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Documentation Complète
📋 Optimisation Contextuelle Automatisée pour Google Drive
💡 Description
Ce workflow automatisé transforme vos documents Google Drive en données exploitables en les intégrant dans une base de vecteurs Pinecone. Utilisant les technologies avancées d'OpenRouter et de Google Gemini, il segmente les documents en sections contextuelles pour améliorer la recherche et le traitement des informations. Cela permet aux entreprises de gérer efficacement de grands volumes de données textuelles, augmentant ainsi la productivité et la précision des recherches. En simplifiant le processus d'intégration de données complexes, ce workflow offre une solution robuste pour l'analyse contextuelle à grande échelle.
📈 Impact & ROI: En réduisant le temps nécessaire à la gestion et à l'analyse des documents, ce workflow améliore considérablement l'efficacité opérationnelle, permettant une meilleure prise de décision basée sur les données.
🚀 Fonctionnalités Clés
- ✅ Transformation automatique des documents Google Drive en vecteurs
- ✅ Amélioration de la recherche grâce au contexte enrichi
- ✅ Intégration fluide avec OpenRouter et Pinecone
- ✅ Gestion efficace des grandes quantités de texte
📊 Architecture Technique
17
Nodes
Nodes
13
Connexions
Connexions
4
Services
Services
🔌 Services Intégrés
Google DriveOpenRouterPineconeGoogle Gemini
🔧 Composition du Workflow
| Node | Type | Description |
|---|---|---|
| When clicking ‘Test workflow’ | manualTrigger | Traitement des données |
| Loop Over Items | splitInBatches | Division des données en plusieurs branches |
| OpenRouter Chat Model | @n8n/n8n-nodes-langchain.lmChatOpenRouter | Traitement des données |
| Pinecone Vector Store | @n8n/n8n-nodes-langchain.vectorStorePinecone | Traitement des données |
| Embeddings Google Gemini | @n8n/n8n-nodes-langchain.embeddingsGoogleGemini | Traitement des données |
| Default Data Loader | @n8n/n8n-nodes-langchain.documentDefaultDataLoader | Traitement des données |
| Recursive Character Text Splitter | @n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter | Division des données en plusieurs branches |
| Get Document From Google Drive | googleDrive | Traitement des données |
| Extract Text Data From Google Document | extractFromFile | Traitement des données |
| Split Document Text Into Sections | code | Traitement des données |
| Prepare Sections For Looping | splitOut | Division des données en plusieurs branches |
| Sticky Note | stickyNote | Traitement des données |
| Sticky Note1 | stickyNote | Traitement des données |
| AI Agent - Prepare Context | @n8n/n8n-nodes-langchain.agent | Traitement des données |
| Concatenate the context and section text | set | Traitement des données |
| Sticky Note2 | stickyNote | Traitement des données |
| Sticky Note3 | stickyNote | Traitement des données |
📖 Guide d'Implémentation
- Import du workflow: Téléchargez le fichier JSON et importez-le dans votre instance n8n
- Configuration des credentials: Configurez les accès pour chaque service utilisé
- Personnalisation: Adaptez les paramètres selon vos besoins spécifiques
- Test: Exécutez le workflow en mode test pour vérifier le bon fonctionnement
- Activation: Activez le workflow pour une exécution automatique
🏷️ Tags
Structure JSON
Voir le code JSON complet
{
"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 ‘Test workflow’",
"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": [
920,
540
],
"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": [
660,
0
],
"parameters": {
"jsCode": "let split_text = \"—---------------------------—-------------[SECTIONEND]—---------------------------—-------------\";\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": [
880,
0
],
"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": [
400,
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
},
{
"id": "4a7a788c-8e5b-453c-ae52-a4522048992d",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
640,
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 ‘Test workflow’": {
"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
}
]
]
}
}
}
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