refactor: Concurrent batch processing

app/worker.py

@@ -362,14 +362,34 @@ async def _process_job_async(job_id: str, transactions: List[Dict[str, Any]]):
                     )
                     try:
                         client = genai.Client(api_key=api_key)
-                        # Batch transactions in chunks of 50
+                        # Split into batches of 50 and fire all concurrently
                         batch_size = 50
-                        for i in range(0, len(uncategorized_txns), batch_size):
-                            batch = uncategorized_txns[i : i + batch_size]
-                            batch_results = await classify_transactions_batch(
-                                client, batch, allowed_categories
+                        batches = [
+                            uncategorized_txns[i : i + batch_size]
+                            for i in range(0, len(uncategorized_txns), batch_size)
+                        ]
+                        logger.info(
+                            f"Dispatching {len(batches)} classification batch(es) concurrently..."
+                        )
+                        with benchmark(
+                            "LLM Concurrent Batch Classification (all batches)"
+                        ):
+                            batch_results_list = await asyncio.gather(
+                                *[
+                                    classify_transactions_batch(
+                                        client, batch, allowed_categories
+                                    )
+                                    for batch in batches
+                                ],
+                                return_exceptions=True,
                             )
-                            llm_results.update(batch_results)
+                        for batch_result in batch_results_list:
+                            if isinstance(batch_result, Exception):
+                                logger.error(
+                                    f"A classification batch failed: {batch_result}"
+                                )
+                            else:
+                                llm_results.update(batch_result)
                     except Exception as e:
                         logger.error(
                             f"Failed to initialize GenAI Client or classify: {e}"