This workflow corresponds to n8n.io template #knowledge_store_agent_with_google_drive — 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
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{
"createdAt": "2025-09-02T05:02:09.689Z",
"updatedAt": "2025-09-02T05:02:09.689Z",
"id": "7NTJXYxlLfh8acC5",
"name": "Knowledge store agent (with Google Drive)",
"active": false,
"isArchived": false,
"nodes": [
{
"parameters": {
"dataType": "binary",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"typeVersion": 1.1,
"position": [
-128,
256
],
"id": "708d9e4e-6566-4b18-86b8-8301bf0c72dd",
"name": "Default Data Loader"
},
{
"parameters": {
"operation": "download",
"fileId": {
"__rl": true,
"value": "={{ $json.id }}",
"mode": "id"
},
"options": {}
},
"type": "n8n-nodes-base.googleDrive",
"typeVersion": 3,
"position": [
-432,
32
],
"id": "604a4e5e-5ccc-4db1-94dc-673711bea6af",
"name": "Download file"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1.2,
"position": [
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],
"id": "e8db240e-bb3f-43ab-b4ed-527c6c2e8f54",
"name": "Embedding model"
},
{
"parameters": {
"options": {
"systemMessage": "# Knowledge Store Agent System Prompt\n\nYou are a data analysis agent that retrieves and analyzes information from a vector store to answer user questions.\n\n## Your Task\n\n1. **Search the vector store** - Use similarity search to find relevant documents and data\n2. **Analyze the results** - Understand what the retrieved data tells you\n3. **Provide clear answers** - Give helpful responses based on the data you found\n\n## How to Work with Vector Data\n\n### Search Process\n- Use the user's question to search for similar content\n- Retrieve multiple relevant chunks of data\n- Look for patterns and connections across the results\n- Consider both exact matches and conceptually similar information\n\n### Analysis Guidelines\n- Read through all retrieved documents carefully\n- Identify key information that answers the user's question\n- Note any conflicting or incomplete information\n- Look for trends, patterns, or insights in the data\n\n### Response Format\n- Start with a direct answer to the user's question\n- Support your answer with specific information from the data\n- Cite which documents or sources your information comes from\n- Be clear about what you found and what you didn't find\n\n## Response Guidelines\n\n### When You Find Good Data\n- Give a confident, detailed answer\n- Include relevant quotes or data points\n- Explain how the information relates to their question\n- Offer additional insights if available\n\n### When Data is Limited\n- Be honest about what information is available\n- Share what you did find, even if partial\n- Suggest related questions you could help with\n- Don't make up information not in the data\n\n### When No Relevant Data is Found\n- Clearly state that you couldn't find relevant information\n- Suggest alternative ways to phrase the question\n- Offer to search for related topics\n\n## Key Principles\n\n- Always base answers on the retrieved data\n- Be transparent about your sources\n- Admit when information is unclear or missing\n- Help users understand what the data shows\n- Ask clarifying questions if the user's request is vague\n\nRemember: Your strength is finding and explaining information that already exists in the vector store. Focus on being accurate and helpful with the data you can retrieve."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2.2,
"position": [
384,
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],
"id": "99f9315f-4f3d-48d9-a6f4-8c519b249eba",
"name": "AI Agent"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.3,
"position": [
176,
32
],
"id": "bedf3615-c8b0-4fa8-afc5-574bca1003d4",
"name": "When chat message received"
},
{
"parameters": {
"model": {
"__rl": true,
"value": "gpt-4o",
"mode": "list",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.2,
"position": [
288,
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],
"id": "9751b75b-50ac-49c8-8f4d-24a8035b6e73",
"name": "Model"
},
{
"parameters": {},
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"typeVersion": 1.3,
"position": [
432,
240
],
"id": "167d097a-69f6-4f1b-a741-8c8936adb3d2",
"name": "Simple Memory"
},
{
"parameters": {
"pollTimes": {
"item": [
{
"mode": "everyMinute"
}
]
},
"triggerOn": "specificFolder",
"folderToWatch": {
"__rl": true,
"value": "1y55jxSWMeFBBFOIXxHWofVA2vkro4YLI",
"mode": "list",
"cachedResultName": "Knowledge store",
"cachedResultUrl": "https://drive.google.com/drive/folders/1y55jxSWMeFBBFOIXxHWofVA2vkro4YLI"
},
"event": "fileCreated",
"options": {}
},
"type": "n8n-nodes-base.googleDriveTrigger",
"typeVersion": 1,
"position": [
-640,
32
],
"id": "0cddc21e-a75f-48df-b168-8e288be12469",
"name": "File uploaded"
},
{
"parameters": {
"content": "### Knowledge store agent\nA chat-based AI agent to retrieve, analyze, and answer questions using documents uploaded to Google Drive and stored in a vector database.\n\n#### Set up\n- Configure credentials in the **Google Drive** and **Open AI** nodes\n- Create a folder in Google Drive to store your documents, then select it in the \"File uploaded\" trigger node\n- Upload a file to that folder, return to n8n and click \"Execute workflow\"\n- Once **Insert documents** has been completed you can Open chat and ask the agent questions about your files.\n\n#### Next steps\nTry connecting other data sources to your knowledge base, using other triggers before the **Insert documents** node.\n",
"height": 512,
"width": 304,
"color": 5
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-1024,
-16
],
"typeVersion": 1,
"id": "b60fde91-e9bf-4f03-a847-b471af228a95",
"name": "Sticky Note"
},
{
"parameters": {
"content": "### Embeddings\n\nThe Insert and Retrieve operation use the same embedding node.\n\nThis is to ensure that they are using the **exact same embeddings and settings**.\n\nDifferent embeddings might not work at all, or have unintended consequences.\n",
"height": 240,
"width": 320,
"color": 4
},
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"typeVersion": 1,
"id": "2fa9684d-f20a-4262-9b8b-81babb2fecc5",
"name": "Sticky Note3"
},
{
"parameters": {
"mode": "insert",
"memoryKey": {
"__rl": true,
"value": "vector_store_key",
"mode": "list",
"cachedResultName": "vector_store_key"
}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.3,
"position": [
-240,
32
],
"id": "64d17551-1637-4a53-a3e0-db45894c12a6",
"name": "Insert documents"
},
{
"parameters": {
"mode": "retrieve-as-tool",
"toolDescription": "Use this tool to retrieve any information required.",
"memoryKey": {
"__rl": true,
"value": "vector_store_key",
"mode": "list",
"cachedResultName": "vector_store_key"
},
"topK": 10
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.3,
"position": [
640,
368
],
"id": "4f8a4a45-28a4-4684-b838-66011cc6a4a5",
"name": "Retrieve documents"
}
],
"connections": {
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert documents",
"type": "ai_document",
"index": 0
}
]
]
},
"Download file": {
"main": [
[
{
"node": "Insert documents",
"type": "main",
"index": 0
}
]
]
},
"Embedding model": {
"ai_embedding": [
[
{
"node": "Insert documents",
"type": "ai_embedding",
"index": 0
},
{
"node": "Retrieve documents",
"type": "ai_embedding",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"File uploaded": {
"main": [
[
{
"node": "Download file",
"type": "main",
"index": 0
}
]
]
},
"Retrieve documents": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
},
"staticData": null,
"meta": {
"templateId": "knowledge_store_agent_with_google_drive"
},
"versionId": "9edeeb3d-b456-46a1-b752-35b1cf0c39cb",
"triggerCount": 0,
"shared": [
{
"createdAt": "2025-09-02T05:02:09.689Z",
"updatedAt": "2025-09-02T05:02:09.689Z",
"role": "workflow:owner",
"workflowId": "7NTJXYxlLfh8acC5",
"projectId": "sjNgDrdbhRMMF6SK"
}
],
"tags": [
{
"createdAt": "2025-06-25T04:00:38.130Z",
"updatedAt": "2025-06-25T04:00:38.130Z",
"id": "EyGUZypc5DKHVUyY",
"name": "\ud328\uc2a4\ud2b8\ucea0\ud37c\uc2a4"
}
]
}
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About this workflow
Knowledge store agent (with Google Drive). Uses documentDefaultDataLoader, googleDrive, embeddingsOpenAi, agent. Chat trigger; 12 nodes.
Source: https://github.com/2innnnn0/fastcampus-n8n-data-analysis-agent/blob/main/workflows/[7NTJXYxlLfh8acC5]knowledge-store-agent-(with-google-drive).json — original creator credit. Request a take-down →
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