This workflow follows the OpenAI Embeddings → Execute Workflow Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
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{
"name": "tool_search_products",
"nodes": [
{
"id": "Start",
"name": "Start",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"typeVersion": 1,
"position": [
-460,
120
],
"parameters": {}
},
{
"id": "Embed Query",
"name": "Embed Query",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"typeVersion": 1,
"position": [
-220,
120
],
"parameters": {
"model": "text-embedding-3-small",
"text": "={{ $json.query }}"
}
},
{
"id": "Qdrant Search",
"name": "Qdrant Search",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"typeVersion": 1,
"position": [
20,
120
],
"parameters": {
"operation": "retrieve",
"collection": "products",
"topK": 5,
"includeMetadata": true
}
},
{
"id": "Format",
"name": "Format",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
260,
120
],
"parameters": {
"jsCode": "const rows = $json.matches || [];\nreturn [{ results: rows.map(r => ({ id: r.payload?.id, name: r.payload?.name, category: r.payload?.category, price: r.payload?.price, description: r.payload?.description, score: r.score })) }];"
}
}
],
"connections": {
"Start": {
"main": [
[
{
"node": "Embed Query",
"type": "main",
"index": 0
}
]
]
},
"Embed Query": {
"main": [
[
{
"node": "Qdrant Search",
"type": "main",
"index": 0
}
]
]
},
"Qdrant Search": {
"main": [
[
{
"node": "Format",
"type": "main",
"index": 0
}
]
]
}
},
"active": false
}
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About this workflow
tool_search_products. Uses executeWorkflowTrigger, embeddingsOpenAi, vectorStoreQdrant. Event-driven trigger; 4 nodes.
Source: https://github.com/ogu83/n8n-ai-agent-lab/blob/main/ep3-rag-pipeline/workflows/tool_search_products.json — original creator credit. Request a take-down →
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