About this recipe
A recipe for Rag Context Aware Chunking Google Drive To Pinecone Via Openrouter & Gemini
Recipe Template
{
"id": "VY4WBXuNDPxmOO5e",
"meta": {
"instanceId": "d16fb7d4b3eb9b9d4ad2ee6a7fbae593d73e9715e51f583c2a0e9acd1781c08e",
"templateCredsSetupCompleted": true
},
"name": "RAG:Context-Aware Chunking | Google Drive to Pinecone via OpenRouter & Gemini",
"tags": [
{
"id": "XZIQK6NdzGvgbZFd",
"name": "Sell",
"createdAt": "2025-01-15T12:28:48.424Z",
"updatedAt": "2025-01-15T12:28:48.424Z"
}
],
"nodes": [
{
"id": "7abbfa6e-4b17-4656-9b82-377b1bacf539",
"name": "When clicking \u2018Test workflow\u2019",
"type": "n8n-nodes-base.manualTrigger",
"position": [
0,
0
],
"parameters": [],
"typeVersion": 1
},
{
"id": "448ec137-bf64-46b4-bf15-c7a040faa306",
"name": "Loop Over Items",
"type": "n8n-nodes-base.splitInBatches",
"position": [
1100,
0
],
"parameters": {
"options": []
},
"typeVersion": 3
},
{
"id": "f22557ee-7f37-40cd-9063-a9a759274663",
"name": "OpenRouter Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatOpenRouter",
"position": [
20,
440
],
"parameters": {
"options": []
},
"credentials": {
"openRouterApi": {
"id": "ddH6iNlm09UxrXvu",
"name": "Auto: OpenRouter"
}
},
"typeVersion": 1
},
{
"id": "57e8792e-25ae-43d5-b4e9-e87642365ee9",
"name": "Pinecone Vector Store",
"type": "@n8n\/n8n-nodes-langchain.vectorStorePinecone",
"position": [
780,
360
],
"parameters": {
"mode": "insert",
"options": [],
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "context-rag-test",
"cachedResultName": "context-rag-test"
}
},
"credentials": {
"pineconeApi": {
"id": "R3QGXSEIRTEAZttK",
"name": "Auto: PineconeApi"
}
},
"typeVersion": 1
},
{
"id": "0a8c2426-0aaf-424a-b246-336a9034aba8",
"name": "Embeddings Google Gemini",
"type": "@n8n\/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
720,
540
],
"parameters": {
"modelName": "models\/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"id": "9idxGZRZ3BAKDoxq",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "edc587bd-494d-43e8-b6d6-26adab7af3dc",
"name": "Default Data Loader",
"type": "@n8n\/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
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],
"parameters": {
"options": []
},
"typeVersion": 1
},
{
"id": "a82d4e0b-248e-426d-9ef3-f25e7078ceb3",
"name": "Recursive Character Text Splitter",
"type": "@n8n\/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
840,
680
],
"parameters": {
"options": [],
"chunkSize": 100000
},
"typeVersion": 1
},
{
"id": "8571b92f-5587-454f-9700-ea04ca35311b",
"name": "Get Document From Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
220,
0
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "list",
"value": "1gm0jxFTLuiWB5u4esEjzoCPImrVqu0AEMIKBIesTf9M",
"cachedResultUrl": "https:\/\/docs.google.com\/document\/d\/1gm0jxFTLuiWB5u4esEjzoCPImrVqu0AEMIKBIesTf9M\/edit?usp=drivesdk",
"cachedResultName": "Udit Rawat - Details"
},
"options": {
"googleFileConversion": {
"conversion": {
"docsToFormat": "text\/plain"
}
}
},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "SsiQguNA8w3Wwv4w",
"name": "Auto: Google Drive"
}
},
"typeVersion": 3
},
{
"id": "2bed3d0f-3d65-4394-87f1-e73320a43a4a",
"name": "Extract Text Data From Google Document",
"type": "n8n-nodes-base.extractFromFile",
"position": [
440,
0
],
"parameters": {
"options": [],
"operation": "text"
},
"typeVersion": 1
},
{
"id": "837fa691-6c66-434b-ba82-d1cad9aecdf7",
"name": "Split Document Text Into Sections",
"type": "n8n-nodes-base.code",
"position": [
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],
"parameters": {
"jsCode": "let split_text = \"\u2014---------------------------\u2014-------------[SECTIONEND]\u2014---------------------------\u2014-------------\";\nfor (const item of $input.all()) {\n item.json.section = item.json.data.split(split_text);\n item.json.document = JSON.stringify(item.json.section)\n}\nreturn $input.all();"
},
"typeVersion": 2
},
{
"id": "cc801e7e-e01b-421a-9211-08322ef8a0b2",
"name": "Prepare Sections For Looping",
"type": "n8n-nodes-base.splitOut",
"position": [
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],
"parameters": {
"options": [],
"fieldToSplitOut": "section"
},
"typeVersion": 1
},
{
"id": "658cb8df-92e3-4b25-8f37-e5f959d913dc",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-40,
-100
],
"parameters": {
"width": 1300,
"height": 280,
"content": "## Prepare Document. \nThis section is responsible for downloading the file from Google Drive, splitting the text into sections by detecting separators, and preparing them for looping."
},
"typeVersion": 1
},
{
"id": "82ee9194-484a-46db-b75c-bec34201c7e2",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-220,
220
],
"parameters": {
"width": 780,
"height": 360,
"content": "## Prepare context\nIn this section, the \nagent node will prepare \ncontext for a section \n(chunk of text), which \nwill then be passed for \nconversion into a vectors \nalong with the section itself."
},
"typeVersion": 1
},
{
"id": "2f6950df-ead1-479a-aa51-7768121a4eb2",
"name": "AI Agent - Prepare Context",
"type": "@n8n\/n8n-nodes-langchain.agent",
"position": [
40,
260
],
"parameters": {
"text": "=<document> \n{{ $('Split Document Text Into Sections').item.json.document }}\n<\/document> \nHere is the chunk we want to situate within the whole document \n<chunk> \n{{ $json.section }}\n<\/chunk> \nPlease give a short succinct context to situate this chunk within the overall document for the purposes of improving search retrieval of the chunk. Answer only with the succinct context and nothing else. ",
"agent": "conversationalAgent",
"options": [],
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "34a465fc-a505-445a-9211-bcd830381354",
"name": "Concatenate the context and section text",
"type": "n8n-nodes-base.set",
"position": [
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260
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "e5fb0381-5d23-46e2-a0d1-438240b80a3e",
"name": "=section_chunk",
"type": "string",
"value": "={{ $json.output }}. {{ $('Loop Over Items').item.json.section }}"
}
]
}
},
"typeVersion": 3.4
},
{
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"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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220
],
"parameters": {
"width": 580,
"height": 600,
"content": "## Convert Text To Vectors\nIn this step, the Pinecone node converts the provided text into vectors using Google Gemini and stores them in the Pinecone vector database."
},
"typeVersion": 1
},
{
"id": "45798b49-fc78-417c-a752-4dd1a8882cd7",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
-120
],
"parameters": {
"width": 400,
"height": 300,
"content": "## Video Demo\n[](https:\/\/www.youtube.com\/watch?v=qBeWP65I4hg)"
},
"typeVersion": 1
}
],
"active": false,
"pinData": [],
"settings": {
"executionOrder": "v1"
},
"versionId": "4f0e2203-5850-4a32-b1dd-5adc57fa43ff",
"connections": {
"Loop Over Items": {
"main": [
[],
[
{
"node": "AI Agent - Prepare Context",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"OpenRouter Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent - Prepare Context",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Pinecone Vector Store": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Embeddings Google Gemini": {
"ai_embedding": [
[
{
"node": "Pinecone Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"AI Agent - Prepare Context": {
"main": [
[
{
"node": "Concatenate the context and section text",
"type": "main",
"index": 0
}
]
]
},
"Prepare Sections For Looping": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Get Document From Google Drive": {
"main": [
[
{
"node": "Extract Text Data From Google Document",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Split Document Text Into Sections": {
"main": [
[
{
"node": "Prepare Sections For Looping",
"type": "main",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Get Document From Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Extract Text Data From Google Document": {
"main": [
[
{
"node": "Split Document Text Into Sections",
"type": "main",
"index": 0
}
]
]
},
"Concatenate the context and section text": {
"main": [
[
{
"node": "Pinecone Vector Store",
"type": "main",
"index": 0
}
]
]
}
}
}How to Use an n8n Template
1
Create a New Workflow
Click "New Workflow" in your n8n dashboard to get started.
2
Copy & Paste Template
First, copy this template:
Click here to copy the JSON
.
Then, in n8n, click the three dots (···) → "Import from file" and paste the JSON code.
3
Customize the Nodes
Go through each node in the workflow to update inputs like spreadsheet IDs, email addresses, or message content. Adjust field mappings to match your data.
4
Grant Access
For nodes that connect to external apps (like Google Sheets or Slack), you'll need to grant access. Connect your accounts using OAuth or an API key and save the credentials in the node.
5
Test It
Run the workflow by clicking "Execute Node" for each step or "Run Once" for the whole thing. Check the right sidebar to inspect data and debug any errors (they'll show up in red).
6
Activate Workflow
Once everything works as expected, click the "Activate" toggle to turn your workflow on. You're all set!