Webhook、Pinecone + OpenAI + n8n に基づくスマートドキュメントQ&Aシステム
上級
これはInternal Wiki, AI RAG分野の自動化ワークフローで、30個のノードを含みます。主にWebhook, GoogleDrive, ManualTrigger, Agent, RespondToWebhookなどのノードを使用。 OpenAI GPT、Pineconeベクターデータベース、およびGoogle Drive統合を基盤としたドキュメントQ&Aシステム
前提条件
- •HTTP Webhookエンドポイント（n8nが自動生成）
- •Google Drive API認証情報
- •OpenAI API Key
- •Pinecone API Key
使用ノード (30)
カテゴリー
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "UVMlpwIIsDBBFclU",
"meta": {
"instanceId": "92e36925b2d06addd7a010605535ce53ac105737436355f7e52e2980c726ed3d",
"templateCredsSetupCompleted": true
},
"name": "AI-Powered Document QA System using Webhook, Pinecone + OpenAI + n8n",
"tags": [
{
"id": "Bv4R1pgV3YCnUGME",
"name": "webhook",
"createdAt": "2025-07-04T05:26:19.837Z",
"updatedAt": "2025-07-04T05:26:19.837Z"
},
{
"id": "lTpSGA7vnSvUGQs6",
"name": "lovable",
"createdAt": "2025-07-04T05:26:29.453Z",
"updatedAt": "2025-07-04T05:26:29.453Z"
},
{
"id": "oKGIn6U0wpeHShTN",
"name": "working flow",
"createdAt": "2025-06-02T06:27:44.762Z",
"updatedAt": "2025-06-02T06:27:44.762Z"
}
],
"nodes": [
{
"id": "784badb8-0cf6-434d-9d5d-1670757b548b",
"name": "ワークフロー実行時",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-300,
-40
],
"parameters": {},
"typeVersion": 1
},
{
"id": "26b93e8c-0a72-4491-90fe-55b5f5da02a0",
"name": "Google Drive",
"type": "n8n-nodes-base.googleDrive",
"position": [
-80,
-40
],
"parameters": {
"filter": {
"folderId": {
"__rl": true,
"mode": "list",
"value": "1NgITWoqBgLAVof9bxF0jIrVToQ9c919u",
"cachedResultUrl": "https://drive.google.com/drive/folders/1NgITWoqBgLAVof9bxF0jIrVToQ9c919u",
"cachedResultName": "contract document"
}
},
"options": {},
"resource": "fileFolder"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "RFbg76pQ49AUClT1",
"name": "name"
}
},
"typeVersion": 3
},
{
"id": "21174f84-5f7b-45bc-944b-0f0a7c2ffd49",
"name": "Google Drive1",
"type": "n8n-nodes-base.googleDrive",
"position": [
140,
-40
],
"parameters": {
"fileId": {
"__rl": true,
"mode": "id",
"value": "={{ $json.id }}"
},
"options": {},
"operation": "download"
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "RFbg76pQ49AUClT1",
"name": "name"
}
},
"typeVersion": 3
},
{
"id": "d84e6051-cc04-4f51-b9c3-0e69e2193571",
"name": "Pinecone Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
360,
-40
],
"parameters": {
"mode": "insert",
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "package1536",
"cachedResultName": "package1536"
}
},
"credentials": {
"pineconeApi": {
"id": "id",
"name": "PineconeApi account 2"
}
},
"typeVersion": 1.3
},
{
"id": "3185a781-28af-4ee0-be7b-2183b80ce0e3",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
300,
160
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "id",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "8eccc3bb-654f-4a92-8074-9d2418afae12",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
500,
180
],
"parameters": {
"options": {},
"dataType": "binary",
"textSplittingMode": "custom"
},
"typeVersion": 1.1
},
{
"id": "9a6a4542-81f0-4fa6-b0fa-6fbfcf5fb3d3",
"name": "Recursive Character Text Splitter",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
600,
400
],
"parameters": {
"options": {},
"chunkOverlap": 100
},
"typeVersion": 1
},
{
"id": "60485603-13aa-46c8-9824-011b75d368bd",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
-180
],
"parameters": {
"width": 1300,
"height": 980,
"content": "## Document Loading \n1. Connect to Google Drive folder to access Contract Agreement Documents\n2. Download and Vectorize the Data using Vector Embedding \n3. Store in Pinecone Database"
},
"typeVersion": 1
},
{
"id": "349466bc-c0c7-4e4e-9e9c-78554a3123ae",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
940
],
"parameters": {
"width": 1300,
"height": 720,
"content": "## Query Document via Chat (for testing)"
},
"typeVersion": 1
},
{
"id": "id",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-100,
980
],
"webhookId": "id",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"name": "AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
120,
980
],
"parameters": {
"options": {
"systemMessage": "*Role*\nYou are a highly experienced contracting, commercial and legal adviser who thoroughly understands the contract related to shipping, clearing and forwarding agreements and advise and reply to chat queries looking into the pinecone vector database and respond accordingly. \n\n**Instructions**\nyou will receive chat query to which you have to reply back in chat\nyou will only look for information in the pinecone vector databse\nyou will not create your own reply if you don't get the answer from the database\n\nNote:\nbe polite and professional in your response\ncan use emojis where it is appropriate\n"
}
},
"typeVersion": 2
},
{
"id": "34d9e834-3aba-4c80-8c4d-4206fcdbfac3",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
80,
1200
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "id",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "784924f6-d197-4666-9a05-e36020021ae2",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
200,
1200
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "00b70c8d-5940-4eef-84c4-b87d69df3ab9",
"name": "Answer questions with a vector store",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
380,
1200
],
"parameters": {
"description": "When ever there is a query from chat, use this pinecone vector database to analyse and construct the response. "
},
"typeVersion": 1.1
},
{
"id": "dfefbee7-5125-42da-b696-f343dc89573c",
"name": "Pinecone Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
180,
1360
],
"parameters": {
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "package1536",
"cachedResultName": "package1536"
}
},
"credentials": {
"pineconeApi": {
"id": "id",
"name": "PineconeApi account 2"
}
},
"typeVersion": 1.3
},
{
"id": "8a0e2476-661e-4702-8563-ec0b12033884",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
200,
1500
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "SCKN5KUziIpM8NB7",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "31a4456c-4a35-4beb-9c4b-de49e460e492",
"name": "OpenAI Chat Model1",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
520,
1420
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "SCKN5KUziIpM8NB7",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "7aa47a91-19f9-4a0e-b1b2-5867cf4982ef",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1660,
-160
],
"parameters": {
"width": 1200,
"height": 980,
"content": "## Query document from a user interface connectied via Webhook\n"
},
"typeVersion": 1
},
{
"id": "c9da6a17-a0aa-4d3c-844a-1c3785a956eb",
"name": "Webhook",
"type": "n8n-nodes-base.webhook",
"position": [
1900,
0
],
"webhookId": "12b44ee5-c43e-430c-a1d4-4fc5ff5e45c4",
"parameters": {
"path": "12b44ee5-c43e-430c-a1d4-4fc5ff5e45c4",
"options": {},
"httpMethod": "POST",
"responseMode": "responseNode"
},
"typeVersion": 2
},
{
"id": "b1e8830f-8cfe-40ef-b611-76e70cd9184b",
"name": "AI Agent1",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
2120,
0
],
"parameters": {
"text": "=the query: {{ $json.body.query }}",
"options": {
"systemMessage": "*Role*\nYou are a highly experienced contracting, commercial and legal adviser who thoroughly understands the contract related to shipping, clearing and forwarding agreements and advise and reply to chat queries looking into the pinecone vector database and respond accordingly. \n\n**Instructions**\nyou will receive chat query to which you have to reply back in chat\nyou will only look for information in the pinecone vector databse\nyou will not create your own reply if you don't get the answer from the database\n\nNote:\nbe polite and professional in your response\ncan use emojis where it is appropriate\n"
},
"promptType": "define"
},
"typeVersion": 2
},
{
"id": "87db20d4-7a7c-48a6-a29a-2fd089f93a43",
"name": "OpenAI Chat Model2",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2020,
220
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "id",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "2454b5ff-e53e-41c5-9844-f171d63ee2d4",
"name": "Simple Memory1",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"disabled": true,
"position": [
2180,
220
],
"parameters": {},
"typeVersion": 1.3
},
{
"id": "e33b7eff-0166-43b2-ab7e-5f53063164a9",
"name": "Answer questions with a vector store1",
"type": "@n8n/n8n-nodes-langchain.toolVectorStore",
"position": [
2380,
220
],
"parameters": {
"description": "When ever there is a query from chat, use this pinecone vector database to analyse and construct the response. "
},
"typeVersion": 1.1
},
{
"id": "e223bcf1-7085-433a-a51d-708b0c36a2e4",
"name": "Pinecone Vector Store2",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
2180,
380
],
"parameters": {
"options": {},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "package1536",
"cachedResultName": "package1536"
}
},
"credentials": {
"pineconeApi": {
"id": "HqCFDvnsq0D6wXpJ",
"name": "PineconeApi account 2"
}
},
"typeVersion": 1.3
},
{
"id": "9e3f06a1-900b-427e-8775-dad8ddc1de80",
"name": "Embeddings OpenAI2",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
2200,
520
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"id": "id",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "df06efec-1f75-4309-923b-044e1c1991f3",
"name": "OpenAI Chat Model3",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
2520,
440
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"id": "id",
"name": "OpenAi account 5"
}
},
"typeVersion": 1.2
},
{
"id": "01b59805-abdd-49ff-a553-0dddf3ed1450",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
2480,
0
],
"parameters": {
"options": {
"responseKey": "={{ $json.output }}"
}
},
"typeVersion": 1.4
},
{
"id": "05fd0853-0ebd-4a99-9345-982c9e664e27",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1000,
-180
],
"parameters": {
"color": 4,
"width": 560,
"height": 980,
"content": "This project demonstrates how to build a Retrieval-Augmented Generation (RAG) system using n8n, which:\n🧾 Downloads any pdf file format documents from Google Drive\n📚 Converts them into vector embeddings using OpenAI\n🔍 Stores and searches them in Pinecone Vector DB\n💬 Allows natural language querying of contracts using AI Agents\n\n## Document Loading & RAG Setup\nThis flow automates:\nReading documents from a Google Drive folder\nVectorizing using text-embedding-3-small\nUploading vectors into Pinecone for later semantic search\n\n### 🧱 Workflow Structure\nA [Manual Trigger] --> B[Google Drive Search]\nB --> C[Google Drive Download]\nC --> D[Pinecone Vector Store]\nD --> E[Default Data Loader]\nE --> F[Recursive Character Text Splitter]\nE --> G[OpenAI Embedding]\n\n### 🪜 Steps\nManual Trigger: Kickstarts the workflow on demand for loading new documents.\nGoogle Drive Search & Download\nNode: Google Drive (Search: file/folder), Credentials required to access google drive folders and files\nDownloads PDF documents from the google drive\n\n#### Recursive Text Splitter to Break long documents into overlapping chunks\nSettings:\nChunk Size: 1000\nChunk Overlap: 100\n\n#### OpenAI Embedding\nModel: text-embedding-3-small\nUsed for creating document vectors\n\n#### Pinecone Vector Store\nIndex: package1536\nBatch Size: 200\nSettings:\nType: Dense\nRegion: us-east-1\nMode: Insert Documents\n\n\n"
},
"typeVersion": 1
},
{
"id": "7f1cc5b2-104e-4571-a838-29c71c79bd08",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1000,
940
],
"parameters": {
"color": 4,
"width": 560,
"height": 720,
"content": "## Quyerying the Documetn via Chat \nThis flow enables chat-style querying of stored documents using OpenAI-powered agents with vector memory.\n\n### 🧱 Workflow Diagram\n A[Webhook (chat message)] --> B[AI Agent]\n B --> C[OpenAI Chat Model]\n B --> D[Simple Memory]\n B --> E[Answer with Vector Store]\n E --> F[Pinecone Vector Store]\n F --> G[Embeddings OpenAI]\n### 🪜 Components\nChat Trigger\nAI Agent Node\n\nHandles query flow using:\nChat Model: OpenAI GPT\nMemory: Simple Memory\nTool: Question Answer with Vector Store\nPinecone Vector Store\nConnected via same embedding index as Flow 1 Embeddings\nEnsures document chunks are retrievable using vector similarity\nResponse Node\nReturns final AI response to user via chat response\n\n"
},
"typeVersion": 1
},
{
"id": "e11b8fbd-c24b-469f-a196-1e507a6d3e75",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
1080,
-160
],
"parameters": {
"color": 4,
"width": 560,
"height": 980,
"content": "## 🌐 Flow 3: UI-Based Query with webhook connecting to Lovable\nThis flow uses a web UI built using Lovable to query contracts directly from a form interface.\n\n### 📥 Webhook Setup for Lovable\nWebhook Node\nMethod: POST\nURL: your webhook url\nResponse: Using 'Respond to Webhook' Node\n\n### 🧱 Workflow Logic\n A[Webhook (Lovable Form)] --> B[AI Agent]\n B --> C[OpenAI Chat Model]\n B --> D[Simple Memory]\n B --> E[Answer with Vector Store]\n E --> F[Pinecone Vector Store]\n F --> G[Embeddings OpenAI]\n B --> H[Respond to Webhook]\n\n### 💡 Lovable UI\nUsers can submit:\nFull Name\nEmail\nDepartment\nFreeform Query\n\nData is sent via webhook to n8n and responded with the answer from contract content.\n\n### 🔍 Use Cases\nContract Querying for Legal/HR teams\nProcurement & Vendor Agreement QA\nCustomer Support Automation (based on terms)\nRAG Systems for private document knowledge\n\n⚙️ Tools & Tech Stack\nComponent\tTool Used\nAI Embedding\tOpenAI text-embedding-3-small\nVector DB\tPinecone\nChunking\tRecursive Text Splitter\nAI Agent\tOpenAI GPT Chat\nAutomation\tn8n\nUI Integration\tLovable (form-based)\n\n\n\n"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "460c7740-a2d1-41f7-92d5-fc9113152663",
"connections": {
"c9da6a17-a0aa-4d3c-844a-1c3785a956eb": {
"main": [
[
{
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"b1e8830f-8cfe-40ef-b611-76e70cd9184b": {
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"784924f6-d197-4666-9a05-e36020021ae2": {
"ai_memory": [
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"34d9e834-3aba-4c80-8c4d-4206fcdbfac3": {
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"8eccc3bb-654f-4a92-8074-9d2418afae12": {
"ai_document": [
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},
"dfefbee7-5125-42da-b696-f343dc89573c": {
"ai_vectorStore": [
[
{
"node": "00b70c8d-5940-4eef-84c4-b87d69df3ab9",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"e223bcf1-7085-433a-a51d-708b0c36a2e4": {
"ai_vectorStore": [
[
{
"node": "e33b7eff-0166-43b2-ab7e-5f53063164a9",
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}
]
]
},
"id": {
"main": [
[
{
"node": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"type": "main",
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}
]
]
},
"9a6a4542-81f0-4fa6-b0fa-6fbfcf5fb3d3": {
"ai_textSplitter": [
[
{
"node": "8eccc3bb-654f-4a92-8074-9d2418afae12",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"00b70c8d-5940-4eef-84c4-b87d69df3ab9": {
"ai_tool": [
[
{
"node": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"type": "ai_tool",
"index": 0
}
]
]
},
"784badb8-0cf6-434d-9d5d-1670757b548b": {
"main": [
[
{
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"type": "main",
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}
]
]
},
"e33b7eff-0166-43b2-ab7e-5f53063164a9": {
"ai_tool": [
[
{
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"type": "ai_tool",
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}
}
}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
上級 - 内部Wiki, AI RAG検索拡張
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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