{
"id": "7gRbzEzCuOzQKn4M",
"meta": {
"instanceId": "edc0464b1050024ebda3e16fceea795e4fdf67b1f61187c4f2f3a72397278df0",
"templateCredsSetupCompleted": true
},
"name": "SHEETS RAG",
"tags": [],
"nodes": [
{
"id": "a073154f-53ad-45e2-9937-d0a4196c7838",
"name": "create table query",
"type": "n8n-nodes-base.code",
"position": [
1280,
2360
],
"parameters": {
"jsCode": "\/\/ Helper function to check if a string is in MM\/DD\/YYYY format\nfunction isDateString(value) {\n const dateRegex = \/^\\d{2}\\\/\\d{2}\\\/\\d{4}$\/;\n if (typeof value !== 'string') return false;\n if (!dateRegex.test(value)) return false;\n const [month, day, year] = value.split('\/').map(Number);\n const date = new Date(year, month - 1, day);\n return !isNaN(date.getTime());\n}\n\nconst tableName = `ai_table_${$('change_this').first().json.sheet_name}`;\nconst rows = $('fetch sheet data').all();\nconst allColumns = new Set();\n\n\/\/ Collect column names dynamically\nrows.forEach(row => {\n Object.keys(row.json).forEach(col => allColumns.add(col));\n});\n\n\/\/ Ensure \"ai_table_identifier\" is always the first column\nconst originalColumns = [\"ai_table_identifier\", ...Array.from(allColumns)];\n\n\/\/ Function to detect currency type (unchanged)\nfunction detectCurrency(values) {\n const currencySymbols = {\n '\u20b9': 'INR', '$': 'USD', '\u20ac': 'EUR', '\u00a3': 'GBP', '\u00a5': 'JPY',\n '\u20a9': 'KRW', '\u0e3f': 'THB', 'z\u0142': 'PLN', 'kr': 'SEK', 'R$': 'BRL',\n 'C$': 'CAD', 'A$': 'AUD'\n };\n\n let detectedCurrency = null;\n for (const value of values) {\n if (typeof value === 'string' && value.trim() !== '') {\n for (const [symbol, code] of Object.entries(currencySymbols)) {\n if (value.trim().startsWith(symbol)) {\n detectedCurrency = code;\n break;\n }\n }\n }\n }\n return detectedCurrency;\n}\n\n\/\/ Function to generate consistent column names\nfunction generateColumnName(originalName, typeInfo) {\n if (typeInfo.isCurrency) {\n return `${originalName}_${typeInfo.currencyCode.toLowerCase()}`;\n }\n return originalName;\n}\n\n\/\/ Infer column types and transform names\nconst columnMapping = {};\noriginalColumns.forEach(col => {\n let typeInfo = { type: \"TEXT\" };\n\n if (col !== \"ai_table_identifier\") {\n const sampleValues = rows\n .map(row => row.json[col])\n .filter(value => value !== undefined && value !== null);\n\n \/\/ Check for currency first\n const currencyCode = detectCurrency(sampleValues);\n if (currencyCode) {\n typeInfo = { type: \"DECIMAL(15,2)\", isCurrency: true, currencyCode };\n }\n \/\/ If all sample values match MM\/DD\/YYYY, treat the column as a date\n else if (sampleValues.length > 0 && sampleValues.every(val => isDateString(val))) {\n typeInfo = { type: \"TIMESTAMP\" };\n }\n }\n\n const newColumnName = generateColumnName(col, typeInfo);\n columnMapping[col] = { newName: newColumnName, typeInfo };\n});\n\n\/\/ Final column names\nconst mappedColumns = originalColumns.map(col => columnMapping[col]?.newName || col);\n\n\/\/ Define SQL columns \u2013 note that for simplicity, this example still uses TEXT for non-special types,\n\/\/ but you can adjust it so that TIMESTAMP columns are created with a TIMESTAMP type.\nconst columnDefinitions = [`\"ai_table_identifier\" UUID PRIMARY KEY DEFAULT gen_random_uuid()`]\n .concat(mappedColumns.slice(1).map(col => {\n \/\/ If the column was inferred as TIMESTAMP, use that type in the CREATE TABLE statement.\n const originalCol = Object.keys(columnMapping).find(key => columnMapping[key].newName === col);\n const inferredType = columnMapping[originalCol]?.typeInfo?.type;\n return `\"${col}\" ${inferredType === \"TIMESTAMP\" ? \"TIMESTAMP\" : \"TEXT\"}`;\n }))\n .join(\", \");\n\nconst createTableQuery = `CREATE TABLE IF NOT EXISTS ${tableName} (${columnDefinitions});`;\n\nreturn [{ \n query: createTableQuery,\n columnMapping: columnMapping \n}];\n"
},
"typeVersion": 2
},
{
"id": "2beb72c4-dab4-4058-b587-545a8ce8b86d",
"name": "create insertion query",
"type": "n8n-nodes-base.code",
"position": [
1660,
2360
],
"parameters": {
"jsCode": "const tableName = `ai_table_${$('change_this').first().json.sheet_name}`;\nconst rows = $('fetch sheet data').all();\nconst allColumns = new Set();\n\n\/\/ Get column mapping from previous node\nconst columnMapping = $('create table query').first().json.columnMapping || {};\n\n\/\/ Collect column names dynamically\nrows.forEach(row => {\n Object.keys(row.json).forEach(col => allColumns.add(col));\n});\n\nconst originalColumns = Array.from(allColumns);\nconst mappedColumns = originalColumns.map(col => \n columnMapping[col] ? columnMapping[col].newName : col\n);\n\n\/\/ Helper function to check if a string is a valid timestamp\nfunction isValidTimestamp(value) {\n const date = new Date(value);\n return !isNaN(date.getTime());\n}\n\n\/\/ Helper to detect currency symbol (unchanged)\nfunction getCurrencySymbol(value) {\n if (typeof value !== 'string') return null;\n \n const currencySymbols = ['\u20b9', '$', '\u20ac', '\u00a3', '\u00a5', '\u20a9', '\u0e3f', 'z\u0142', 'kr', 'R$', 'C$', 'A$'];\n for (const symbol of currencySymbols) {\n if (value.trim().startsWith(symbol)) {\n return symbol;\n }\n }\n return null;\n}\n\n\/\/ Helper to normalize currency values (unchanged)\nfunction normalizeCurrencyValue(value, currencySymbol) {\n if (typeof value !== 'string') return null;\n if (!currencySymbol) return value;\n \n const numericPart = value.replace(currencySymbol, '').replace(\/,\/g, '');\n return !isNaN(parseFloat(numericPart)) ? parseFloat(numericPart) : null;\n}\n\n\/\/ Helper to normalize percentage values (unchanged)\nfunction normalizePercentageValue(value) {\n if (typeof value !== 'string') return value;\n if (!value.trim().endsWith('%')) return value;\n \n const numericPart = value.replace('%', '');\n return !isNaN(parseFloat(numericPart)) ? parseFloat(numericPart) \/ 100 : null;\n}\n\n\/\/ Function to parse MM\/DD\/YYYY strings into ISO format\nfunction parseDateString(value) {\n const dateRegex = \/^\\d{2}\\\/\\d{2}\\\/\\d{4}$\/;\n if (typeof value === 'string' && dateRegex.test(value)) {\n const [month, day, year] = value.split('\/').map(Number);\n const date = new Date(year, month - 1, day);\n return !isNaN(date.getTime()) ? date.toISOString() : null;\n }\n return value;\n}\n\n\/\/ Format rows properly based on column mappings and types\nconst formattedRows = rows.map(row => {\n const formattedRow = {};\n\n originalColumns.forEach((col, index) => {\n const mappedCol = mappedColumns[index];\n let value = row.json[col];\n const typeInfo = columnMapping[col]?.typeInfo || { type: \"TEXT\" };\n\n if (value === \"\" || value === null || value === undefined) {\n value = null;\n } \n else if (typeInfo.isCurrency) {\n const symbol = getCurrencySymbol(value);\n if (symbol) {\n value = normalizeCurrencyValue(value, symbol);\n } else {\n value = null;\n }\n }\n else if (typeInfo.isPercentage) {\n if (typeof value === 'string' && value.trim().endsWith('%')) {\n value = normalizePercentageValue(value);\n } else {\n value = !isNaN(parseFloat(value)) ? parseFloat(value) \/ 100 : null;\n }\n }\n else if (typeInfo.type === \"DECIMAL(15,2)\" || typeInfo.type === \"INTEGER\") {\n if (typeof value === 'string') {\n const cleanedValue = value.replace(\/,\/g, '');\n value = !isNaN(parseFloat(cleanedValue)) ? parseFloat(cleanedValue) : null;\n } else if (typeof value === 'number') {\n value = parseFloat(value);\n } else {\n value = null;\n }\n } \n else if (typeInfo.type === \"BOOLEAN\") {\n if (typeof value === 'string') {\n const lowercased = value.toString().toLowerCase();\n value = lowercased === \"true\" ? true : \n lowercased === \"false\" ? false : null;\n } else {\n value = Boolean(value);\n }\n } \n else if (typeInfo.type === \"TIMESTAMP\") {\n \/\/ Check if the value is in MM\/DD\/YYYY format and parse it accordingly.\n if (\/^\\d{2}\\\/\\d{2}\\\/\\d{4}$\/.test(value)) {\n value = parseDateString(value);\n } else if (isValidTimestamp(value)) {\n value = new Date(value).toISOString();\n } else {\n value = null;\n }\n }\n else if (typeInfo.type === \"TEXT\") {\n value = value !== null && value !== undefined ? String(value) : null;\n }\n\n formattedRow[mappedCol] = value;\n });\n\n return formattedRow;\n});\n\n\/\/ Generate SQL placeholders dynamically\nconst valuePlaceholders = formattedRows.map((_, rowIndex) =>\n `(${mappedColumns.map((_, colIndex) => `$${rowIndex * mappedColumns.length + colIndex + 1}`).join(\", \")})`\n).join(\", \");\n\n\/\/ Build the insert query string\nconst insertQuery = `INSERT INTO ${tableName} (${mappedColumns.map(col => `\"${col}\"`).join(\", \")}) VALUES ${valuePlaceholders};`;\n\n\/\/ Flatten parameter values for PostgreSQL query\nconst parameters = formattedRows.flatMap(row => mappedColumns.map(col => row[col]));\n\nreturn [\n {\n query: insertQuery,\n parameters: parameters\n }\n];\n"
},
"typeVersion": 2
},
{
"id": "ba19c350-ffb7-4fe1-9568-2a619c914434",
"name": "Google Drive Trigger",
"type": "n8n-nodes-base.googleDriveTrigger",
"position": [
600,
2060
],
"parameters": {
"pollTimes": {
"item": [
[]
]
},
"triggerOn": "specificFile",
"fileToWatch": {
"__rl": true,
"mode": "list",
"value": "1yGx4ODHYYtPV1WZFAtPcyxGT2brcXM6pl0KJhIM1f_c",
"cachedResultUrl": "https:\/\/docs.google.com\/spreadsheets\/d\/1yGx4ODHYYtPV1WZFAtPcyxGT2brcXM6pl0KJhIM1f_c\/edit?usp=drivesdk",
"cachedResultName": "Spreadsheet"
}
},
"credentials": {
"googleDriveOAuth2Api": {
"id": "zOt0lyWOZz1UlS67",
"name": "Google Drive account"
}
},
"typeVersion": 1
},
{
"id": "dd2108fe-0cfe-453c-ac03-c0c5b10397e6",
"name": "execute_query_tool",
"type": "@n8n\/n8n-nodes-langchain.toolWorkflow",
"position": [
1340,
1720
],
"parameters": {
"name": "query_executer",
"schemaType": "manual",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "oPWJZynrMME45ks4",
"cachedResultName": "query_executer"
},
"description": "Call this tool to execute a query. Remember that it should be in a postgreSQL query structure.",
"inputSchema": "{\n\"type\": \"object\",\n\"properties\": {\n\t\"sql\": {\n\t\t\"type\": \"string\",\n\t\t\"description\": \"A SQL query based on the users question and database schema.\"\n\t\t}\n\t}\n}",
"specifyInputSchema": true
},
"typeVersion": 1.1999999999999999555910790149937383830547332763671875
},
{
"id": "f2c110db-1097-4b96-830d-f028e08b6713",
"name": "Google Gemini Chat Model",
"type": "@n8n\/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
880,
1680
],
"parameters": {
"options": [],
"modelName": "models\/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "Kr5lNqvdmtB0Ybyo",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "2460801c-5b64-41b3-93f7-4f2fbffabfd6",
"name": "get_postgres_schema",
"type": "@n8n\/n8n-nodes-langchain.toolWorkflow",
"position": [
1160,
1720
],
"parameters": {
"name": "get_postgres_schema",
"workflowId": {
"__rl": true,
"mode": "list",
"value": "iNLPk34SeRGHaeMD",
"cachedResultName": "get database schema"
},
"description": "Call this tool to retrieve the schema of all the tables inside of the database. A string will be retrieved with the name of the table and its columns, each table is separated by \\n\\n.",
"workflowInputs": {
"value": [],
"schema": [],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"typeVersion": 2
},
{
"id": "4b43ff94-df0d-40f1-9f51-cf488e33ff68",
"name": "change_this",
"type": "n8n-nodes-base.set",
"position": [
800,
2060
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "908ed843-f848-4290-9cdb-f195d2189d7c",
"name": "table_url",
"type": "string",
"value": "https:\/\/docs.google.com\/spreadsheets\/d\/1yGx4ODHYYtPV1WZFAtPcyxGT2brcXM6pl0KJhIM1f_c\/edit?gid=0#gid=0"
},
{
"id": "50f8afaf-0a6c-43ee-9157-79408fe3617a",
"name": "sheet_name",
"type": "string",
"value": "product_list"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
},
{
"id": "a27a47ff-9328-4eef-99e8-280452cff189",
"name": "is not in database",
"type": "n8n-nodes-base.if",
"position": [
1380,
2060
],
"parameters": {
"options": [],
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "619ce84c-0a50-4f88-8e55-0ce529aea1fc",
"operator": {
"type": "boolean",
"operation": "false",
"singleValue": true
},
"leftValue": "={{ $('table exists?').item.json.exists }}",
"rightValue": "true"
}
]
}
},
"typeVersion": 2.20000000000000017763568394002504646778106689453125
},
{
"id": "8ad9bc36-08b1-408e-ba20-5618a801b4ed",
"name": "table exists?",
"type": "n8n-nodes-base.postgres",
"position": [
1000,
2060
],
"parameters": {
"query": "SELECT EXISTS (\n SELECT 1 \n FROM information_schema.tables \n WHERE table_name = 'ai_table_{{ $json.sheet_name }}'\n);\n",
"options": [],
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "f66b7ca7-ecb7-47fc-9214-2d2b37b0fbe4",
"name": "fetch sheet data",
"type": "n8n-nodes-base.googleSheets",
"position": [
1180,
2060
],
"parameters": {
"options": [],
"sheetName": {
"__rl": true,
"mode": "name",
"value": "={{ $('change_this').item.json.sheet_name }}"
},
"documentId": {
"__rl": true,
"mode": "url",
"value": "={{ $('change_this').item.json.table_url }}"
}
},
"credentials": {
"googleSheetsOAuth2Api": {
"id": "3au0rUsZErkG0zc2",
"name": "Google Sheets account"
}
},
"typeVersion": 4.5
},
{
"id": "11ba5da0-e7c4-49ee-8d35-24c8d3b9fea9",
"name": "remove table",
"type": "n8n-nodes-base.postgres",
"position": [
980,
2360
],
"parameters": {
"query": "DROP TABLE IF EXISTS ai_table_{{ $('change_this').item.json.sheet_name }} CASCADE;",
"options": [],
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "3936ecb3-f084-4f86-bd5f-abab0957ebc0",
"name": "create table",
"type": "n8n-nodes-base.postgres",
"position": [
1460,
2360
],
"parameters": {
"query": "{{ $json.query }}",
"options": [],
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "8a3ea239-f3fa-4c72-af99-31f4bd992b58",
"name": "perform insertion",
"type": "n8n-nodes-base.postgres",
"position": [
1860,
2360
],
"parameters": {
"query": "{{$json.query}}",
"options": {
"queryReplacement": "={{$json.parameters}}"
},
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "21239928-b573-4753-a7ca-5a9c3aa8aa3e",
"name": "Execute Workflow Trigger",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
1720,
1720
],
"parameters": [],
"typeVersion": 1
},
{
"id": "c94256a9-e44e-4800-82f8-90f85ba90bde",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1920,
1460
],
"parameters": {
"color": 7,
"width": 500,
"height": 260,
"content": "Place this in a separate workflow named:\n### query_executer"
},
"typeVersion": 1
},
{
"id": "daec928e-58ee-43da-bd91-ba8bcd639a4a",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1920,
1840
],
"parameters": {
"color": 7,
"width": 500,
"height": 280,
"content": "place this in a separate workflow named: \n### get database schema"
},
"typeVersion": 1
},
{
"id": "8908e342-fcbe-4820-b623-cb95a55ea5db",
"name": "When chat message received",
"type": "@n8n\/n8n-nodes-langchain.manualChatTrigger",
"position": [
640,
1540
],
"parameters": [],
"typeVersion": 1.100000000000000088817841970012523233890533447265625
},
{
"id": "d0ae90c2-169e-44d7-b3c2-4aff8e7d4be9",
"name": "response output",
"type": "n8n-nodes-base.set",
"position": [
2220,
1540
],
"parameters": {
"options": [],
"assignments": {
"assignments": [
{
"id": "e2f94fb1-3deb-466a-a36c-e3476511d5f2",
"name": "response",
"type": "string",
"value": "={{ $json }}"
}
]
}
},
"typeVersion": 3.399999999999999911182158029987476766109466552734375
},
{
"id": "81c58d9b-ded4-4b74-8227-849e665cbdff",
"name": "sql query executor",
"type": "n8n-nodes-base.postgres",
"position": [
2000,
1540
],
"parameters": {
"query": "{{ $json.query.sql }}",
"options": [],
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "377d1727-4577-41bb-8656-38273fc4412b",
"name": "schema finder",
"type": "n8n-nodes-base.postgres",
"position": [
2000,
1920
],
"parameters": {
"query": "SELECT \n t.schemaname,\n t.tablename,\n c.column_name,\n c.data_type\nFROM \n pg_catalog.pg_tables t\nJOIN \n information_schema.columns c\n ON t.schemaname = c.table_schema\n AND t.tablename = c.table_name\nWHERE \n t.schemaname = 'public'\nORDER BY \n t.tablename, c.ordinal_position;",
"options": [],
"operation": "executeQuery"
},
"credentials": {
"postgres": {
"id": "KQiQIZTArTBSNJH7",
"name": "Postgres account"
}
},
"typeVersion": 2.5
},
{
"id": "89d3c59c-2b67-454d-a8f3-e90e75a28a8c",
"name": "schema to string",
"type": "n8n-nodes-base.code",
"position": [
2220,
1920
],
"parameters": {
"jsCode": "function transformSchema(input) {\n const tables = {};\n \n input.forEach(({ json }) => {\n if (!json) return;\n \n const { tablename, schemaname, column_name, data_type } = json;\n \n if (!tables[tablename]) {\n tables[tablename] = { schema: schemaname, columns: [] };\n }\n tables[tablename].columns.push(`${column_name} (${data_type})`);\n });\n \n return Object.entries(tables)\n .map(([tablename, { schema, columns }]) => `Table ${tablename} (Schema: ${schema}) has columns: ${columns.join(\", \")}`)\n .join(\"\\n\\n\");\n}\n\n\/\/ Example usage\nconst input = $input.all();\n\nconst transformedSchema = transformSchema(input);\n\nreturn { data: transformedSchema };"
},
"typeVersion": 2
},
{
"id": "42d1b316-60ca-49db-959b-581b162ca1f9",
"name": "AI Agent With SQL Query Prompt",
"type": "@n8n\/n8n-nodes-langchain.agent",
"position": [
900,
1540
],
"parameters": {
"options": {
"maxIterations": 5,
"systemMessage": "=## Role\nYou are a **Database Query Assistant** specializing in generating PostgreSQL queries based on natural language questions. You analyze database schemas, construct appropriate SQL queries, and provide clear explanations of results.\n\n## Tools\n1. `get_postgres_schema`: Retrieves the complete database schema (tables and columns)\n2. `execute_query_tool`: Executes SQL queries with the following input format:\n ```json\n {\n \"sql\": \"Your SQL query here\"\n }\n ```\n\n## Process Flow\n\n### 1. Analyze the Question\n- Identify the **data entities** being requested (products, customers, orders, etc.)\n- Determine the **query type** (COUNT, AVG, SUM, SELECT, etc.)\n- Extract any **filters** or **conditions** mentioned\n\n### 2. Fetch and Analyze Schema\n- Call `get_postgres_schema` to retrieve database structure\n- Identify relevant tables and columns that match the entities in the question\n- Prioritize exact matches, then semantic matches\n\n### 3. Query Construction\n- Build case-insensitive queries using `LOWER(column) LIKE LOWER('%value%')`\n- Filter out NULL or empty values with appropriate WHERE clauses\n- Use joins when information spans multiple tables\n- Apply aggregations (COUNT, SUM, AVG) as needed\n\n### 4. Query Execution\n- Execute query using the `execute_query_tool` with proper formatting\n- If results require further processing, perform calculations as needed\n\n### 5. Result Presentation\n- Format results in a conversational, easy-to-understand manner\n- Explain how the data was retrieved and any calculations performed\n- When appropriate, suggest further questions the user might want to ask\n\n## Best Practices\n- Use parameterized queries to prevent SQL injection\n- Implement proper error handling\n- Respond with \"NOT_ENOUGH_INFO\" when the question lacks specificity\n- Always verify table\/column existence before attempting queries\n- Use explicit JOINs instead of implicit joins\n- Limit large result sets when appropriate\n\n## Numeric Validation (IMPORTANT)\nWhen validating or filtering numeric values in string columns:\n1. **AVOID** complex regular expressions with `~` operator as they cause syntax errors\n2. Use these safer alternatives instead:\n ```sql\n -- Simple numeric check without regex\n WHERE column_name IS NOT NULL AND trim(column_name) != '' AND column_name NOT LIKE '%[^0-9.]%'\n \n -- For type casting with validation\n WHERE column_name IS NOT NULL AND trim(column_name) != '' AND column_name ~ '[0-9]'\n \n -- Safe numeric conversion\n WHERE CASE WHEN column_name ~ '[0-9]' THEN TRUE ELSE FALSE END\n ```\n3. For simple pattern matching, use LIKE instead of regex when possible\n4. When CAST is needed, always guard against invalid values:\n ```sql\n SELECT SUM(CASE WHEN column_name ~ '[0-9]' THEN CAST(column_name AS NUMERIC) ELSE 0 END) AS total\n ```\n\n## Response Structure\n1. **Analysis**: Brief mention of how you understood the question\n2. **Query**: The SQL statement used (in code block format)\n3. **Results**: Clear presentation of the data found\n4. **Explanation**: Simple description of how the data was retrieved\n\n## Examples\n\n### Example 1: Basic Counting Query\n**Question**: \"How many products are in the inventory?\"\n\n**Process**:\n1. Analyze schema to find product\/inventory tables\n2. Construct a COUNT query on the relevant table\n3. Execute the query\n4. Present the count with context\n\n**SQL**:\n```sql\nSELECT COUNT(*) AS product_count \nFROM products \nWHERE quantity IS NOT NULL;\n```\n\n**Response**:\n\"There are 1,250 products currently in the inventory. This count includes all items with a non-null quantity value in the products table.\"\n\n### Example 2: Filtered Aggregation Query\n**Question**: \"What is the average order value for premium customers?\"\n\n**Process**:\n1. Identify relevant tables (orders, customers)\n2. Determine join conditions\n3. Apply filters for \"premium\" customers\n4. Calculate average\n\n**SQL**:\n```sql\nSELECT AVG(o.total_amount) AS avg_order_value\nFROM orders o\nJOIN customers c ON o.customer_id = c.id\nWHERE LOWER(c.customer_type) = LOWER('premium')\nAND o.total_amount IS NOT NULL;\n```\n\n**Response**:\n\"Premium customers spend an average of $85.42 per order. This was calculated by averaging the total_amount from all orders placed by customers with a 'premium' customer type.\"\n\n### Example 3: Numeric Calculation from String Column\n**Question**: \"What is the total of all ratings?\"\n\n**Process**:\n1. Find the ratings table and column\n2. Use safe numeric validation\n3. Sum the values\n\n**SQL**:\n```sql\nSELECT SUM(CASE WHEN rating ~ '[0-9]' THEN CAST(rating AS NUMERIC) ELSE 0 END) AS total_rating\nFROM ratings\nWHERE rating IS NOT NULL AND trim(rating) != '';\n```\n\n**Response**:\n\"The sum of all ratings is 4,285. This calculation includes all valid numeric ratings from the ratings table.\"\n\n### Example 4: Date Range Aggregation for Revenue \n**Question**: \"How much did I make last week?\" \n\n**Process**: \n1. Identify the sales table and relevant columns (e.g., `sale_date` for dates and `revenue_amount` for revenue). \n2. Use PostgreSQL date functions (`date_trunc` and interval arithmetic) to calculate the date range for the previous week. \n3. Sum the revenue within the computed date range. \n\n**SQL**: \n```sql\nSELECT SUM(revenue_amount) AS total_revenue\nFROM sales_data\nWHERE sale_date >= date_trunc('week', CURRENT_DATE) - INTERVAL '1 week'\n AND sale_date < date_trunc('week', CURRENT_DATE);\n``` \n\n**Response**: \n\"Last week's total revenue is calculated by summing the `revenue_amount` for records where the `sale_date` falls within the previous week. This query uses date functions to dynamically determine the correct date range.\"\n\nToday's date: {{ $now }}"
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