iamvaar This workflow corresponds to n8n.io template #14254 — we link there as the canonical source.
This workflow follows the Agent → 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 →
{
"meta": {
"templateCredsSetupCompleted": true
},
"nodes": [
{
"id": "da09cc5d-4b12-4a17-8916-ed24446e041f",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
2400,
-48
],
"parameters": {
"width": 480,
"height": 656,
"content": "## Chat with databricks\n\n### How it works\n\n1. A Slack trigger initiates the workflow upon receiving a message. 2. The workflow fetches the Databricks table schema and parses it for the AI model. 3. An AI agent processes the data using a chat model and SQL tools to generate a response. 4. The workflow evaluates the AI output and sends a message back via Slack.\n\n### Setup steps\n\n- Configure the Slack Trigger node with your Slack app credentials.\n- Update the Databricks Configuration node with your target table and warehouse IDs.\n- Ensure the AI Agent has valid Google Gemini and Redis credentials.\n- Connect the Slack nodes to your workspace's notification channel.\n\n### Customization\n\nAdjust the system prompt in the AI agent to tailor the SQL generation style for your specific database schema."
},
"typeVersion": 1
},
{
"id": "4df1fe14-99b9-4c68-bf3d-c13f827decf2",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
2944,
128
],
"parameters": {
"color": 7,
"width": 608,
"height": 272,
"content": "## Initialize trigger and connection\n\nTriggering the process and preparing Databricks connection details."
},
"typeVersion": 1
},
{
"id": "4f0836ac-a5b8-4749-97d4-4c50c163d66f",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
3584,
80
],
"parameters": {
"color": 7,
"width": 656,
"height": 544,
"content": "## Process schema and AI agent\n\nParsing table schema and processing AI logic using Gemini and tools to execute generated sql query."
},
"typeVersion": 1
},
{
"id": "334d777e-dfa3-4df4-9d2a-eca0f2a3a7cc",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
4272,
-48
],
"parameters": {
"color": 7,
"width": 448,
"height": 560,
"content": "## Evaluate conditions and report\n\nHandling conditional responses and final Slack notifications."
},
"typeVersion": 1
},
{
"id": "a9f9654f-f76a-4646-b2eb-28adbe041d3b",
"name": "When Slack Message Received",
"type": "n8n-nodes-base.slackTrigger",
"position": [
2992,
240
],
"parameters": {
"options": {},
"trigger": [
"app_mention"
],
"watchWorkspace": true
},
"credentials": {
"slackApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "80f0882d-dd25-4e3b-b5c4-2b605359f3ff",
"name": "Parse Table Schema",
"type": "n8n-nodes-base.code",
"position": [
3648,
240
],
"parameters": {
"jsCode": "// Get the rows from the Databricks result\nconst rows = $json.result.data_array;\n\n// Map the rows into a structured object\nconst structuredSchema = rows.map(row => {\n return {\n column: row[0],\n type: row[1],\n description: row[2] || \"No description provided\"\n };\n});\n\n// Create a simplified string version for the AI Prompt\nconst simpleText = rows.map(row => `- ${row[0]} (${row[1]})`).join('\\n');\n\nreturn [{\n json: {\n table_name: \"franchises\", // You can pass this dynamically if needed\n columns_count: structuredSchema.length,\n schema_details: structuredSchema,\n ai_friendly_schema: simpleText\n }\n}];"
},
"typeVersion": 2
},
{
"id": "69c720ed-86e4-4edf-bbf8-79c01fb40c90",
"name": "Post to Slack Channel",
"type": "n8n-nodes-base.slack",
"position": [
4560,
80
],
"parameters": {
"text": "={{ $json.output }}",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "id",
"value": "={{ $('When Slack Message Received').item.json.channel }}"
},
"otherOptions": {
"includeLinkToWorkflow": false
}
},
"credentials": {
"slackApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.4
},
{
"id": "7d5794c0-19a4-4f31-b9bd-a86fb7eb4b4b",
"name": "Fetch Databricks Schema",
"type": "n8n-nodes-base.httpRequest",
"position": [
3408,
240
],
"parameters": {
"url": "https://enter-your-databricks-url.cloud.databricks.com/api/2.0/sql/statements",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "warehouse_id",
"value": "={{ $json.warehouse_id }}"
},
{
"name": "statement",
"value": "=DESCRIBE {{ $json.target_table }}"
},
{
"name": "wait_timeout",
"value": "50s"
}
]
},
"genericAuthType": "httpBearerAuth"
},
"credentials": {
"httpBearerAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.4
},
{
"id": "0faf472b-c048-4879-b0b1-147a81392aba",
"name": "Set Databricks Config",
"type": "n8n-nodes-base.set",
"position": [
3200,
240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "e4bb3b8f-f081-4c1e-a548-7156daed098b",
"name": "target_table",
"type": "string",
"value": "enter_your_databricks_table_id"
},
{
"id": "ffb52ca6-fbab-45ad-82cb-bb6872462ba4",
"name": "warehouse_id",
"type": "string",
"value": "enter_your_databricks_warehouse_id"
}
]
}
},
"typeVersion": 3.4
},
{
"id": "29de86cd-0e50-4976-93a6-c3812a911dfc",
"name": "SQL Data Analyst Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
3872,
240
],
"parameters": {
"text": "=You are an expert Databricks SQL analyst.\n\nYour goal is to answer the user's question by:\n1. Generating ONE SQL query\n2. Executing it using the SQL execution tool\n3. Interpreting the results and providing a clear, conversational answer\n\nSchema:\n{{ $json.ai_friendly_schema }}\n\nUser question:\n{{ $('When Slack Message Received').item.json.text }}\\\n\nTable ID:\n{{ $('Set Databricks Config').item.json.target_table }}\n\nRULES:\n- Use the SQL execution tool ONLY ONCE\n- After getting results, provide a clear answer in natural language\n- Format numbers and data in a readable way\n- If the question doesn't need database data, answer directly without using the tool",
"options": {},
"promptType": "define"
},
"typeVersion": 3.1
},
{
"id": "a91bbc88-0063-4a23-ba0f-2770e61070c1",
"name": "Gemini Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
3744,
448
],
"parameters": {
"options": {},
"modelName": "models/gemini-3.1-flash-lite-preview"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "8b21f5c3-0ec3-4cf6-9b36-362ce2c162ac",
"name": "Redis Chat Memory",
"type": "@n8n/n8n-nodes-langchain.memoryRedisChat",
"position": [
3936,
448
],
"parameters": {
"sessionKey": "={{ $('When Slack Message Received').item.json.thread_ts || $('When Slack Message Received').item.json.ts }}",
"sessionTTL": 60,
"sessionIdType": "customKey"
},
"credentials": {
"redis": {
"name": "<your credential>"
}
},
"typeVersion": 1.5
},
{
"id": "c6e06aa2-79fe-472c-8ceb-353843e12ee2",
"name": "Run Primary SQL Query",
"type": "n8n-nodes-base.httpRequestTool",
"position": [
4112,
448
],
"parameters": {
"url": "https://enter-your-databricks-url-here.cloud.databricks.com/api/2.0/sql/statements",
"method": "POST",
"options": {},
"sendBody": true,
"authentication": "genericCredentialType",
"bodyParameters": {
"parameters": [
{
"name": "warehouse_id",
"value": "={{ $('Set Databricks Config').item.json.warehouse_id }}"
},
{
"name": "statement",
"value": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('parameters1_Value', ``, 'string') }}"
},
{
"name": "wait_timeout",
"value": "50s"
}
]
},
"genericAuthType": "httpBearerAuth",
"toolDescription": "Executes a single SQL query on Databricks.\n\nIMPORTANT:\n- This tool can be used ONLY ONCE per request.\n- The AI must provide a complete SQL query in the \"statement\" field.\n- After execution, the AI must produce a final answer and MUST NOT call this tool again.\n\nReturns query results in JSON format."
},
"credentials": {
"httpBearerAuth": {
"name": "<your credential>"
}
},
"typeVersion": 4.4
},
{
"id": "490fac20-41ef-465f-81ec-b0efed3ffcab",
"name": "If Output Valid",
"type": "n8n-nodes-base.if",
"position": [
4320,
240
],
"parameters": {
"options": {},
"conditions": {
"options": {
"version": 3,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "loose"
},
"combinator": "and",
"conditions": [
{
"id": "fe5bbd74-d036-4c5b-a6e5-090ef9dcf515",
"operator": {
"type": "string",
"operation": "contains"
},
"leftValue": "={{ $('When Slack Message Received').item.json.thread_ts \n ? 'thread[' + $('When Slack Message Received').item.json.thread_ts + ']' \n : 'message_ts[' + $('When Slack Message Received').item.json.ts + ']' \n}}",
"rightValue": "message"
}
]
},
"looseTypeValidation": true
},
"typeVersion": 2.3
},
{
"id": "24e93021-0a48-4f42-a61f-c176cfdd7571",
"name": "Post Error to Slack",
"type": "n8n-nodes-base.slack",
"position": [
4576,
336
],
"parameters": {
"text": "={{ $json.output }}",
"select": "channel",
"channelId": {
"__rl": true,
"mode": "id",
"value": "={{ $('When Slack Message Received').item.json.channel }}"
},
"otherOptions": {
"thread_ts": {
"replyValues": {
"thread_ts": "={{ $('When Slack Message Received').item.json.thread_ts }}"
}
},
"includeLinkToWorkflow": false
}
},
"credentials": {
"slackApi": {
"name": "<your credential>"
}
},
"typeVersion": 2.4
}
],
"connections": {
"Gemini Model": {
"ai_languageModel": [
[
{
"node": "SQL Data Analyst Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"If Output Valid": {
"main": [
[
{
"node": "Post to Slack Channel",
"type": "main",
"index": 0
}
],
[
{
"node": "Post Error to Slack",
"type": "main",
"index": 0
}
]
]
},
"Redis Chat Memory": {
"ai_memory": [
[
{
"node": "SQL Data Analyst Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"Parse Table Schema": {
"main": [
[
{
"node": "SQL Data Analyst Agent",
"type": "main",
"index": 0
}
]
]
},
"Run Primary SQL Query": {
"ai_tool": [
[
{
"node": "SQL Data Analyst Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"Set Databricks Config": {
"main": [
[
{
"node": "Fetch Databricks Schema",
"type": "main",
"index": 0
}
]
]
},
"SQL Data Analyst Agent": {
"main": [
[
{
"node": "If Output Valid",
"type": "main",
"index": 0
}
]
]
},
"Fetch Databricks Schema": {
"main": [
[
{
"node": "Parse Table Schema",
"type": "main",
"index": 0
}
]
]
},
"When Slack Message Received": {
"main": [
[
{
"node": "Set Databricks Config",
"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.
googlePalmApihttpBearerAuthredisslackApi
Pro
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About this workflow
This workflow is divided into three functional phases: Initialization, AI Processing, and Response Delivery.
Source: https://n8n.io/workflows/14254/ — original creator credit. Request a take-down →
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