{ "workflow": { "id": 6745, "name": "Generate BigQuery SQL from natural language queries using GPT-4o chat", "views": 1924, "recentViews": 1, "totalViews": 1924, "createdAt": "2025-07-31T13:13:34.069Z", "description": "\nGive business users a chat box; get back **valid BigQuery SQL** and live query results. \nThe workflow:\n\n1. **Captures** a plain-language question from a chat widget or internal portal. \n2. **Fetches** the current **table + column schema** from your BigQuery dataset (via `INFORMATION_SCHEMA`). \n3. **Feeds** both the schema and the question to **GPT-4o** so it can craft a syntactically correct SQL query *using only fields that truly exist*. \n4. **Executes** the AI-generated SQL in BigQuery and returns the results. \n5. **Stores** a short-term memory by session, enabling natural follow-up questions.\n\nPerfect for analysts, customer-success teams, or any stakeholder who needs data without writing SQL.\n\n---\n\n#### ⚙️ Setup Instructions\n\n1. **Import the workflow** \n - n8n → **Workflows → Import from File** (or **Paste JSON**) → **Save**\n\n2. **Add credentials** \n | Service | Where to create credentials | Node(s) to update |\n |---------|----------------------------|-------------------|\n | **OpenAI** | <https://platform.openai.com> → Create API key | **OpenAI Chat Model** |\n | **Google BigQuery** | Google Cloud Console → IAM & Admin → Service Account JSON key | **Google BigQuery** (schema + query) |\n\n3. **Point the schema fetcher to your dataset** \n - In **Google BigQuery1** you’ll 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—BigQuery’s `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: *“How many distinct email leads were created last week?”* \n - After a few seconds the workflow will return a table of results—or 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\n\n#### 🧩 Customization Ideas\n- **Row-limit safeguard**: automatically append `LIMIT 1000` to every query. \n- **Chart rendering**: send query results to Google Sheets + Looker Studio for instant visuals. \n- **Slack bot**: forward both the question and the SQL result to a Slack channel for team visibility. \n- **Schema caching**: store the INFORMATION_SCHEMA result for 24 hours to cut BigQuery costs.\n\n---\n\n### Contact\n- **Email:** rbreen@ynteractive.com \n- **Website:** https://ynteractive.com \n- **YouTube:** https://www.youtube.com/@ynteractivetraining \n- **LinkedIn:** https://www.linkedin.com/in/robertbreen\n\n\n", "workflow": { "meta": { "instanceId": "efb474b59b0341d7791932605bd9ff04a6c7ed9941fdd53dc4a2e4b99a6f9439" }, "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": { "id": "credential-id", "name": "openAiApi 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#### ⚙️ Setup Instructions\n\n1. **Import the workflow** \n - n8n → **Workflows → Import from File** (or **Paste JSON**) → **Save**\n\n2. **Add credentials** \n | Service | Where to create credentials | Node(s) to update |\n |---------|----------------------------|-------------------|\n | **OpenAI** | → Create API key | **OpenAI Chat Model** |\n | **Google BigQuery** | Google Cloud Console → IAM & Admin → Service Account JSON key | **Google BigQuery** (schema + query) |\n\n3. **Point the schema fetcher to your dataset** \n - In **Google BigQuery1** you’ll 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—BigQuery’s `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: *“How many distinct email leads were created last week?”* \n - After a few seconds the workflow will return a table of results—or 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 ], "webhookId": "20173599-7d16-408b-aab0-6252b05a516b", "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": { "id": "credential-id", "name": "googleBigQueryOAuth2Api 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’s 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 — 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⚠️ Do NOT invent table or column names.\n⚠️ If a relevant field does not exist, make the best effort to answer with what's available, or omit that part.\n⚠️ Do NOT include any explanation, notes, or comments — 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": { "id": "credential-id", "name": "googleBigQueryOAuth2Api 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 } ], "pinData": {}, "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 } ] ] } } }, "lastUpdatedBy": 29, "workflowInfo": { "nodeCount": 13, "nodeTypes": { "n8n-nodes-base.code": { "count": 2 }, "n8n-nodes-base.merge": { "count": 1 }, "n8n-nodes-base.aggregate": { "count": 1 }, "n8n-nodes-base.stickyNote": { "count": 2 }, "n8n-nodes-base.googleBigQuery": { "count": 2 }, "@n8n/n8n-nodes-langchain.agent": { "count": 1 }, "@n8n/n8n-nodes-langchain.chatTrigger": { "count": 1 }, "@n8n/n8n-nodes-langchain.lmChatOpenAi": { "count": 1 }, "@n8n/n8n-nodes-langchain.memoryBufferWindow": { "count": 1 }, "@n8n/n8n-nodes-langchain.outputParserStructured": { "count": 1 } } }, "status": "published", "user": { "name": "Robert Breen", "username": "rbreen", "bio": "Professional services consultant with over 10 years of experience solving complex business problems across industries. I specialize in n8n and process automation—designing custom workflows that integrate tools like Google Calendar, Airtable, GPT, and internal systems. Whether you need to automate scheduling, sync data, or streamline operations, I build solutions that save time and drive results.", "verified": true, "links": [ "https://ynteractive.com/" ], "avatar": "https://gravatar.com/avatar/15bb5ad97bad47ca2079e1fa123a8287000c72c86498c90043f70ec2adab05f3?r=pg&d=retro&size=200" }, "nodes": [ { "id": 24, "icon": "file:merge.svg", "name": "n8n-nodes-base.merge", "codex": { "data": { "alias": [ "Join", "Concatenate", "Wait" ], "resources": { "generic": [ { "url": "https://n8n.io/blog/how-to-sync-data-between-two-systems/", "icon": "🏬", "label": "How to synchronize data between two systems (one-way vs. two-way sync" }, { "url": "https://n8n.io/blog/supercharging-your-conference-registration-process-with-n8n/", "icon": "🎫", "label": "Supercharging your conference registration process with n8n" }, { "url": "https://n8n.io/blog/migrating-community-metrics-to-orbit-using-n8n/", "icon": "📈", "label": "Migrating Community Metrics to Orbit using n8n" }, { 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