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Your sole function is to act as a transfer function, converting raw, unstructured text input (D_Raw) into structured, BI-ready JSON output (D_BI).\n\nYou must recognize and process only the following event types with their corresponding schemas:\n\n1. **USER_SIGNUP**:\n * Triggered by phrases indicating a new user registration.\n * Required fields: \\`email\\`, \\`timestamp\\`.\n * Optional fields: \\`referredBy\\` (another email), \\`marketingChannel\\`.\n * Schema: \\`{ \"eventType\": \"USER_SIGNUP\", \"user\": { \"email\": \"...\" }, \"timestamp\": \"...\", \"details\": { \"referredBy\": \"...\", \"marketingChannel\": \"...\" } }\\`\n\n2. **PLAN_UPGRADE**:\n * Triggered by phrases indicating a user changed their subscription plan.\n * Required fields: \\`email\\` or \\`userId\\`, \\`timestamp\\`, \\`newPlan\\`.\n * Optional fields: \\`previousPlan\\`.\n * Schema: \\`{ \"eventType\": \"PLAN_UPGRADE\", \"user\": { \"email\": \"...\", \"userId\": \"...\" }, \"timestamp\": \"...\", \"details\": { \"newPlan\": \"...\", \"previousPlan\": \"...\" } }\\`\n\n3. **PAYMENT_FAILED**:\n * Triggered by phrases indicating a failed payment or transaction.\n * Required fields: \\`email\\` or \\`userId\\`, \\`timestamp\\`, \\`amount\\`, \\`currency\\`.\n * Optional fields: \\`reasonCode\\`.\n * Schema: \\`{ \"eventType\": \"PAYMENT_FAILED\", \"user\": { \"email\": \"...\", \"userId\": \"...\" }, \"timestamp\": \"...\", \"details\": { \"amount\": 0.0, \"currency\": \"...\", \"reasonCode\": \"...\" } }\\`\n\n**Execution Rules:**\n\n* **Strict Adherence**: You MUST respond ONLY with a single, minified JSON object representing the AIResponse interface.\n* **Timestamp Normalization**: All dates and times must be converted to a full ISO 8601 timestamp (YYYY-MM-DDTHH:mm:ss.sssZ). Assume the current year is 2025 if not specified.\n* **Input Rejection**: If the raw input text is ambiguous, lacks required information for any known event type, or does not correspond to a defined event, you MUST NOT attempt to guess. Instead, you must respond with a 'REJECTED' status.\n* **Justification Requirement**: For any 'REJECTED' status, you MUST provide a clear, concise reason in the \\`rejectionReason\\` field, explaining what information was missing or why the input was ambiguous. This is critical for system observability.\n\nExample rejection:\nInput: \"User 123 had an issue.\"\nOutput: \\`{\"status\":\"REJECTED\",\"rejectionReason\":\"The input is ambiguous. It does not specify a recognized event type (USER_SIGNUP, PLAN_UPGRADE, PAYMENT_FAILED) or provide necessary details.\"}\\`\n`;\n\n// --- DATABASE INITIALIZATION (The \"Data Lake/Warehouse\") ---\n// This function ensures the target table exists before processing requests.\nasync function initializeDatabase() {\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS transformed_events (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n event_type TEXT NOT NULL,\n user_identifier TEXT NOT NULL,\n event_timestamp TEXT NOT NULL,\n raw_data_input TEXT NOT NULL,\n structured_data TEXT NOT NULL, -- Stored as a JSON string\n processed_at TEXT NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%f', 'now'))\n )\n `);\n}\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string) {\n return `\n\n\n \n \n G_ETL(s): AI Data Transfer Function\n \n \n \n\n\n\n

G_ETL(s): AI Data Transfer Function

Using OpenAI as a Serverless Transformation Engine.

\n \n

1. Input Signal: Raw Data (D_Raw)

\n \n

\n\n

2. Output Response: BI-Ready Data (D_BI)

Awaiting transformation...

\n \n \n

\n\n\n\n`;\n}\n\n// --- API ROUTING & LOGIC ---\nconst app = new Hono();\nconst openai = new OpenAI();\n\n// Middleware to ensure database is ready\napp.use(\"*\", async (c, next) => {\n await initializeDatabase();\n await next();\n});\n\n// Endpoint to receive raw data, transform it, and load it into the database\napp.post(\"/transform\", async (c) => {\n const { rawData } = await c.req.json<{ rawData: string }>();\n if (!rawData) {\n return c.json({ error: \"rawData field is required\" }, 400);\n }\n\n // This is the core of the \"Transfer Function\" G_ETL(s)\n const completion = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: DATA_TRANSFORM_PROMPT },\n { role: \"user\", content: rawData },\n ],\n temperature: 0.1, // Low temperature for deterministic, precise output\n response_format: { type: \"json_object\" },\n });\n\n const aiResult: AIResponse = JSON.parse(completion.choices[0].message.content);\n\n if (aiResult.status === \"REJECTED\" || !aiResult.event) {\n return c.json({\n rejectionReason: aiResult.rejectionReason || \"AI rejected input without a reason.\",\n }, 422); // Unprocessable Entity\n }\n\n // If transformation is successful, load the data into SQLite\n const { event } = aiResult;\n try {\n await sqlite.execute({\n sql: `\n INSERT INTO transformed_events (event_type, user_identifier, event_timestamp, raw_data_input, structured_data)\n VALUES (:eventType, :userIdentifier, :timestamp, :rawData, :structuredData)\n `,\n args: {\n eventType: event.eventType,\n userIdentifier: event.user.email || event.user.userId,\n timestamp: event.timestamp,\n rawData: rawData,\n structuredData: JSON.stringify(event),\n },\n });\n return c.json(event);\n } catch (dbError) {\n console.error(\"Database Load Error:\", dbError);\n return c.json({ error: \"Failed to load transformed data into the database.\", details: dbError.message }, 500);\n }\n});\n\n// Endpoint to query the transformed data (\"Data Warehouse\")\napp.get(\"/events\", async (c) => {\n const { results } = await sqlite.execute(\n \"SELECT event_type, user_identifier, event_timestamp FROM transformed_events ORDER BY id DESC LIMIT 50\",\n );\n return c.json(results);\n});\n\n// Serve the main HTML page\napp.get(\"/\", (c) => {\n const sourceUrl = new URL(c.req.url).origin + new URL(c.req.url).pathname;\n return c.html(generateHtml(sourceUrl));\n});\n\nexport default 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GETL(s): AI Data Transfer Function

1. Input Signal: Raw Data (DRaw)

2. Output Response: BI-Ready Data (DBI)

G_ETL(s): AI Data Transfer Function

1. Input Signal: Raw Data (D_Raw)

2. Output Response: BI-Ready Data (D_BI)