Davide Boizza This workflow corresponds to n8n.io template #2784 — 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": "fqQcmSdoVqnPeGHj",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "OpenAI Personal Shopper with RAG and WooCommerce",
"tags": [],
"nodes": [
{
"id": "635901e5-4afd-4c81-a63e-52f1b863a025",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-200,
280
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "d11cd97c-1539-462d-858c-8758cf1a8278",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
620,
580
],
"parameters": {
"sessionKey": "={{ $('Edit Fields').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "02bb43e4-f26e-4906-8049-c49d3fecd817",
"name": "Calculator",
"type": "@n8n/n8n-nodes-langchain.toolCalculator",
"position": [
760,
580
],
"parameters": {},
"typeVersion": 1
},
{
"id": "ad6058dd-b429-4f3c-b68a-7e3d98beec83",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
20,
280
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "7015c229-f9fe-4c77-b2b9-4ac09a3a3cb1",
"name": "sessionId",
"type": "string",
"value": "={{ $json.sessionId }}"
},
{
"id": "f8fc0044-6a1a-455b-a435-58931a8c4c8e",
"name": "chatInput",
"type": "string",
"value": "={{ $json.chatInput }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "43f7ee25-4529-4558-b5ea-c2a722b0bce5",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
500,
580
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "8b5ec20d-8735-4030-8113-717d578928eb",
"name": "RAG",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
1000,
580
],
"parameters": {
"name": "informazioni_negozio",
"description": "Informazioni relative al negozio: orari di apertura, indirizzo, contatti, informazioni generali"
},
"typeVersion": 1
},
{
"id": "0fd0f1d6-41df-43d4-9418-0685afad409a",
"name": "Qdrant Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
900,
780
],
"parameters": {
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "scarperia",
"cachedResultName": "scarperia"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "72084a2e-0e47-4723-a004-585ae8b67ae3",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
840,
940
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "30d398a3-2331-4a3d-898d-c184779c7ef3",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1200,
800
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "e10a8024-51ec-4553-a1fa-dbaa49a4d2c2",
"name": "personal_shopper",
"type": "n8n-nodes-base.wooCommerceTool",
"position": [
880,
580
],
"parameters": {
"options": {
"sku": "={{ $('Information Extractor').item.json.output.SKU }}",
"search": "={{ $('Information Extractor').item.json.output.keyword }}",
"maxPrice": "={{ $('Information Extractor').item.json.output.price_max }}",
"minPrice": "={{ $('Information Extractor').item.json.output.price_min }}",
"stockStatus": "instock"
},
"operation": "getAll"
},
"credentials": {
"wooCommerceApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f0c53b0d-7173-4ec9-8fb4-f8f45d9ceedc",
"name": "Information Extractor",
"type": "@n8n/n8n-nodes-langchain.informationExtractor",
"position": [
220,
280
],
"parameters": {
"text": "={{ $json.chatInput }}",
"options": {
"systemPromptTemplate": "You are an intelligent assistant for a shoe and accessories store (mainly bags). Your task is to analyze the input text coming from a chat and determine if the user is looking for a product. If the user is looking for a product, you need to extract the following information:\n1. The keyword (keyword) useful for the search.\n2. Any minimum or maximum prices specified.\n3. An SKU (product code) if mentioned.\n4. The name of the category to search in, if specified.\n\nInstructions:\n1. Identify the intent: Determine if the user is looking for a specific product.\n2. Extract the information:\n- If the user is looking for a product, identify:\n- Set the type \"search\" to true. Otherwise, set it to false\n- The keywords.\n- Any minimum or maximum prices (e.g. \"less than 50 euros\", \"between 30 and 60 euros\").\n- An SKU (e.g. \"ABC123 code\").\n- The category name (e.g. \"t-shirts\", \"jeans\", \"women\", \"men\").\n3. Output format: Return a JSON object with the given structure"
},
"schemaType": "manual",
"inputSchema": "{\n \"search_intent\": true,\n \"search_params\": [\n { \"type\": \"search\", \"value\": \"ture or false\" },\n { \"type\": \"keyword\", \"value\": \"valore_keyword\" },\n { \"type\": \"min_price\", \"value\": \"valore_min_price\" },\n { \"type\": \"max_price\", \"value\": \"valore_max_price\" },\n { \"type\": \"sku\", \"value\": \"valore_sku\" },\n { \"type\": \"category\", \"value\": \"valore_categoria\" }\n ]\n }"
},
"typeVersion": 1
},
{
"id": "8386e554-e2f1-42c8-881f-a06e8099f718",
"name": "OpenAI Chat Model2",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
200,
460
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "4ff30e15-1bf5-4750-a68a-e72f86a4f32c",
"name": "Google Drive2",
"type": "n8n-nodes-base.googleDrive",
"position": [
320,
-440
],
"parameters": {
"filter": {
"driveId": {
"__rl": true,
"mode": "list",
"value": "My Drive",
"cachedResultUrl": "https://drive.google.com/drive/my-drive",
"cachedResultName": "My Drive"
},
"folderId": {
"__rl": true,
"mode": "list",
"value": "1lmnqpLFKS-gXmXT92C5VG0P1XlcoeFOb",
"cachedResultUrl": "https://drive.google.com/drive/folders/1lmnqpLFKS-gXmXT92C5VG0P1XlcoeFOb",
"cachedResultName": "Scarperia Sal\u00f2 - RAG"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "b4ca79b2-220b-4290-a33a-596250c8fd2d",
"name": "Google Drive1",
"type": "n8n-nodes-base.googleDrive",
"position": [
520,
-440
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text/plain"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "18f5e068-ad4a-4be7-987c-83ed5791f012",
"name": "Embeddings OpenAI3",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
680,
-260
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.1
},
{
"id": "43693ee0-a2a3-44d3-86de-4156af84e251",
"name": "Default Data Loader2",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
880,
-220
],
"parameters": {
"options": {},
"dataType": "binary"
},
"typeVersion": 1
},
{
"id": "f0d351e5-faee-49a4-a43c-985785c3d2c8",
"name": "Token Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterTokenSplitter",
"position": [
960,
-60
],
"parameters": {
"chunkSize": 300,
"chunkOverlap": 30
},
"typeVersion": 1
},
{
"id": "ff77338e-4dac-4261-87a1-10a21108f543",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-200,
-440
],
"parameters": {},
"typeVersion": 1
},
{
"id": "72484893-875a-4e8b-83fc-ca137e812050",
"name": "HTTP Request",
"type": "n8n-nodes-base.httpRequest",
"position": [
40,
-440
],
"parameters": {
"url": "https://YOUR_AWS_SECRET_KEY_HERE",
"method": "POST",
"options": {},
"jsonBody": "{\n \"filter\": {}\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"authentication": "genericCredentialType",
"genericAuthType": "httpHeaderAuth",
"headerParameters": {
"parameters": [
{
"name": "Content-Type",
"value": "application/json"
}
]
}
},
"credentials": {
"httpHeaderAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.2
},
{
"id": "5837e3ac-e3d1-45b6-bd67-8c3d03bf0a1e",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-20,
-500
],
"parameters": {
"width": 259.7740863787376,
"height": 234.1528239202657,
"content": "Replace the URL and Collection name with your own"
},
"typeVersion": 1
},
{
"id": "79baf424-e647-4a80-a19e-c023ad3b1860",
"name": "Qdrant Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
760,
-440
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "scarperia",
"cachedResultName": "scarperia"
}
},
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "17015f50-a3a8-4e62-9816-7e71127c1ea1",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-220,
-640
],
"parameters": {
"color": 3,
"width": 1301.621262458471,
"height": 105.6212624584717,
"content": "## Step 1 \nCreate a collectiopn on your Qdrant instance. Then create a basic RAG system with documents uploaded to Google Drive and embedded in the Qdrant vector database"
},
"typeVersion": 1
},
{
"id": "0ddbf6be-fa2d-4412-8e85-fe108cd6e84d",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1020,
980.0000000000001
],
"parameters": {
"color": 3,
"width": 1301.621262458471,
"height": 105.6212624584717,
"content": "## Step 1 \nCreate a basic RAG system with documents uploaded to Google Drive and embedded in the Qdrant vector database"
},
"typeVersion": 1
},
{
"id": "3782a22d-b3a7-44ea-ad36-fa4382c9fcfd",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-200,
120
],
"parameters": {
"color": 3,
"width": 1301.621262458471,
"height": 105.6212624584717,
"content": "## Step 2 \nThe Information Extractor tries to understand if the request is related to products and if so, it extracts the useful information to filter the products available on WooCommerce by calling the \"personal_shopper\". If it is a general question, the RAG system is called"
},
"typeVersion": 1
},
{
"id": "d4d1fb16-3f54-4c1a-ab4e-bcf86d897e9d",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
580,
280
],
"parameters": {
"text": "={{ $('When chat message received').item.json.chatInput }}",
"options": {
"systemMessage": "=You are an intelligent assistant for a clothing store. Your task is to analyze the input text from a chat and determine if the user is looking for a product.\n\nBehavior:\n- If the user is looking for a product the \"search\" field of the following JSON is set to true and you must pass the following JSON as input to the \"personal_shopper\" tool to extract:\n\n```json\n{{ JSON.stringify($json.output) }}\n```\n\n- If the user asks questions related to the store such as address or opening hours, you must use the \"RAG\" tool"
},
"promptType": "define"
},
"typeVersion": 1.7
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "47513e11-8e9f-4b7c-b3de-e15cf00a1200",
"connections": {
"RAG": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Calculator": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "Information Extractor",
"type": "main",
"index": 0
}
]
]
},
"HTTP Request": {
"main": [
[
{
"node": "Google Drive2",
"type": "main",
"index": 0
}
]
]
},
"Google Drive1": {
"main": [
[
{
"node": "Qdrant Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"Google Drive2": {
"main": [
[
{
"node": "Google Drive1",
"type": "main",
"index": 0
}
]
]
},
"Token Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader2",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"personal_shopper": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI3": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"OpenAI Chat Model1": {
"ai_languageModel": [
[
{
"node": "RAG",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI Chat Model2": {
"ai_languageModel": [
[
{
"node": "Information Extractor",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"ai_vectorStore": [
[
{
"node": "RAG",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Default Data Loader2": {
"ai_document": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_document",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Information Extractor": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "HTTP Request",
"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.
googleDriveOAuth2ApihttpHeaderAuthopenAiApiqdrantApiwooCommerceApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios: Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from…
Source: https://n8n.io/workflows/2784/ — 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
⚡AI-Powered YouTube Playlist & Video Summarization and Analysis v2. Uses lmChatGoogleGemini, agent, splitOut, chainLlm. Chat trigger; 72 nodes.
Google Gemini Chat, Agent, Chain Llm +11
This n8n workflow transforms entire YouTube playlists or single videos into interactive knowledge bases you can chat with. Ask questions and get summaries without needing to watch hours of content. 🔗
Google Gemini Chat, Agent, Chain Llm +11
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