VECTORIZE TUSS TABLE
Description
Categories
🔧 Engineering🤖 AI & Machine Learning
Nodes Used
n8n-nodes-base.coden8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.stickyNoten8n-nodes-base.manualTriggern8n-nodes-base.splitInBatches@n8n/n8n-nodes-langchain.vectorStorePGVector@n8n/n8n-nodes-langchain.embeddingsGoogleGemini@n8n/n8n-nodes-langchain.documentDefaultDataLoader@n8n/n8n-nodes-langchain.textSplitterTokenSplitter
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Last Updated11/28/2025
workflow.json
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"instanceId": "98986c4dc0b4b2af9fd0666d254f8b80e6e6b2044e2f3f3eedc76242999f4b5e"
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"name": "VECTORIZE TUSS TABLE",
"tags": [],
"nodes": [
{
"id": "c3de0ca4-e8e7-4e27-93a3-82cf65c0153f",
"name": "Postgres PGVector Store2",
"type": "@n8n/n8n-nodes-langchain.vectorStorePGVector",
"onError": "continueRegularOutput",
"position": [
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"parameters": {
"mode": "insert",
"options": {},
"tableName": "{your_table_name_here}"
},
"credentials": {
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"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"width": 432,
"height": 208,
"content": "## DATABASE AND/OR FILE CONNECTION\n\n### HERE YOU SHOULD PROVIDE THE TUSS TABLE, WITH ONE COLUMN NAMED CD_ITEM AND ANOTHER NAMED DS_ITEM\n"
},
"typeVersion": 1
},
{
"id": "104c2ec3-9ad4-4cd0-851f-be002e7b5a13",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"width": 352,
"content": "## VECTORIZATION\n\nHERE THE VECTORIZATION WILL BE PERFORMED IN A POSTGRES DATABASE USING PGVECTOR\n"
},
"typeVersion": 1
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{
"id": "ac59dec8-36c6-473d-8622-a6c9e6dddfa9",
"name": "VECTORIZE TUSS TABLE",
"type": "n8n-nodes-base.manualTrigger",
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"parameters": {},
"typeVersion": 1
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"id": "c9f12d90-9b9f-42fb-8519-6367b4ce11e2",
"name": "ORACLE DATABASE CONNECTION",
"type": "n8n-nodes-oracle-database-parameterization.Oracle Database with Parameterization",
"position": [
176,
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],
"parameters": {
"query": "SELECT CD_ITEM, DS_ITEM FROM {sua tabela aqui }"
},
"credentials": {
"oracleCredentials": {
"id": "Dj4SbJh38O9fJ7kg",
"name": "PROTHEUS - PRODUÇÃO (APPN8N)"
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"name": "COLLECTION OF MEDICAL PROCEDURES",
"type": "n8n-nodes-base.code",
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"parameters": {
"jsCode": "return $input.first().json.rows;"
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"name": "FOR - MEDICAL PROCEDURES",
"type": "n8n-nodes-base.splitInBatches",
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"parameters": {
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"executeOnce": false,
"typeVersion": 3,
"alwaysOutputData": false
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"type": "n8n-nodes-base.stickyNote",
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"parameters": {
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"height": 224,
"content": "## DATA LOADER\n\nResponsible for preparing and formatting the textual data before vectorization, ensuring that each piece of information has the correct structure and metadata—such as the medical procedure code and its description—to generate precise and useful vectors for semantic search.\n"
},
"typeVersion": 1
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{
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"name": "Data Loader",
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"position": [
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"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "cd_item",
"value": "={{ $json.cd_item }}"
},
{
"name": "procedureName",
"value": "={{ $json.ds_item }}"
}
]
}
},
"jsonData": "=Medical procedure code: {{ $json.cd_item }}\nMedical procedure description: {{ $json.ds_item }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
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"active": false,
"pinData": {},
"settings": {
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"Token Splitter": {
"ai_textSplitter": [
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"node": "Data Loader",
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"VECTORIZE TUSS TABLE": {
"main": [
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"node": "ORACLE DATABASE CONNECTION",
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"Embeddings Google Gemini": {
"ai_embedding": [
[
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"type": "ai_embedding",
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},
"FOR - MEDICAL PROCEDURES": {
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"Postgres PGVector Store2": {
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{
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"type": "main",
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"ORACLE DATABASE CONNECTION": {
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"COLLECTION OF MEDICAL PROCEDURES": {
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