Mary Newhauser This workflow corresponds to n8n.io template #7170 — we link there as the canonical source.
This workflow follows the Chainretrievalqa → Retrievervectorstore 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 →
{
"id": "hWxVTNvDEcGofp5O",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "rag-with-weaviate",
"tags": [],
"nodes": [
{
"id": "4cfa559c-9cec-4a74-84d5-9cf4a2d7915a",
"name": "Weaviate Vector Store",
"type": "@n8n/n8n-nodes-langchain.vectorStoreWeaviate",
"position": [
432,
288
],
"parameters": {
"mode": "insert",
"options": {
"textKey": "text"
},
"weaviateCollection": {
"__rl": true,
"mode": "id",
"value": "FileUpload"
}
},
"credentials": {
"weaviateApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "c87c8fe2-56bf-405f-a91a-3b1af7cf2e8c",
"name": "Default Data Loader",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
512,
496
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{}
]
}
},
"jsonData": "={{ $json.text }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "ac0c796a-3b1d-4ba6-83e5-23a673e4628d",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
384,
496
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "d89bd8be-a275-4a57-a1a6-6006302ba3dd",
"name": "Recursive Character Text Splitter1",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
544,
656
],
"parameters": {
"options": {},
"chunkSize": 500
},
"typeVersion": 1
},
{
"id": "dcabfdff-749b-4442-933d-409b1479d2c8",
"name": "Extract from File",
"type": "n8n-nodes-base.extractFromFile",
"position": [
-48,
288
],
"parameters": {
"options": {
"maxPages": 99
},
"operation": "pdf",
"binaryPropertyName": "PDF_File"
},
"typeVersion": 1
},
{
"id": "779add5c-4770-472a-a2e9-934e1e2e4569",
"name": "Edit Fields",
"type": "n8n-nodes-base.set",
"position": [
128,
288
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "d719d94d-6597-402c-8958-dd270de82ce6",
"name": "text",
"type": "string",
"value": "={{ $json.text }}"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "eda536c5-d88f-45e4-9505-d3b1e6c5fa27",
"name": "When chat message received",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
992,
288
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "94ed5f2b-fb86-42e8-a778-ab51d7a49d42",
"name": "Weaviate Vector Store1",
"type": "@n8n/n8n-nodes-langchain.vectorStoreWeaviate",
"position": [
1280,
624
],
"parameters": {
"options": {},
"weaviateCollection": {
"__rl": true,
"mode": "list",
"value": "FileUpload",
"cachedResultName": "FileUpload"
}
},
"credentials": {
"weaviateApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.3
},
{
"id": "ae3a9b66-a5c8-4108-980c-1c3ace05e528",
"name": "Question and Answer Chain",
"type": "@n8n/n8n-nodes-langchain.chainRetrievalQa",
"position": [
1168,
288
],
"parameters": {
"text": "=Using only the attached Weaviate vector store collection (and no external knowledge), answer the following query:\n{{ $json.chatInput }}",
"options": {},
"promptType": "define"
},
"typeVersion": 1.6
},
{
"id": "d9110fef-f3ae-4c3a-bfc0-b3bfe0f6fcb0",
"name": "Vector Store Retriever",
"type": "@n8n/n8n-nodes-langchain.retrieverVectorStore",
"position": [
1280,
480
],
"parameters": {},
"typeVersion": 1
},
{
"id": "79aaa15c-f0d6-4c88-bd15-ff7835131b2b",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
1152,
480
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4.1-mini"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "9f961ef2-5466-475d-9ccb-1436469dee00",
"name": "Embeddings OpenAI1",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1360,
768
],
"parameters": {
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "d46d3a49-ec16-40aa-928e-291fc90b9f2d",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-256,
176
],
"parameters": {
"color": 5,
"width": 544,
"height": 336,
"content": "## Part 1: Manually upload data \nIn this example, we manually upload a 100+ page article from arXiv called [\"A Survey of Large Language Models\"](https://arxiv.org/pdf/2303.18223).\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_**Note: This is a simple implementation of loading data. You can replace this block with your own (more advanced) data pipeline!**_"
},
"typeVersion": 1
},
{
"id": "9b192882-6d54-43c5-9ccb-cca0bcf456f4",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
944,
176
],
"parameters": {
"color": 6,
"width": 640,
"height": 736,
"content": "## Part 3: Perform RAG over PDF file with Weaviate\nEnter your query by running the Chat Node and get a RAG response grounded in context."
},
"typeVersion": 1
},
{
"id": "431f55e3-9599-4de7-a363-2e82e6581101",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
-256,
-128
],
"parameters": {
"color": 4,
"width": 992,
"height": 288,
"content": "# RAG over a PDF file with Weaviate\nThis workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes. \n\n## Prerequisites\n1. **An existing Weaviate cluster.** You can view instructions for setting up a **local cluster** with Docker [here](https://weaviate.io/developers/weaviate/installation/docker-compose#starter-docker-compose-file) or a **Weaviate Cloud** cluster [here](https://weaviate.io/developers/wcs/quickstart).\n2. **API keys** to generate embeddings and power chat models. We use [OpenAI](https://openai.com/), but feel free to switch out the models as you like.\n3. **Self-hosted n8n instance.** See this [video](https://www.youtube.com/watch?v=kq5bmrjPPAY&t=108s) for how to get set up in just three minutes.\n\n\n\ud83d\udc9a Sign up [here](https://console.weaviate.cloud/?utm_source=recipe&utm_campaign=n8n&utm_content=n8n_arxiv_template) for a 14-day free trial of Weaviate Cloud (no credit card required)."
},
"typeVersion": 1
},
{
"id": "588d9eee-f769-4c6b-9b16-2b1790118a4d",
"name": "Upload PDF",
"type": "n8n-nodes-base.formTrigger",
"position": [
-224,
288
],
"parameters": {
"options": {},
"formTitle": "Upload your file here",
"formFields": {
"values": [
{
"fieldType": "file",
"fieldLabel": "PDF File",
"multipleFiles": false,
"requiredField": true,
"acceptFileTypes": ".pdf"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "4d4244ef-c4af-4eb1-8389-6bd95cc5613e",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
304,
176
],
"parameters": {
"color": 3,
"width": 624,
"height": 688,
"content": "## Part 2: Embed and load data into Weaviate collection\nWe generate embeddings for the full-text of the article and store them in Weaviate.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n_**Note: We don't add any metadata to Weaviate in this example. To add metadata, click on the Default Data Loader node \u2192 `Add Option` \u2192 `Metadata`.**_"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "61bb6430-21b1-4443-8924-34a83eab6965",
"connections": {
"Upload PDF": {
"main": [
[
{
"node": "Extract from File",
"type": "main",
"index": 0
}
]
]
},
"Edit Fields": {
"main": [
[
{
"node": "Weaviate Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Embeddings OpenAI": {
"ai_embedding": [
[
{
"node": "Weaviate Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Extract from File": {
"main": [
[
{
"node": "Edit Fields",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "Question and Answer Chain",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Embeddings OpenAI1": {
"ai_embedding": [
[
{
"node": "Weaviate Vector Store1",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Weaviate Vector Store",
"type": "ai_document",
"index": 0
}
]
]
},
"Weaviate Vector Store": {
"main": [
[]
]
},
"Vector Store Retriever": {
"ai_retriever": [
[
{
"node": "Question and Answer Chain",
"type": "ai_retriever",
"index": 0
}
]
]
},
"Weaviate Vector Store1": {
"ai_vectorStore": [
[
{
"node": "Vector Store Retriever",
"type": "ai_vectorStore",
"index": 0
}
]
]
},
"When chat message received": {
"main": [
[
{
"node": "Question and Answer Chain",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter1": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
}
}
}
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.
openAiApiweaviateApi
Pro
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About this workflow
This workflow allows you to upload a PDF file and ask questions about it using the Question and Answer Chain and the Weaviate Vector Store nodes.
Source: https://n8n.io/workflows/7170/ — original creator credit. Request a take-down →
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