Chandan Singh This workflow corresponds to n8n.io template #11971 — we link there as the canonical source.
This workflow follows the Datatable → HTTP Request 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": "DtuDy7JtfybUocZU",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Template - MySQL Schema Embeddings in Pinecone",
"tags": [
{
"id": "pH0FjEImo9WfHqWC",
"name": "Template",
"createdAt": "2025-12-19T17:46:15.670Z",
"updatedAt": "2025-12-19T17:46:15.670Z"
}
],
"nodes": [
{
"id": "cda78dc2-e6da-4039-87e2-829d0295a726",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1696,
-1232
],
"parameters": {
"width": 736,
"height": 960,
"content": "## Workflow Overview - MySQL Schema Embeddings in Pinecone\n\nThis workflow synchronizes MySQL database table schemas with a vector database in a controlled, idempotent manner.\nEach database table is indexed as a single vector to preserve complete schema context for AI-based retrieval and reasoning.\nThe workflow prevents duplicate vectors and automatically handles schema changes by detecting differences and re-indexing only when required.\n\n### How it works\n- The workflow starts with a manual trigger and loads global configuration values.\n- All database tables are discovered and processed one-by-one inside a loop.\n- For each table, a normalized schema representation is generated and a deterministic hash is calculated.\n- A metadata table is checked to determine whether a vector already exists for the table.\n- If a vector exists, the stored schema hash is compared with the current hash to detect schema changes.\n- When a schema change is detected, the existing vector and metadata are deleted.\n- The updated table schema is embedded as a single vector (without chunking) and upserted into the vector database.\n- Vector identifiers and schema hashes are persisted for future executions.\n\n### Setup steps\n- Set the MySQL database name using mysql_database_name.\n- Configure the Pinecone index name using pinecone_index.\n- Set the vector namespace using vector_namespace.\n- Configure the Pinecone index host using vector_index_host.\n- Add your Pinecone API key using pinecone_apikey.\n- Select the embedding model using embedding_model.\n- Configure text processing options:\n - chunk_size\n - chunk_overlap\n- Set the metadata table identifier using dataTable_Id.\n- Save and run the workflow manually to perform the initial schema synchronization.\n\n### Limitations\n- This workflow indexes database table schemas only. Table data (rows) are not embedded or indexed.\n- Each table is stored as a single vector. Very large or highly complex schemas may approach model token limits depending on the selected embedding model.\n- Schema changes are detected using a hash-based comparison. Non-structural changes that do not affect the schema representation will not trigger re-indexing."
},
"typeVersion": 1
},
{
"id": "5c8558c4-b7a8-4dae-8f6e-839f4867e57d",
"name": "Sync DB Schema to Vector Store",
"type": "n8n-nodes-base.manualTrigger",
"position": [
-800,
-752
],
"parameters": {},
"typeVersion": 1
},
{
"id": "5d1589fb-28dd-45a7-8002-1b0a0ab8cf18",
"name": "Load Global Configuration",
"type": "n8n-nodes-base.set",
"position": [
-624,
-752
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "{\n \"vector_namespace\": \"\",\n \"embedding_model\": \"\",\n \"chunk_size\": \"\",\n \"chunk_overlap\": \"\",\n \"pinecone_index\": \"\",\n \"mysql_database_name\" : \"\",\n \"vector_index_host\" : \"\",\n \"pinecone_apikey\" : \"\",\n \"dataTable_Id\" : \"\"\n}"
},
"typeVersion": 3.4
},
{
"id": "dd9a808b-0be9-4af6-9b9f-3832db3b8306",
"name": "Fetch All Database Tables",
"type": "n8n-nodes-base.mySql",
"position": [
-464,
-752
],
"parameters": {
"query": "SELECT CONCAT('SHOW CREATE TABLE `', TABLE_NAME, '`;') AS queries, TABLE_NAME as tbl, DATABASE() as db\nFROM information_schema.tables\nWHERE table_schema = DATABASE()\nAND TABLE_TYPE = 'BASE TABLE';",
"options": {},
"operation": "executeQuery"
},
"credentials": {
"mySql": {
"name": "<your credential>"
}
},
"typeVersion": 2.4
},
{
"id": "b5924c88-248d-4e26-a82d-ebbe8ad3c52e",
"name": "Set Table Schema Context",
"type": "n8n-nodes-base.set",
"position": [
-48,
-736
],
"parameters": {
"mode": "raw",
"options": {},
"jsonOutput": "={\n \"vector_id\" : \"mysql_schema::{{ $node['Loop Over executable queries'].json.db }}::{{ $node['Loop Over executable queries'].json.tbl }}\",\n\"table_name\" : \"{{ $node['Loop Over executable queries'].json.tbl }}\",\n\"source_type\" : \"mysql_schema\"\n}\n"
},
"typeVersion": 3.4
},
{
"id": "695c1804-f072-4272-9a14-80d1103946ec",
"name": "Fetch Table Schema Definition",
"type": "n8n-nodes-base.mySql",
"position": [
112,
-736
],
"parameters": {
"query": "{{ $node['Loop Over executable queries'].json.queries }}",
"options": {},
"operation": "executeQuery"
},
"credentials": {
"mySql": {
"name": "<your credential>"
}
},
"typeVersion": 2.4
},
{
"id": "9fc19d70-5435-46ce-9ede-1f511a4e52ed",
"name": "Generate Schema Hash",
"type": "n8n-nodes-base.code",
"position": [
288,
-736
],
"parameters": {
"jsCode": "// Only hash what defines the schema\nconst schemaText = $input.first().json['Create Table']\n\nlet hash = 5381;\nfor (let i = 0; i < schemaText.length; i++) {\n hash = ((hash << 5) + hash) + schemaText.charCodeAt(i);\n}\n\nreturn {\n ...$input.first().json,\n vector_id: $('Set Table Schema Context').first().json.vector_id,\n source_type: $('Set Table Schema Context').first().json.source_type,\n schema_hash: hash.toString()\n};\n"
},
"typeVersion": 2
},
{
"id": "5a8761b1-dc3a-4b6a-9bf7-8cdba5c185eb",
"name": "Check Existing Vector Metadata",
"type": "n8n-nodes-base.dataTable",
"position": [
544,
-736
],
"parameters": {
"filters": {
"conditions": [
{
"keyName": "vector_id",
"keyValue": "={{ $node['Generate Schema Hash'].json.vector_id }}"
}
]
},
"operation": "get",
"returnAll": true,
"dataTableId": {
"__rl": true,
"mode": "id",
"value": "={{ $('Load Global Configuration').item.json.dataTable_Id }}"
}
},
"typeVersion": 1,
"alwaysOutputData": true
},
{
"id": "d5302713-4ea7-4448-9c7a-fc4ae75ad56f",
"name": "Vector Exists?",
"type": "n8n-nodes-base.if",
"position": [
736,
-736
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "db0cb7c0-f0b9-44b6-8427-fc0787c9e3d5",
"operator": {
"type": "object",
"operation": "empty",
"singleValue": true
},
"leftValue": "={{ $node[\"Check Existing Vector Metadata\"].json }}",
"rightValue": 0
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.2,
"alwaysOutputData": false
},
{
"id": "2de84f88-0e49-4a87-8e58-4fbb7b1aca4b",
"name": "Schema Changed?",
"type": "n8n-nodes-base.if",
"position": [
880,
-640
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "ff61f3da-c1b0-49da-b7a5-6103932fd4ac",
"operator": {
"type": "string",
"operation": "notEquals"
},
"leftValue": "={{ $node['Vector Exists?'].json.schema_hash }}",
"rightValue": "={{ $node['Generate Schema Hash'].json.schema_hash }}"
}
]
}
},
"typeVersion": 2.2
},
{
"id": "5bc39c82-e002-4d23-a702-b5f2b5a11e25",
"name": "Delete Existing Vector (Pinecone)",
"type": "n8n-nodes-base.httpRequest",
"position": [
1136,
-656
],
"parameters": {
"url": "={{ $('Load Global Configuration').item.json.vector_index_host }}/vectors/delete",
"method": "POST",
"options": {
"response": {
"response": {}
},
"allowUnauthorizedCerts": true
},
"jsonBody": "={\n \"filter\": {\n \"vector_id\": {\n \"$eq\": \"{{ $('Generate Schema Hash').item.json.vector_id }}\"\n }\n },\n \"namespace\": \"{{ $('Load Global Configuration').item.json.vector_namespace }}\"\n}",
"sendBody": true,
"sendHeaders": true,
"specifyBody": "json",
"headerParameters": {
"parameters": [
{
"name": "Api-Key",
"value": "={{ $('Load Global Configuration').item.json.pinecone_apikey }}"
}
]
}
},
"typeVersion": 4.3
},
{
"id": "d8048466-aca3-4f57-b1b4-413c13b01bfe",
"name": "Delete Vector Metadata Record",
"type": "n8n-nodes-base.dataTable",
"position": [
1312,
-656
],
"parameters": {
"filters": {
"conditions": [
{
"keyName": "vector_id",
"keyValue": "={{ $('Generate Schema Hash').item.json.vector_id }}"
}
]
},
"options": {},
"operation": "deleteRows",
"dataTableId": {
"__rl": true,
"mode": "list",
"value": "2IZryiCLAMg3BCTg",
"cachedResultUrl": "/projects/Jz2ZhOCR6j1Rldr1/datatables/2IZryiCLAMg3BCTg",
"cachedResultName": "rag_embedding_log"
}
},
"typeVersion": 1
},
{
"id": "5f8e1571-e4a1-460d-a5f1-05b887e66944",
"name": "Split Schema Text (No Chunking)",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
1600,
-512
],
"parameters": {
"options": {
"splitCode": "markdown"
},
"chunkSize": "={{ $('Load Global Configuration').item.json.chunk_size }}",
"chunkOverlap": "={{ $('Load Global Configuration').item.json.chunk_overlap }}"
},
"typeVersion": 1
},
{
"id": "2f9bc643-4faa-47aa-9daa-3f200cb1d8ae",
"name": "Generate Schema Embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
1488,
-640
],
"parameters": {
"model": "={{ $('Load Global Configuration').item.json.embedding_model }}",
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "20ca023e-76bb-46dd-81ed-a67c06da80aa",
"name": "Upsert Vector Metadata Record",
"type": "n8n-nodes-base.dataTable",
"position": [
1888,
-544
],
"parameters": {
"columns": {
"value": {
"vector_id": "={{ $node['Generate Schema Hash'].json.vector_id }}",
"table_name": "={{ $node['Generate Schema Hash'].json.Table }}",
"schema_hash": "={{ $node['Generate Schema Hash'].json.schema_hash }}"
},
"schema": [
{
"id": "vector_id",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "vector_id",
"defaultMatch": false
},
{
"id": "table_name",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "table_name",
"defaultMatch": false
},
{
"id": "schema_hash",
"type": "string",
"display": true,
"removed": false,
"readOnly": false,
"required": false,
"displayName": "schema_hash",
"defaultMatch": false
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
},
"filters": {
"conditions": [
{
"keyName": "vector_id",
"keyValue": "={{ $node['Generate Schema Hash'].json.vector_id }}"
}
]
},
"options": {},
"operation": "upsert",
"dataTableId": {
"__rl": true,
"mode": "list",
"value": "2IZryiCLAMg3BCTg",
"cachedResultUrl": "/projects/Jz2ZhOCR6j1Rldr1/datatables/2IZryiCLAMg3BCTg",
"cachedResultName": "rag_embedding_log"
}
},
"typeVersion": 1
},
{
"id": "11105b1b-70e2-4115-ab1f-06f99bcdac21",
"name": "Prepare Schema Document",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
"position": [
1600,
-640
],
"parameters": {
"options": {
"metadata": {
"metadataValues": [
{
"name": "table_name",
"value": "={{ $('Generate Schema Hash').item.json.Table }}"
},
{
"name": "vector_id",
"value": "={{ $('Generate Schema Hash').item.json.vector_id }}"
},
{
"name": "source_type",
"value": "={{ $('Generate Schema Hash').item.json.source_type }}"
},
{
"name": "schema_hash",
"value": "={{ $('Generate Schema Hash').item.json.schema_hash }}"
}
]
}
},
"jsonData": "={{ $('Generate Schema Hash').item.json['Create Table'] }}",
"jsonMode": "expressionData"
},
"typeVersion": 1
},
{
"id": "e1c786bf-e9f5-4ddd-b347-edecef79fa9d",
"name": "Insert Schema Vector to Pinecone",
"type": "@n8n/n8n-nodes-langchain.vectorStorePinecone",
"position": [
1536,
-800
],
"parameters": {
"mode": "insert",
"options": {
"pineconeNamespace": "={{ $node['Load Global Configuration'].json.vector_namespace }}"
},
"pineconeIndex": {
"__rl": true,
"mode": "list",
"value": "dbrag",
"cachedResultName": "dbrag"
}
},
"credentials": {
"pineconeApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "a0bbece4-1c0a-488d-8ec0-b6b84ddfebd4",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-848,
-880
],
"parameters": {
"width": 1280,
"height": 560,
"content": "## Setup Workflow & Schema Discovery\n Initialize configuration, discover database tables, and generate schema fingerprints"
},
"typeVersion": 1
},
{
"id": "79fbc689-c786-41b0-978a-63e866f4cef8",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
480,
-880
],
"parameters": {
"width": 560,
"height": 560,
"content": "## Vector Existence & Change Detection\n Duplicate prevention and schema drift logic"
},
"typeVersion": 1
},
{
"id": "16f257cc-f4ba-44cb-bc12-f766228b4248",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1088,
-880
],
"parameters": {
"width": 960,
"height": 560,
"content": "## Vector Cleanup & Indexing \n Safe deletion and re-indexing pipeline"
},
"typeVersion": 1
},
{
"id": "94b17575-2d49-41b4-b8d6-0a4be701f049",
"name": "Loop Over executable queries",
"type": "n8n-nodes-base.splitInBatches",
"position": [
-272,
-752
],
"parameters": {
"options": {
"reset": false
}
},
"executeOnce": false,
"typeVersion": 3
},
{
"id": "bfd300b1-8c5d-49b4-b662-75cc796f9bc9",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-592,
-1232
],
"parameters": {
"width": 464,
"height": 320,
"content": "## How to Use This Workflow\n@[youtube](elykvVRjVL0)"
},
"typeVersion": 1
}
],
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "dad112b4-574c-4cc0-b54b-125fffd6922f",
"connections": {
"Vector Exists?": {
"main": [
[
{
"node": "Insert Schema Vector to Pinecone",
"type": "main",
"index": 0
}
],
[
{
"node": "Schema Changed?",
"type": "main",
"index": 0
}
]
]
},
"Schema Changed?": {
"main": [
[
{
"node": "Delete Existing Vector (Pinecone)",
"type": "main",
"index": 0
}
],
[
{
"node": "Loop Over executable queries",
"type": "main",
"index": 0
}
]
]
},
"Generate Schema Hash": {
"main": [
[
{
"node": "Check Existing Vector Metadata",
"type": "main",
"index": 0
}
]
]
},
"Prepare Schema Document": {
"ai_document": [
[
{
"node": "Insert Schema Vector to Pinecone",
"type": "ai_document",
"index": 0
}
]
]
},
"Set Table Schema Context": {
"main": [
[
{
"node": "Fetch Table Schema Definition",
"type": "main",
"index": 0
}
]
]
},
"Fetch All Database Tables": {
"main": [
[
{
"node": "Loop Over executable queries",
"type": "main",
"index": 0
}
]
]
},
"Load Global Configuration": {
"main": [
[
{
"node": "Fetch All Database Tables",
"type": "main",
"index": 0
}
]
]
},
"Generate Schema Embeddings": {
"ai_embedding": [
[
{
"node": "Insert Schema Vector to Pinecone",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Loop Over executable queries": {
"main": [
[],
[
{
"node": "Set Table Schema Context",
"type": "main",
"index": 0
}
]
]
},
"Delete Vector Metadata Record": {
"main": [
[
{
"node": "Insert Schema Vector to Pinecone",
"type": "main",
"index": 0
}
]
]
},
"Fetch Table Schema Definition": {
"main": [
[
{
"node": "Generate Schema Hash",
"type": "main",
"index": 0
}
]
]
},
"Upsert Vector Metadata Record": {
"main": [
[
{
"node": "Loop Over executable queries",
"type": "main",
"index": 0
}
]
]
},
"Check Existing Vector Metadata": {
"main": [
[
{
"node": "Vector Exists?",
"type": "main",
"index": 0
}
]
]
},
"Sync DB Schema to Vector Store": {
"main": [
[
{
"node": "Load Global Configuration",
"type": "main",
"index": 0
}
]
]
},
"Split Schema Text (No Chunking)": {
"ai_textSplitter": [
[
{
"node": "Prepare Schema Document",
"type": "ai_textSplitter",
"index": 0
}
]
]
},
"Insert Schema Vector to Pinecone": {
"main": [
[
{
"node": "Upsert Vector Metadata Record",
"type": "main",
"index": 0
}
]
]
},
"Delete Existing Vector (Pinecone)": {
"main": [
[
{
"node": "Delete Vector Metadata Record",
"type": "main",
"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.
mySqlopenAiApipineconeApi
Pro
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About this workflow
This workflow synchronizes MySQL database table schemas with a vector database in a controlled, idempotent manner. Each database table is indexed as a single vector to preserve complete schema context for AI-based retrieval and reasoning. The workflow prevents duplicate vectors…
Source: https://n8n.io/workflows/11971/ — original creator credit. Request a take-down →
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