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": "7cXvgkl9170QXzT2",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "RAG Workflow For Company Documents stored in Google Drive",
"tags": [],
"nodes": [
{
"id": "753455a3-ddc8-4a74-b043-70a0af38ff9e",
"name": "Pinecone Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
680,
0
],
"parameters": {
"mode": "insert",
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "company-files",
"cachedResultName": "company-files"
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "a7c8fa7f-cad2-4497-a295-30aa2e98cacc",
"name": "Embeddings Google Gemini",
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
640,
280
],
"parameters": {
"modelName": "models/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "215f0519-4359-4e4b-a90c-7e54b1cc52b5",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
840,
220
],
"parameters": {
"options": {},
"dataType": "binary",
"binaryMode": "specificField"
},
"typeVersion": 1
},
{
"id": "863d3d1d-1621-406e-8320-688f64b07b09",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
820,
420
],
"parameters": {
"options": {},
"chunkOverlap": 100
},
"typeVersion": 1
},
{
"id": "5af1efb1-ea69-466e-bb3b-2b7e6b1ceef7",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
420,
840
],
"parameters": {
"options": {
"systemMessage": "You are a helpful HR assistant designed to answer employee questions based on company policies.\n\nRetrieve relevant information from the provided internal documents and provide a concise, accurate, and informative answer to the employee's question.\n\nUse the tool called \"company_documents_tool\" to retrieve any information from the company's documents.\n\nIf the answer cannot be found in the provided documents, respond with \"I cannot find the answer in the available resources.\""
}
},
"typeVersion": 1.7
},
{
"id": "825632ac-1edf-4e63-948d-b1a498b2b962",
"name": "Vector Store Tool",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
820,
1060
],
"parameters": {
"name": "company_documents_tool",
"description": "Retrieve information from any company documents"
},
"typeVersion": 1
},
{
"id": "72d2f685-bcc3-4e62-a5e3-72c0fe65f8e8",
"name": "Pinecone Vector Store (Retrieval)",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
720,
1240
],
"parameters": {
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "company-files",
"cachedResultName": "company-files"
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "eeff81cb-6aec-4e7f-afe0-432d87085fb2",
"name": "Embeddings Google Gemini (retrieval)",
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
700,
1400
],
"parameters": {
"modelName": "models/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "8bb6ebb1-1deb-498b-8da4-b809a736e097",
"name": "Download File From Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
460,
0
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {
"fileName": "={{ $json.name }}"
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 3
},
{
"id": "bd83bacf-dff1-4b7c-af5c-b249fb16c113",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
420,
660
],
"parameters": {
"content": "## Chat with company documents"
},
"typeVersion": 1
},
{
"id": "7b90daab-0fb2-4c8a-93e6-b138bb04f282",
"name": "Google Drive File Updated",
"type": "n8n-nodes-base.googleDriveTrigger",
"position": [
140,
140
],
"parameters": {
"event": "fileUpdated",
"options": {},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"triggerOn": "specificFolder",
"folderToWatch": {
"__rl": true,
"mode": "list",
"value": "1evDIoHePhjw_LgVFZXSZyK1sZm2GHp9W",
"cachedResultUrl": "https://drive.google.com/drive/folders/1evDIoHePhjw_LgVFZXSZyK1sZm2GHp9W",
"cachedResultName": "INNOVI PRO"
}
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "3a6c6cef-7a19-42ef-8092-eaf57dae4cdd",
"name": "Google Drive File Created",
"type": "n8n-nodes-base.googleDriveTrigger",
"position": [
140,
-120
],
"parameters": {
"event": "fileCreated",
"options": {
"fileType": "all"
},
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"triggerOn": "specificFolder",
"folderToWatch": {
"__rl": true,
"mode": "list",
"value": "1evDIoHePhjw_LgVFZXSZyK1sZm2GHp9W",
"cachedResultUrl": "https://drive.google.com/drive/folders/1evDIoHePhjw_LgVFZXSZyK1sZm2GHp9W",
"cachedResultName": "INNOVI PRO"
}
},
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "1e38f1c8-7bd0-4eeb-addc-62339582d350",
"name": "Window Buffer Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
500,
1140
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "4b0ab858-99b1-4337-8c5c-a223519e3662",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
80,
840
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "bfb684d1-e5c1-41da-8305-b2606a2eade6",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
440,
-240
],
"parameters": {
"width": 320,
"content": "## Add docuemnts to vector store when updating or creating new documents in Google Drive"
},
"typeVersion": 1
},
{
"id": "8f627ec6-4b3f-43ad-a4a3-e2b199a7fe58",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
320,
1140
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "f2133a06-0088-46de-9f74-a3f9fe478f98",
"name": "Google Gemini Chat Model (retrieval)",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1080,
1240
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-exp"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "578deb96-8393-4850-9757-fa97b2bc9992",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-540,
220
],
"parameters": {
"width": 420,
"height": 720,
"content": "## Set up steps\n\n1. Google Cloud Project and Vertex AI API:\n* Create a Google Cloud project.\n* Enable the Vertex AI API for your project.\n2. Google AI API Key:\n* Obtain a Google AI API key from Google AI Studio.\n3. Pinecone Account:\n* Create a free account on the Pinecone website.\nObtain your API key from your Pinecone dashboard.\n* Create an index named company-files in your Pinecone project.\n4. Google Drive:\n* Create a dedicated folder in your Google Drive where company documents will be stored.\n5. Credentials in n8n: Configure credentials in your n8n environment for:\n* Google Drive OAuth2\n* Google Gemini(PaLM) Api (using your Google AI API key)\n* Pinecone API (using your Pinecone API key)\n5. Import the Workflow:\n* Import this workflow into your n8n instance.\n6. Configure the Workflow:\n* Update both Google Drive Trigger nodes to watch the specific folder you created in your Google Drive.\n* Configure the Pinecone Vector Store nodes to use your company-files index."
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "33b252fb-5d87-4a29-a0a7-97308140699c",
"connections": {
"AI Agent": {
"main": [
[]
]
},
"Vector Store Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Window Buffer Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Pinecone Vector Store": {
"main": [
[]
]
},
"Embeddings Google Gemini": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Google Drive File Created": {
"main": [
[
{
"node": "Download File From Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Google Drive File Updated": {
"main": [
[
{
"node": "Download File From Google Drive",
"type": "main",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Download File From Google Drive": {
"main": [
[
{
"node": "Pinecone Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Pinecone Vector Store (Retrieval)": {
"ai_vectorStore": [
[
{
"node": "Vector Store Tool",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Embeddings Google Gemini (retrieval)": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store (Retrieval)",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Google Gemini Chat Model (retrieval)": {
"ai_languageModel": [
[
{
"node": "Vector Store Tool",
"type": "ai_languageModel",
"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.
googleDriveOAuth2ApigooglePalmApipineconeApi
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
How this works
This workflow empowers teams to retrieve precise, context-aware answers from company documents stored in Google Drive, saving hours of manual searching and ensuring responses draw directly from verified sources. It suits knowledge workers, support teams, or managers who need quick insights without sifting through files, particularly in document-heavy environments like legal or HR. The key step involves an AI agent using Google Gemini embeddings to query a Pinecone vector store, pulling relevant document chunks for accurate retrieval-augmented generation.
Use this when handling frequent queries on evolving company documents in Google Drive, such as internal FAQs or policy checks, to maintain up-to-date responses without rebuilding from scratch. Avoid it for real-time data like live analytics or non-text files like images, where specialised loaders are needed. Common variations include swapping Pinecone for another vector store or integrating with Slack for chat-based queries.
About this workflow
RAG Workflow For Company Documents stored in Google Drive. Uses vectorStorePinecone, embeddingsGoogleGemini, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Event-driven trigger; 18 nodes.
Source: https://github.com/Zie619/n8n-workflows — original creator credit. Request a take-down →
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