMESSAGEADD",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event }}",
"rightValue": "ONIMBOTMESSAGEADD"
}
]
},
"renameOutput": true
},
{
"outputKey": "ONIMBOTJOINCHAT",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "e9125f57-129e-4026-86ff-746d40b92b04",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event }}",
"rightValue": "ONIMBOTJOINCHAT"
}
]
},
"renameOutput": true
},
{
"outputKey": "ONAPPINSTALL",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "2db7bed5-fd88-4900-b8d2-e27b49c2fcca",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event }}",
"rightValue": "ONAPPINSTALL"
}
]
},
"renameOutput": true
},
{
"outputKey": "ONIMBOTDELETE",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "b708d339-fd46-470d-b0d5-ff2eb405f5ce",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.body.event }}",
"rightValue": "ONIMBOTDELETE"
}
]
},
"renameOutput": true
}
]
},
"options": {}
},
"typeVersion": 3.2
},
{
"id": "c21f6d64-0543-4958-9f64-501dce37893f",
"name": "Process Message",
"type": "n8n-nodes-base.function",
"position": [
-420,
400
],
"parameters": {
"functionCode": "// Process Message Node\nconst items = $input.all();\nconst item = items[0];\n\n// Get message data from the correct path\nconst message = item.json.body['data[PARAMS][MESSAGE]'];\nconst dialogId = item.json.body['data[PARAMS][DIALOG_ID]'];\n\nconst sessionId = item.json.body['data[PARAMS][CHAT_ENTITY_DATA_1]'].split(\"|\")[5];\n\nconst botId = Object.keys(item.json.body)\n .filter(key => key.startsWith(\"data[BOT]\") && key.endsWith(\"[BOT_ID]\"))\n .map(key => $json.body[key])\n .shift() || null;\nconst userId = item.json.body['data[USER][ID]'];\n\n// Get auth data\nconst auth = {\n access_token: item.json.access_token,\n domain: item.json.domain\n};\n\nif (message.toLowerCase() === \"what's hot\") {\n return {\n json: {\n DIALOG_ID: dialogId,\n SESSION_ID: sessionId,\n BOT_ID: botId,\n USER_ID: userId,\n MESSAGE_ORI: message,\n MESSAGE: \"Hi! I am an example-bot.\\nI repeat what you say\",\n AUTH: auth.access_token,\n DOMAIN: auth.domain\n }\n };\n} else {\n return {\n json: {\n DIALOG_ID: dialogId,\n SESSION_ID: sessionId,\n BOT_ID: botId,\n USER_ID: userId,\n MESSAGE_ORI: message,\n MESSAGE: `You said:\\n${message}`,\n AUTH: auth.access_token,\n DOMAIN: auth.domain\n }\n };\n}"
},
"typeVersion": 1
},
{
"id": "06a59835-2999-44b6-81bd-0601e5570113",
"name": "Process Join",
"type": "n8n-nodes-base.function",
"position": [
-420,
780
],
"parameters": {
"functionCode": "// Process Join Node\nconst items = $input.all();\nconst item = items[0];\n\n// Get dialog ID from the correct path\nconst dialogId = item.json.body['data[PARAMS][DIALOG_ID]'];\n\n// Get auth data\nconst auth = {\n access_token: item.json.access_token,\n domain: item.json.domain\n};\n\nconst message = \n 'ITR Menu:\\n' +\n '[send=1]1. find out more about me[/send]\\n' +\n '[send=0]0. wait for operator response[/send]';\n\nreturn {\n json: {\n DIALOG_ID: dialogId,\n MESSAGE: message,\n AUTH: auth.access_token,\n DOMAIN: auth.domain\n }\n};"
},
"typeVersion": 1
},
{
"id": "40accf33-c217-497c-8172-6106eb15800f",
"name": "Process Install",
"type": "n8n-nodes-base.function",
"position": [
-420,
940
],
"parameters": {
"functionCode": "// Process Install Node\nconst items = $input.all();\nconst item = items[0];\n\n// Get the webhook URL from input\nconst handlerBackUrl = item.json.webhookUrl;\n\n// Get auth data directly from item.json\nconst auth = {\n access_token: item.json.access_token,\n application_token: item.json.application_token,\n domain: item.json.domain\n};\n\nreturn {\n json: {\n handler_back_url: handlerBackUrl,\n CODE: 'OpenChanelExampleBot',\n TYPE: 'O',\n OPENLINE: 'Y',\n EVENT_MESSAGE_ADD: handlerBackUrl,\n EVENT_WELCOME_MESSAGE: handlerBackUrl,\n EVENT_BOT_DELETE: handlerBackUrl,\n PROPERTIES: {\n NAME: 'Open chanel Bot',\n LAST_NAME: 'Example',\n COLOR: 'AQUA',\n EMAIL: 'no@example.com',\n PERSONAL_BIRTHDAY: '2020-07-18',\n WORK_POSITION: 'Report on affairs',\n PERSONAL_GENDER: 'M'\n },\n // Use the auth data from item.json\n AUTH: auth.access_token,\n CLIENT_ID: auth.application_token,\n DOMAIN: auth.domain\n }\n};"
},
"typeVersion": 1
},
{
"id": "22a7c363-ab5a-4adc-9de4-268f3f3739f3",
"name": "Register Bot",
"type": "n8n-nodes-base.httpRequest",
"position": [
-220,
940
],
"parameters": {
"url": "=https://{{ $json.DOMAIN }}/rest/imbot.register?auth={{$json.AUTH}}",
"method": "POST",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "CODE",
"value": "OpenChanelExampleBot"
},
{
"name": "TYPE",
"value": "O"
},
{
"name": "EVENT_MESSAGE_ADD",
"value": "={{$json.handler_back_url}}"
},
{
"name": "EVENT_WELCOME_MESSAGE",
"value": "={{$json.handler_back_url}}"
},
{
"name": "EVENT_BOT_DELETE",
"value": "={{$json.handler_back_url}}"
},
{
"name": "PROPERTIES",
"value": "={{ {\n NAME: 'Bot',\n LAST_NAME: 'Example',\n COLOR: 'AQUA',\n EMAIL: 'no@example.com',\n PERSONAL_BIRTHDAY: '2020-07-18',\n WORK_POSITION: 'Report on affairs',\n PERSONAL_GENDER: 'M'\n} }}"
},
{
"name": "CLIENT_ID",
"value": "={{ $json.CLIENT_ID }}"
},
{
"name": "CLIENT_SECRET",
"value": "={{ $json.AUTH }}"
},
{
"name": "OPENLINE",
"value": "Y"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "e728c7eb-b169-4757-b4f4-b99ec4db0184",
"name": "Send Message",
"type": "n8n-nodes-base.httpRequest",
"position": [
740,
420
],
"parameters": {
"url": "=https://{{ $json.data.DOMAIN }}/rest/imbot.message.add?auth={{ $json.data.AUTH }}",
"method": "POST",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "DIALOG_ID",
"value": "={{ $json.data.DIALOG_ID }}"
},
{
"name": "MESSAGE",
"value": "={{ $json.data.MESSAGE }}"
},
{
"name": "AUTH",
"value": "={{ $json.data.AUTH }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "1a1fae0e-d74f-48b9-8ec8-4e926763de28",
"name": "Send Join Message",
"type": "n8n-nodes-base.httpRequest",
"position": [
-220,
780
],
"parameters": {
"url": "=https://{{$json.DOMAIN}}/rest/imbot.message.add?auth={{$json.AUTH}}",
"method": "POST",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "DIALOG_ID",
"value": "={{ $json.DIALOG_ID }}"
},
{
"name": "MESSAGE",
"value": "={{ $json.MESSAGE }}"
},
{
"name": "AUTH",
"value": "={{ $json.AUTH }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "f377d8eb-2a90-4ca5-8bd8-122c8df2ced3",
"name": "Process Delete",
"type": "n8n-nodes-base.noOp",
"position": [
-420,
1100
],
"parameters": {},
"typeVersion": 1
},
{
"id": "faa4c61e-faf4-4bd7-b096-706d3c5cf366",
"name": "Success Response",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
1200,
700
],
"parameters": {
"options": {
"responseCode": 200
},
"respondWith": "json",
"responseBody": "={\n \"result\": true\n}"
},
"typeVersion": 1.1
},
{
"id": "ade154b4-64d9-4ecd-8a83-328002c98569",
"name": "Error Response",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
-820,
780
],
"parameters": {
"options": {
"responseCode": 401
},
"respondWith": "json",
"responseBody": "={{\n \"result\": false,\n \"error\": \"Invalid application token\"\n}}"
},
"typeVersion": 1.1
},
{
"id": "a5866396-3a25-4bbf-81b2-56a8d35fc63b",
"name": "Merge parameters for Subworkflow",
"type": "n8n-nodes-base.merge",
"position": [
-180,
1340
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineAll"
},
"typeVersion": 3
},
{
"id": "feb3e1c0-4556-4d77-9e5e-42eb2c10a5f8",
"name": "Get a list of available storages",
"type": "n8n-nodes-base.httpRequest",
"position": [
-860,
2080
],
"parameters": {
"url": "=https://{{ $json.domain }}/rest/disk.storage.getlist.json?auth={{ $json.access_token }}",
"method": "POST",
"options": {},
"jsonBody": "={\n\"filter\": {\n\t\t\t\t\"ENTITY_TYPE\": \"common\",\n\t\t\t\t\"%NAME\": \"{{ $json.storageName }}\"\n }\n}\n",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "7bb0412e-57ab-4a63-aec2-5db64b83ef7e",
"name": "Get a list of List of Files and Folders",
"type": "n8n-nodes-base.httpRequest",
"position": [
-640,
2080
],
"parameters": {
"url": "=https://{{ $('Execute Workflow Trigger').item.json.domain }}/rest/disk.storage.getchildren.json?auth={{ $('Execute Workflow Trigger').item.json.access_token }}",
"method": "POST",
"options": {},
"jsonBody": "={\n\"id\": {{ $json.result[0].ID }},\n\"filter\": {\n\t\t\t\t\"TYPE\": \"folder\",\n\t\t\t\t\"%NAME\": \"{{ $('Execute Workflow Trigger').item.json.folderName }}\"\n }\n}\n",
"sendBody": true,
"specifyBody": "json"
},
"typeVersion": 4.2
},
{
"id": "5528a3be-375a-4077-a346-2eb77cf9160f",
"name": "Get a list of Folders files",
"type": "n8n-nodes-base.httpRequest",
"position": [
-420,
2080
],
"parameters": {
"url": "=https://{{ $('Execute Workflow Trigger').item.json.domain }}/rest/disk.folder.getchildren.json?auth={{ $('Execute Workflow Trigger').item.json.access_token }}",
"method": "POST",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "id",
"value": "={{ $json.result[0].ID }}"
}
]
}
},
"typeVersion": 4.2
},
{
"id": "f713eda1-f51e-484d-a98a-f359bc7ce654",
"name": "Download file",
"type": "n8n-nodes-base.httpRequest",
"position": [
280,
2080
],
"parameters": {
"url": "={{ $json.DOWNLOAD_URL }}",
"options": {}
},
"typeVersion": 4.2
},
{
"id": "3b2c67ea-bb6c-49c8-b55f-ffde7d1d8e83",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
700,
2280
],
"parameters": {
"loader": "pdfLoader",
"options": {
"splitPages": true
},
"dataType": "binary"
},
"typeVersion": 1
},
{
"id": "0b5c2963-3692-469d-a1ce-66fe598dc25f",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
840,
2460
],
"parameters": {
"options": {},
"chunkOverlap": 100
},
"typeVersion": 1
},
{
"id": "52bfdf6b-0fb1-4e85-b31d-ec6c9ef912d8",
"name": "Split Out folder files and folders",
"type": "n8n-nodes-base.splitOut",
"position": [
-180,
2080
],
"parameters": {
"options": {},
"fieldToSplitOut": "result"
},
"typeVersion": 1
},
{
"id": "fd53fca7-d11d-4254-be56-9a62b6f0fadf",
"name": "Filter for files",
"type": "n8n-nodes-base.filter",
"position": [
40,
2080
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "6e68a8be-c155-41c7-ace4-bf76bfd362fc",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.TYPE }}",
"rightValue": "file"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "748a65d3-dceb-4787-a068-b364371b392b",
"name": "Move files to Vector stored folder",
"type": "n8n-nodes-base.httpRequest",
"position": [
520,
1860
],
"parameters": {
"url": "=https://{{ $('Execute Workflow Trigger').item.json.domain }}/rest/disk.file.moveto.json?auth={{ $('Execute Workflow Trigger').item.json.access_token }}",
"method": "POST",
"options": {},
"sendBody": true,
"bodyParameters": {
"parameters": [
{
"name": "id",
"value": "={{ $json.ID }}"
},
{
"name": "targetFolderId",
"value": "={{ $('Get a list of Folders files').item.json.result[0].ID }}"
}
]
}
},
"executeOnce": false,
"typeVersion": 4.2
},
{
"id": "47f3aec5-c3cb-4d3e-97bc-8b708ccc0db5",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-1080,
2080
],
"parameters": {},
"typeVersion": 1
},
{
"id": "9d3eb788-96cf-4c01-af5f-2beb1f6fa7b8",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1160,
1780
],
"parameters": {
"width": 2168.7691983135305,
"height": 818.1434255918864,
"content": "Subworkflow for Register Bot\nHere are files vector stored for Open line chanel bot\nAfter files are stored they are moved to subfolder"
},
"typeVersion": 1
},
{
"id": "91c26264-a61d-426c-a044-e2b287e54de0",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
580,
2080
],
"parameters": {
"mode": "insert",
"options": {
"collectionConfig": ""
},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "bitrix-docs",
"cachedResultName": "bitrix-docs"
}
},
"typeVersion": 1
},
{
"id": "0dd69952-402e-4d9e-a44f-7cf96ab4055e",
"name": "Embeddings Ollama",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
500,
2280
],
"parameters": {
"model": "nomic-embed-text:latest"
},
"typeVersion": 1
},
{
"id": "fcfbfe53-da04-48f2-9a62-204a8f1f06a8",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
140,
240
],
"parameters": {
"topK": 10
},
"typeVersion": 1
},
{
"id": "22d3be40-b74a-453f-8f52-8974b1527d49",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
0,
0
],
"parameters": {
"text": "={{ $json.MESSAGE_ORI }}",
"options": {
"systemPromptTemplate": "=Use the following pieces of context to answer the user's question.\nIf you don't know the answer, just say that you don't know, don't try to make up an answer.\n\n----------------\n{context}\n\nYour response must contain **only** the following key-value pairs:\n- `\"DIALOG_ID\"`: **Use exactly** this value from the input: `{{ $json.DIALOG_ID }}`\n- `\"AUTH\"`: **Use exactly** this value from the input: `{{ $json.AUTH }}`\n- `\"DOMAIN\"`: **Use exactly** this value from the input: `{{ $json.DOMAIN }}`\n- `\"MESSAGE\"`: **Your AI-generated response**, based on the conversation history and the user's input.\n\n**Do not modify** the values of `\"DIALOG_ID\"`, `\"AUTH\"`, or `\"DOMAIN\"`. They must remain exactly as received from the input. \nThe `\"MESSAGE\"` field must contain a relevant and clear response.\n\nIf the user asks **\"find out more about me\"**, respond with: \n*\"I am a Retrieval-Augmented Generation (RAG) system that answers questions based on uploaded documents and provided context.\"*"
},
"promptType": "define"
},
"typeVersion": 1.4
},
{
"id": "fb19f0b5-72cb-4ccd-ac0e-75ea3a32c9cc",
"name": "Prepare output parameters",
"type": "n8n-nodes-base.set",
"position": [
440,
60
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "ef09b5f8-2111-4731-8317-e338885a10c3",
"name": "data",
"type": "object",
"value": "={{ $json.response.text.removeMarkdown().replace(/`+$/, '')}}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "52429ac1-d738-49d7-81a8-725df6587312",
"name": "Embeddings Ollama1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOllama",
"position": [
360,
600
],
"parameters": {
"model": "nomic-embed-text:latest"
},
"typeVersion": 1
},
{
"id": "bade110b-d252-4633-9ecf-44a42772b8d5",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
-60,
300
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"typeVersion": 1
},
{
"id": "41eb5179-6195-485b-848b-46bb997de38e",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
160,
420
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "bitrix-docs",
"cachedResultName": "bitrix-docs"
}
},
"typeVersion": 1
},
{
"id": "484cbc38-4c6f-4d3d-9409-b04df2c7e102",
"name": "Execute subworkflow",
"type": "n8n-nodes-base.executeWorkflow",
"position": [
200,
1340
],
"parameters": {
"options": {},
"workflowId": {
"__rl": true,
"mode": "id",
"value": "={{ $workflow.id }}"
}
},
"typeVersion": 1.1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "e3e24337-997c-4ce2-b8c1-3e6f8b9eb85c",
"connections": {
"Credentials": {
"main": [
[
{
"node": "Validate Token",
"type": "main",
"index": 0
},
{
"node": "Merge parameters for Subworkflow",
"type": "main",
"index": 1
}
]
]
},
"Route Event": {
"main": [
[
{
"node": "Process Message",
"type": "main",
"index": 0
}
],
[
{
"node": "Process Join",
"type": "main",
"index": 0
}
],
[
{
"node": "Process Install",
"type": "main",
"index": 0
}
],
[
{
"node": "Process Delete",
"type": "main",
"index": 0
}
]
]
},
"Process Join": {
"main": [
[
{
"node": "Send Join Message",
"type": "main",
"index": 0
}
]
]
},
"Register Bot": {
"main": [
[
{
"node": "Success Response",
"type": "main",
"index": 0
},
{
"node": "Merge parameters for Subworkflow",
"type": "main",
"index": 0
}
]
]
},
"Send Message": {
"main": [
[
{
"node": "Success Response",
"type": "main",
"index": 0
}
]
]
},
"Download file": {
"main": [
[
{
"node": "Move files to Vector stored folder",
"type": "main",
"index": 0
},
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Process Delete": {
"main": [
[
{
"node": "Success Response",
"type": "main",
"index": 0
}
]
]
},
"Validate Token": {
"main": [
[
{
"node": "Route Event",
"type": "main",
"index": 0
}
],
[
{
"node": "Error Response",
"type": "main",
"index": 0
}
]
]
},
"Process Install": {
"main": [
[
{
"node": "Register Bot",
"type": "main",
"index": 0
}
]
]
},
"Process Message": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Bitrix24 Handler": {
"main": [
[
{
"node": "Credentials",
"type": "main",
"index": 0
}
]
]
},
"Filter for files": {
"main": [
[
{
"node": "Download file",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Ollama": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Send Join Message": {
"main": [
[
{
"node": "Success Response",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Ollama1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Execute subworkflow": {
"main": [
[
{
"node": "Success Response",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"Execute Workflow Trigger": {
"main": [
[
{
"node": "Get a list of available storages",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Prepare output parameters": {
"main": [
[
{
"node": "Send Message",
"type": "main",
"index": 0
}
]
]
},
"Question and Answer Chain": {
"main": [
[
{
"node": "Prepare output parameters",
"type": "main",
"index": 0
}
]
]
},
"Get a list of Folders files": {
"main": [
[
{
"node": "Split Out folder files and folders",
"type": "main",
"index": 0
}
]
]
},
"Get a list of available storages": {
"main": [
[
{
"node": "Get a list of List of Files and Folders",
"type": "main",
"index": 0
}
]
]
},
"Merge parameters for Subworkflow": {
"main": [
[
{
"node": "Execute subworkflow",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Split Out folder files and folders": {
"main": [
[
{
"node": "Filter for files",
"type": "main",
"index": 0
}
]
]
},
"Get a list of List of Files and Folders": {
"main": [
[
{
"node": "Get a list of Folders files",
"type": "main",
"index": 0
}
]
]
}
}
}
Pro
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How this works
This workflow enables seamless integration of a Retrieval-Augmented Generation (RAG) chatbot into Bitrix24's Open Channels, allowing businesses to deliver intelligent, context-aware responses to customer queries directly within their messaging platform. It's ideal for customer support teams or sales organisations seeking to automate interactions without losing the personal touch, leveraging Ollama for efficient embeddings and Qdrant for vector storage to ensure responses draw from accurate, up-to-date knowledge bases. The key step involves processing incoming webhook events from Bitrix24, validating tokens, and routing messages to the RAG chain for precise retrieval and generation.
Use this workflow when building a scalable chatbot for high-volume Bitrix24 interactions that require referencing specific documents or FAQs, such as in e-commerce support or internal helpdesks. Avoid it for simple rule-based bots lacking document retrieval needs, or if your setup demands real-time voice processing rather than text-based chats. Common variations include swapping Ollama for other embedding models or adding post-response actions like logging to a CRM for analytics.
About this workflow
Bitrix24 Open Chanel RAG Chatbot Application Workflow example with Webhook Integration. Uses httpRequest, noOp, respondToWebhook, documentDefaultDataLoader. Webhook trigger; 34 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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