Paolo Ronco This workflow corresponds to n8n.io template #13291 — we link there as the canonical source.
This workflow follows the Agent → Documentdefaultdataloader 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 →
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{
"node": "AI Agent - SKILLS",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"OpenAI Chat Model3": {
"ai_languageModel": [
[
{
"node": "AI Agent3",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Respond to Webhook": {
"main": [
[
{
"node": "Crypto: IP Hash",
"type": "main",
"index": 0
}
]
]
},
"SmallTalk Response": {
"main": [
[
{
"node": "Code-Smalltalk_response",
"type": "main",
"index": 0
}
]
]
},
"Code in JavaScript3": {
"main": [
[
{
"node": "AI Agent - SKILLS",
"type": "main",
"index": 0
}
]
]
},
"Default Data Loader": {
"ai_document": [
[
{
"node": "Qdrant Vector Store1",
"type": "ai_document",
"index": 0
}
]
]
},
"Code-Request_Parsing": {
"main": [
[
{
"node": "OpenAI Intent Router",
"type": "main",
"index": 0
}
]
]
},
"OpenAI Intent Router": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"Code-Response_Parsing": {
"main": [
[
{
"node": "Code: remove followup",
"type": "main",
"index": 0
}
]
]
},
"Code: remove followup": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Code-Smalltalk_response": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"Structured Output Parser3": {
"ai_outputParser": [
[
{
"node": "AI Agent3",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"MongoDB Atlas Vector SKILLS": {
"main": [
[
{
"node": "MongoDB Atlas Vector PROFILE",
"type": "main",
"index": 0
}
]
]
},
"MongoDB Atlas Vector PROFILE": {
"main": [
[
{
"node": "Code in JavaScript3",
"type": "main",
"index": 0
}
]
]
},
"Prepare Documents for Indexing": {
"main": [
[
{
"node": "Qdrant Vector Store1",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Search (Collection_name)": {
"main": [
[
{
"node": "Build context from Qdrant results",
"type": "main",
"index": 0
}
]
]
},
"Build context from Qdrant results": {
"main": [
[
{
"node": "AI Agent3",
"type": "main",
"index": 0
}
]
]
},
"Recursive Character Text Splitter": {
"ai_textSplitter": [
[
{
"node": "Default Data Loader",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Build context from Qdrant results1": {
"main": [
[
{
"node": "AI Agent",
"type": "main",
"index": 0
}
]
]
},
"set: ObjectName_Date/Folder & data": {
"main": [
[
{
"node": "Code: parsing",
"type": "main",
"index": 0
}
]
]
}
}
}
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About this workflow
Build a powerful, customizable AI chatbot for your WordPress website that intelligently retrieves posts, answers questions, and engages in natural conversations. This complete solution handles content indexing with OpenAI embeddings and vector storage (Qdrant or MongoDB), then…
Source: https://n8n.io/workflows/13291/ — original creator credit. Request a take-down →
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