This workflow follows the Agent → Chat Trigger 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": "vectordb_many_pdfs",
"nodes": [
{
"parameters": {
"content": "## 1. VectorDB \uc800\uc7a5\n- \uad6c\uae00 \ub4dc\ub77c\uc774\ube0c \uacbd\ub85c\uc5d0\n- \ud30c\uc77c\uc774 \uc800\uc7a5\ub418\uba74\n- Vector DB\ub85c \ubcc0\ud658",
"height": 620,
"width": 1220
},
"type": "n8n-nodes-base.stickyNote",
"position": [
-420,
0
],
"typeVersion": 1,
"id": "1bbcbcb5-affd-4019-a5cd-fe288a584d74",
"name": "Sticky Note"
},
{
"parameters": {
"content": "## 2. \ucc44\ud305\uc744 \ud1b5\ud574 RAG \uad6c\uc131\n- Vector DB\ub97c \ubd88\ub7ec\uc634\n- Tool\ub85c \uc5f0\uacb0",
"height": 620,
"width": 780
},
"type": "n8n-nodes-base.stickyNote",
"position": [
820,
0
],
"typeVersion": 1,
"id": "eaf8ff20-ffdc-4b49-a549-e695329196b1",
"name": "Sticky Note1"
},
{
"parameters": {
"mode": "insert",
"qdrantCollection": {
"__rl": true,
"value": "docs",
"mode": "list",
"cachedResultName": "docs"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"typeVersion": 1.1,
"position": [
380,
220
],
"id": "8c8bcd1a-c9fa-4ef8-9c7c-c3ddb68e96f0",
"name": "Qdrant Vector Store",
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1.2,
"position": [
340,
380
],
"id": "1ae1aa85-59da-4a8a-a6ba-5fd1277a173b",
"name": "Embeddings OpenAI",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"typeVersion": 1,
"position": [
500,
380
],
"id": "5c693753-12e9-4a75-801d-52276beb358a",
"name": "Default Data Loader"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"typeVersion": 1,
"position": [
400,
500
],
"id": "77bf61e4-9578-4433-8332-ba63dc94e16e",
"name": "Recursive Character Text Splitter"
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
940,
140
],
"id": "07555b54-1b44-4666-96d6-5f3d932f031d",
"name": "When chat message received"
},
{
"parameters": {
"promptType": "define",
"text": "={{ $json.chatInput }}",
"options": {
"systemMessage": "You are a multilingual AI assistant specializing in document analysis and question answering. Your task is to use the provided context to answer questions in the same language they were asked, regardless of the language of the source documents.\n\nFollow these guidelines:\n\nCarefully analyze the given context to extract relevant information.\n\n- **Always refer to vector store to find exact information.**\n\nIf the question is in a different language than the context (e.g., Korean question with English context), first understand the question\u2019s intent.\n\nSearch for relevant information in the context, even if it\u2019s in a different language.\n\nProvide a comprehensive answer based on the information found in the context.\n\nProvide your answer in the same language as the question was asked.\n\nIf the context contains information but you need to translate it to match the question language, do so accurately while preserving the original meaning.\n\nIf you cannot find relevant information in the context, acknowledge this limitation in your answer.\n\nNever invent information not present in the context.\n\nWhen answering in a language different from the context, ensure your translation maintains the original technical concepts and terminology.\n\nFor complex or technical topics, consider providing both the original terms and their translations for clarity.\n\nAfter each piece of information used in your answer, cite the source document using the format [Attach: Document Title, Page X] or [Document Title, Page X] if author information is not available.\n\nAt the end of your answer, include a \u201cReferences\u201d section listing all cited sources with:\n\nAuthor(s) name(s) when available\n\nDocument title\n\nPublication year if available\n\nPage numbers referenced in your answer\n\nUse the following pieces of context to answer the user\u2019s question:\n\n{context}\n\nRemember: Always respond in the same language as the question, providing a complete and academic-style answer based solely on the information available in the context. Include academic-style citations for each piece of information and provide a proper references section at the end of your answer."
}
},
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 1.9,
"position": [
1160,
140
],
"id": "fff29b68-e7d8-4e03-8bbd-73d44cd2b8c0",
"name": "AI Agent"
},
{
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.2,
"position": [
1080,
380
],
"id": "ae893700-ff56-469f-ada1-db40e2d214fa",
"name": "OpenAI Chat Model",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {},
"type": "n8n-nodes-base.manualTrigger",
"typeVersion": 1,
"position": [
-320,
140
],
"id": "2b8cfd97-9484-4aed-a1da-d4fe9eaf3377",
"name": "When clicking \u2018Test workflow\u2019"
},
{
"parameters": {
"resource": "fileFolder",
"limit": 10,
"filter": {
"folderId": {
"__rl": true,
"value": "13MNLhucMhOcJcTPOSA-eswiivTPvHQMm",
"mode": "list",
"cachedResultName": "PAPER",
"cachedResultUrl": "https://drive.google.com/drive/folders/13MNLhucMhOcJcTPOSA-eswiivTPvHQMm"
}
},
"options": {}
},
"type": "n8n-nodes-base.googleDrive",
"typeVersion": 3,
"position": [
-160,
140
],
"id": "99d8934c-a3b4-4e43-814e-4ba32c28441c",
"name": "Google Drive",
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "n8n-nodes-base.splitInBatches",
"typeVersion": 3,
"position": [
40,
140
],
"id": "45b0c9b3-f4d0-42c3-a826-ad1e5d3b99c8",
"name": "Loop Over Items"
},
{
"parameters": {
"operation": "download",
"fileId": {
"__rl": true,
"value": "={{ $json.id }}",
"mode": "id"
},
"options": {}
},
"type": "n8n-nodes-base.googleDrive",
"typeVersion": 3,
"position": [
200,
220
],
"id": "f374aec3-5fdc-491a-870e-625d26dbd388",
"name": "Google Drive1",
"credentials": {
"googleDriveOAuth2Api": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1.2,
"position": [
1320,
820
],
"id": "b8b7b440-99dd-454a-97eb-a4ae7b94862f",
"name": "Embeddings OpenAI1",
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"mode": "retrieve-as-tool",
"toolName": "rag_knowledge_base",
"toolDescription": "work with vector store in qdrant collection",
"qdrantCollection": {
"__rl": true,
"value": "docs",
"mode": "list",
"cachedResultName": "docs"
},
"options": {}
},
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"typeVersion": 1.1,
"position": [
1320,
680
],
"id": "4f9746d4-77f5-4f0d-9168-ee30ab3b7168",
"name": "Qdrant Vector Store1",
"credentials": {
"qdrantApi": {
"name": "<your credential>"
}
},
"disabled": true
},
{
"parameters": {
"description": "call this tool to check vector store",
"workflowId": {
"__rl": true,
"value": "JJaI1r2cpbHsIzKI",
"mode": "list",
"cachedResultName": "QA-chain"
},
"workflowInputs": {
"mappingMode": "defineBelow",
"value": {
"query": "={{ $fromAI('query', ``, 'string') }}"
},
"matchingColumns": [
"query"
],
"schema": [
{
"id": "query",
"displayName": "query",
"required": false,
"defaultMatch": false,
"display": true,
"canBeUsedToMatch": true,
"type": "string",
"removed": false
}
],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"typeVersion": 2.2,
"position": [
1340,
380
],
"id": "00729025-0a66-418c-98e6-94909ab38b69",
"name": "Call n8n Workflow Tool"
}
],
"connections": {
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"When clicking \u2018Test workflow\u2019": {
"main": [
[
{
"node": "Google Drive",
"type": "main",
"index": 0
}
]
]
},
"Google Drive": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Loop Over Items": {
"main": [
[],
[
{
"node": "Google Drive1",
"type": "main",
"index": 0
}
]
]
},
"Google Drive1": {
"main": [
[
{
"node": "Qdrant Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Vector Store": {
"main": [
[
{
"node": "Loop Over Items",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Qdrant Vector Store1": {
"ai_tool": [
[]
]
},
"Call n8n Workflow Tool": {
"ai_tool": [
[
{
"node": "AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "297f34fd-2b77-4606-bb48-cf22d43c1692",
"meta": {
"templateCredsSetupCompleted": true
},
"id": "pILIBxeV28qmCOZS",
"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.
googleDriveOAuth2ApiopenAiApiqdrantApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
vectordb_many_pdfs. Uses vectorStoreQdrant, embeddingsOpenAi, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter. Chat trigger; 16 nodes.
Source: https://github.com/aieeiee/fc_nocoderag/blob/main/workflow/2_2_vectordb_many_pdfs.json — original creator credit. Request a take-down →
Related workflows
Workflows that share integrations, category, or trigger type with this one. All free to copy and import.
This workflow acts as a 24/7 sales agent, engaging leads across WhatsApp, Instagram, Facebook, Telegram, and your website. It intelligently transcribes audio messages, answers questions using a knowle
Chat Trigger, Memory Postgres Chat, Tool Workflow +20
• Create a Google Drive folder to watch. • Connect your Google Drive account in n8n and authorize access. • Point the Google Drive Trigger node to this folder (new/modified files trigger the flow).
Agent, Chat Trigger, Memory Buffer Window +14
The workflow operates through a three-step process that handles incoming chat messages with intelligent tool orchestration: Message Trigger: The node triggers whenever a user message arrives and passe
Chat Trigger, Memory Postgres Chat, OpenAI Embeddings +16
Build an All-Source Knowledge Assistant with Claude, RAG, Perplexity, and Drive. Uses chatTrigger, memoryPostgresChat, embeddingsOpenAi, rerankerCohere. Chat trigger; 40 nodes.
Chat Trigger, Memory Postgres Chat, OpenAI Embeddings +16
Advanced Ai Demo Presented At Ai Developers 14 Meetup. Uses slack, stickyNote, textSplitterRecursiveCharacterTextSplitter, embeddingsOpenAi. Chat trigger; 39 nodes.
Slack, Text Splitter Recursive Character Text Splitter, OpenAI Embeddings +14