Robert Breen This workflow corresponds to n8n.io template #6745 — we link there as the canonical source.
This workflow follows the Agent → Chat Trigger 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 →
{
"nodes": [
{
"id": "1045c9ed-ad7c-45b8-94f7-27139c158f92",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
580,
80
],
"parameters": {
"sessionKey": "={{ $('Embedable chat for users to ask questions of bigquery').item.json.sessionId }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "3fb1381a-42b6-4459-86f1-9f4c25aba299",
"name": "OpenAI Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
440,
80
],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o",
"cachedResultName": "gpt-4o"
},
"options": {}
},
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
},
"typeVersion": 1.2
},
{
"id": "640fb030-18d8-405b-ab1d-37d1fd625ef8",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
720,
60
],
"parameters": {
"jsonSchemaExample": "{\n\t\"query\": \"sql query and no other text\"\n}"
},
"typeVersion": 1.2
},
{
"id": "cd9edc59-cb69-4e32-8984-026f6c0c0331",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
-460,
-620
],
"parameters": {
"width": 1700,
"height": 1000,
"content": "Talk-to-Data: Instant BigQuery SQL Generator\n### Need more help?\n- **LinkedIn:** https://www.linkedin.com/in/robertbreen\n- **Email:** rbreen@ynteractive.com "
},
"typeVersion": 1
},
{
"id": "9fab1e8c-d541-4972-976e-34e596818a9f",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-1280,
-620
],
"parameters": {
"color": 5,
"width": 780,
"height": 1000,
"content": "\n#### \u2699\ufe0f Setup Instructions\n\n1. **Import the workflow** \n - n8n \u2192 **Workflows \u2192 Import from File** (or **Paste JSON**) \u2192 **Save**\n\n2. **Add credentials** \n | Service | Where to create credentials | Node(s) to update |\n |---------|----------------------------|-------------------|\n | **OpenAI** | <https://platform.openai.com> \u2192 Create API key | **OpenAI Chat Model** |\n | **Google BigQuery** | Google Cloud Console \u2192 IAM & Admin \u2192 Service Account JSON key | **Google BigQuery** (schema + query) |\n\n3. **Point the schema fetcher to your dataset** \n - In **Google BigQuery1** you\u2019ll see: \n ```sql\n SELECT table_name, column_name, data_type\n FROM `n8nautomation-453001.email_leads_schema.INFORMATION_SCHEMA.COLUMNS`\n ``` \n - Replace **`n8nautomation-453001.email_leads_schema`** with **`YOUR_PROJECT.YOUR_DATASET`**. \n - Keep the rest of the query the same\u2014BigQuery\u2019s `INFORMATION_SCHEMA` always surfaces `table_name`, `column_name`, and `data_type`.\n\n4. **Update the execution node** \n - Open **Google BigQuery** (the second BigQuery node). \n - In **Project ID** select your project. \n - The **SQL Query** field is already `{{ $json.output.query }}` so it will run whatever the AI returns.\n\n5. **(Optional)Embed the chat interface** \n\n6. **Test end-to-end** \n - Open the embedded chat widget. \n - Ask: *\u201cHow many distinct email leads were created last week?\u201d* \n - After a few seconds the workflow will return a table of results\u2014or an error if the schema lacks the requested fields.\n - As specific questions about your data\n\n8. **Activate** \n - Toggle **Active** so the chat assistant is available 24/7.\n\n"
},
"typeVersion": 1
},
{
"id": "18e74b72-1776-4d60-a81e-a2c5f589794d",
"name": "Embedable chat for users to ask questions of bigquery",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-380,
-280
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "b57350a1-cd14-424a-b027-381619e738f8",
"name": "Output all table, and column names in your schema",
"type": "n8n-nodes-base.googleBigQuery",
"position": [
-200,
-80
],
"parameters": {
"options": {},
"sqlQuery": "SELECT \n table_name,\n column_name,\n data_type\nFROM `n8nautomation-453001.email_leads_schema.INFORMATION_SCHEMA.COLUMNS`\n",
"projectId": {
"__rl": true,
"mode": "list",
"value": "n8nautomation-453001",
"cachedResultUrl": "https://console.cloud.google.com/bigquery?project=n8nautomation-453001",
"cachedResultName": "n8nAutomation"
}
},
"credentials": {
"googleBigQueryOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "4d8e1a77-bc7b-417a-a528-635c92d7dd16",
"name": "Combine into one field",
"type": "n8n-nodes-base.aggregate",
"position": [
-40,
-200
],
"parameters": {
"options": {},
"aggregate": "aggregateAllItemData"
},
"typeVersion": 1
},
{
"id": "177be4fc-97a4-429c-8f3d-27349aad9fdd",
"name": "Convert table names and columns into single text for agent",
"type": "n8n-nodes-base.code",
"position": [
140,
-300
],
"parameters": {
"jsCode": "return [\n {\n json: {\n text: items.map(item => JSON.stringify(item.json)).join('\\n'),\n },\n },\n];\n"
},
"typeVersion": 2
},
{
"id": "650de27a-f596-4b13-9ef7-8c86494fd9ce",
"name": "combine the table names with user question",
"type": "n8n-nodes-base.merge",
"position": [
280,
-440
],
"parameters": {
"mode": "combine",
"options": {},
"combineBy": "combineAll"
},
"typeVersion": 3.2
},
{
"id": "82289b5e-96bf-41c9-91d0-ee70238c57da",
"name": "AI Agent - Write SQL Query",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
460,
-160
],
"parameters": {
"text": "=user question: {{ $('Embedable chat for users to ask questions of bigquery').item.json.chatInput }}\nTable and column names: {{ $json.text }}",
"options": {
"systemMessage": "=You are a helpful AI assistant that writes valid SQL queries for Google BigQuery.\n\nYou will be given:\n- A user\u2019s question,\n- A list of available table names and column names. {{ $json.text }}\n\nYour task is to:\n1. Write a syntactically correct BigQuery SQL query that best answers the user's question,\n2. Only use table and column names that appear in the provided schema \u2014 do not guess or invent names,\n3. Make the best possible guess about which table and columns to use *from the given list only*,\n4. Return your output in a strict JSON format with one key: \"query\".\n\n\u26a0\ufe0f Do NOT invent table or column names.\n\u26a0\ufe0f If a relevant field does not exist, make the best effort to answer with what's available, or omit that part.\n\u26a0\ufe0f Do NOT include any explanation, notes, or comments \u2014 only the final JSON.\n\n---\n\n\n**this schema must be written before the table name Schema:**\n\n`n8nautomation-453001.email_leads_schema.\n\noutput data in json like this. \n{\n\t\"query\": \"sql query and no other text\"\n} "
},
"promptType": "define",
"hasOutputParser": true
},
"typeVersion": 2
},
{
"id": "adc7ad4c-4a17-4be7-975b-cdc2be4c116e",
"name": "Run query against schema",
"type": "n8n-nodes-base.googleBigQuery",
"onError": "continueErrorOutput",
"position": [
820,
-280
],
"parameters": {
"options": {},
"sqlQuery": "{{ $json.output.query }}",
"projectId": {
"__rl": true,
"mode": "list",
"value": "n8nautomation-453001",
"cachedResultUrl": "https://console.cloud.google.com/bigquery?project=n8nautomation-453001",
"cachedResultName": "n8nAutomation"
}
},
"credentials": {
"googleBigQueryOAuth2Api": {
"name": "<your credential>"
}
},
"typeVersion": 2.1
},
{
"id": "ab6bb5b4-8ea1-40ac-a293-213a8f03b114",
"name": "Ask User to try another question",
"type": "n8n-nodes-base.code",
"position": [
1080,
40
],
"parameters": {
"jsCode": "return [\n {\n json: {\n message: \"That query didn't work. Try another question.\"\n }\n }\n];\n"
},
"typeVersion": 2
}
],
"connections": {
"Simple Memory": {
"ai_memory": [
[
{
"node": "AI Agent - Write SQL Query",
"type": "ai_memory",
"index": 0
}
]
]
},
"OpenAI Chat Model": {
"ai_languageModel": [
[
{
"node": "AI Agent - Write SQL Query",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Combine into one field": {
"main": [
[
{
"node": "Convert table names and columns into single text for agent",
"type": "main",
"index": 0
}
]
]
},
"Run query against schema": {
"main": [
[],
[
{
"node": "Ask User to try another question",
"type": "main",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent - Write SQL Query",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"AI Agent - Write SQL Query": {
"main": [
[
{
"node": "Run query against schema",
"type": "main",
"index": 0
}
]
]
},
"combine the table names with user question": {
"main": [
[
{
"node": "AI Agent - Write SQL Query",
"type": "main",
"index": 0
}
]
]
},
"Output all table, and column names in your schema": {
"main": [
[
{
"node": "Combine into one field",
"type": "main",
"index": 0
}
]
]
},
"Embedable chat for users to ask questions of bigquery": {
"main": [
[
{
"node": "Output all table, and column names in your schema",
"type": "main",
"index": 0
},
{
"node": "combine the table names with user question",
"type": "main",
"index": 0
}
]
]
},
"Convert table names and columns into single text for agent": {
"main": [
[
{
"node": "combine the table names with user question",
"type": "main",
"index": 1
}
]
]
}
}
}
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.
googleBigQueryOAuth2ApiopenAiApi
Pro
For the full experience including quality scoring and batch install features for each workflow upgrade to Pro
About this workflow
Give business users a chat box; get back valid BigQuery SQL and live query results. The workflow: Captures a plain-language question from a chat widget or internal portal. Fetches the current table + column schema from your BigQuery dataset (via ). Feeds both the schema and the…
Source: https://n8n.io/workflows/6745/ — original creator credit. Request a take-down →
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