This workflow follows the Agent → Google Gemini Embeddings 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 →
{
"name": "rag",
"nodes": [
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 2.1,
"position": [
-48,
96
],
"id": "bc60a346-e6e6-4a4f-ab7b-6891c553629d",
"name": "AI Agent"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
-304,
96
],
"id": "42a5358c-8046-4765-9c99-732e33f83f5e",
"name": "When chat message received"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"typeVersion": 1,
"position": [
-144,
288
],
"id": "1bb653fd-7040-415b-a78b-9560085e53df",
"name": "Google Gemini Chat Model",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {},
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"typeVersion": 1.3,
"position": [
16,
288
],
"id": "1204fd33-dc35-4b24-94c5-6a87e614a316",
"name": "Simple Memory"
},
{
"parameters": {},
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
"typeVersion": 1,
"position": [
256,
544
],
"id": "25d66c05-af1f-4eb0-8dac-a9221c7c92ca",
"name": "Embeddings Google Gemini",
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"dataType": "binary",
"binaryMode": "specificField",
"loader": "textLoader",
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"typeVersion": 1.1,
"position": [
736,
288
],
"id": "719123c1-d732-46e7-b2f9-d36271d533e8",
"name": "Default Data Loader"
},
{
"parameters": {
"formTitle": "Add doc for RAG",
"formFields": {
"values": [
{
"fieldLabel": "data",
"fieldType": "file",
"acceptFileTypes": ".txt"
}
]
},
"options": {}
},
"type": "n8n-nodes-base.formTrigger",
"typeVersion": 2.2,
"position": [
464,
96
],
"id": "40bc6f21-bfd8-4536-8a2e-d5965b0dc797",
"name": "On form submission"
},
{
"parameters": {
"mode": "retrieve-as-tool",
"toolDescription": "vector store which can retrieve documents about Chat-\u5b1b\u5b1b",
"memoryKey": {
"__rl": true,
"value": "rag_demo",
"mode": "list",
"cachedResultName": "rag_demo"
}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.3,
"position": [
144,
288
],
"id": "94fcf172-7a63-40be-8a71-73834a70104d",
"name": "Retrieve Documents"
},
{
"parameters": {
"mode": "insert",
"memoryKey": {
"__rl": true,
"value": "rag_demo",
"mode": "list",
"cachedResultName": "rag_demo"
},
"clearStore": true
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
"typeVersion": 1.3,
"position": [
672,
96
],
"id": "e2e6d257-2abc-4127-b238-8408812dd522",
"name": "Insert Documents to Store"
}
],
"connections": {
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Embeddings Google Gemini": {
"ai_embedding": [
[
{
"node": "Retrieve Documents",
"type": "ai_embedding",
"index": 0
},
{
"node": "Insert Documents to Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Insert Documents to Store",
"type": "ai_document",
"index": 0
}
]
]
},
"On form submission": {
"main": [
[
{
"node": "Insert Documents to Store",
"type": "main",
"index": 0
}
]
]
},
"Retrieve Documents": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "287f7bda-773b-4fa9-b2de-f238979a960d",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "Uf8uGe4wIAJ5TbvX",
"tags": []
}
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.
googlePalmApihttpHeaderAuthpostgressupabaseApi
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About this workflow
RAG. Uses httpRequest, agent, lmChatGoogleGemini, memoryPostgresChat. Webhook trigger; 16 nodes.
Source: https://github.com/jirayu-ct/n8n-agentic-rag-agent/blob/0f3eb3f0057e7c8e7dbee221f88b34aa87a7b4c9/example/RAG.json — original creator credit. Request a take-down →
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