Webhook, Pinecone + OpenAI + n8n을 기반으로 한 지능형 문서 질문 응답 시스템
고급
이것은Internal Wiki, AI RAG분야의자동화 워크플로우로, 30개의 노드를 포함합니다.주로 Webhook, GoogleDrive, ManualTrigger, Agent, RespondToWebhook 등의 노드를 사용하며. 기반 OpenAI GPT, Pinecone 벡터 데이터베이스 및 Google Drive 통합의 문서 질문 답변 시스템
사전 요구사항
- •HTTP Webhook 엔드포인트(n8n이 자동으로 생성)
- •Google Drive API 인증 정보
- •OpenAI API Key
- •Pinecone API Key
사용된 노드 (30)
워크플로우 미리보기
노드 연결 관계를 시각적으로 표시하며, 확대/축소 및 이동을 지원합니다
워크플로우 내보내기
다음 JSON 구성을 복사하여 n8n에 가져오면 이 워크플로우를 사용할 수 있습니다
{
"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": "워크플로우 실행 시",
"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": "채팅 메시지 수신 시",
"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": "벡터 저장소로 질문에 답변",
"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": "벡터 저장소로 질문에 답변1",
"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": "Webhook에 응답",
"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": [
[
{
"node": "b1e8830f-8cfe-40ef-b611-76e70cd9184b",
"type": "main",
"index": 0
}
]
]
},
"b1e8830f-8cfe-40ef-b611-76e70cd9184b": {
"main": [
[
{
"node": "01b59805-abdd-49ff-a553-0dddf3ed1450",
"type": "main",
"index": 0
}
]
]
},
"26b93e8c-0a72-4491-90fe-55b5f5da02a0": {
"main": [
[
{
"node": "21174f84-5f7b-45bc-944b-0f0a7c2ffd49",
"type": "main",
"index": 0
}
]
]
},
"21174f84-5f7b-45bc-944b-0f0a7c2ffd49": {
"main": [
[
{
"node": "d84e6051-cc04-4f51-b9c3-0e69e2193571",
"type": "main",
"index": 0
}
]
]
},
"784924f6-d197-4666-9a05-e36020021ae2": {
"ai_memory": [
[
{
"node": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"type": "ai_memory",
"index": 0
}
]
]
},
"2454b5ff-e53e-41c5-9844-f171d63ee2d4": {
"ai_memory": [
[
{
"node": "b1e8830f-8cfe-40ef-b611-76e70cd9184b",
"type": "ai_memory",
"index": 0
}
]
]
},
"3185a781-28af-4ee0-be7b-2183b80ce0e3": {
"ai_embedding": [
[
{
"node": "d84e6051-cc04-4f51-b9c3-0e69e2193571",
"type": "ai_embedding",
"index": 0
}
]
]
},
"34d9e834-3aba-4c80-8c4d-4206fcdbfac3": {
"ai_languageModel": [
[
{
"node": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"8a0e2476-661e-4702-8563-ec0b12033884": {
"ai_embedding": [
[
{
"node": "dfefbee7-5125-42da-b696-f343dc89573c",
"type": "ai_embedding",
"index": 0
}
]
]
},
"9e3f06a1-900b-427e-8775-dad8ddc1de80": {
"ai_embedding": [
[
{
"node": "e223bcf1-7085-433a-a51d-708b0c36a2e4",
"type": "ai_embedding",
"index": 0
}
]
]
},
"31a4456c-4a35-4beb-9c4b-de49e460e492": {
"ai_languageModel": [
[
{
"node": "00b70c8d-5940-4eef-84c4-b87d69df3ab9",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"87db20d4-7a7c-48a6-a29a-2fd089f93a43": {
"ai_languageModel": [
[
{
"node": "b1e8830f-8cfe-40ef-b611-76e70cd9184b",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"df06efec-1f75-4309-923b-044e1c1991f3": {
"ai_languageModel": [
[
{
"node": "e33b7eff-0166-43b2-ab7e-5f53063164a9",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"8eccc3bb-654f-4a92-8074-9d2418afae12": {
"ai_document": [
[
{
"node": "d84e6051-cc04-4f51-b9c3-0e69e2193571",
"type": "ai_document",
"index": 0
}
]
]
},
"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",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"id": {
"main": [
[
{
"node": "4240e62e-0b44-4dbd-9cff-87a404a496bd",
"type": "main",
"index": 0
}
]
]
},
"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": [
[
{
"node": "26b93e8c-0a72-4491-90fe-55b5f5da02a0",
"type": "main",
"index": 0
}
]
]
},
"e33b7eff-0166-43b2-ab7e-5f53063164a9": {
"ai_tool": [
[
{
"node": "b1e8830f-8cfe-40ef-b611-76e70cd9184b",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}자주 묻는 질문
이 워크플로우를 어떻게 사용하나요?
위의 JSON 구성 코드를 복사하여 n8n 인스턴스에서 새 워크플로우를 생성하고 "JSON에서 가져오기"를 선택한 후, 구성을 붙여넣고 필요에 따라 인증 설정을 수정하세요.
이 워크플로우는 어떤 시나리오에 적합한가요?
고급 - 내부 위키, AI RAG
유료인가요?
이 워크플로우는 완전히 무료이며 직접 가져와 사용할 수 있습니다. 다만, 워크플로우에서 사용하는 타사 서비스(예: OpenAI API)는 사용자 직접 비용을 지불해야 할 수 있습니다.
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