feat: Implement batch processing for document ingestion in MediawikiETL

hivemind_etl/mediawiki/etl.py

@@ -1,6 +1,7 @@
 import logging
 import os
 import shutil
+from concurrent.futures import ThreadPoolExecutor, as_completed
 
 from llama_index.core import Document
 from tc_hivemind_backend.ingest_qdrant import CustomIngestionPipeline
@@ -100,7 +101,28 @@ def load(self, documents: list[Document]) -> None:
         ingestion_pipeline = CustomIngestionPipeline(
             self.community_id, collection_name=self.platform_id
         )
-        ingestion_pipeline.run_pipeline(documents)
+
+        # Process batches in parallel using ThreadPoolExecutor
+        batch_size = 1000
+        batches = [documents[i:i + batch_size] for i in range(0, len(documents), batch_size)]
+
+        with ThreadPoolExecutor() as executor:
+            # Submit all batch processing tasks
+            future_to_batch = {
+                executor.submit(ingestion_pipeline.run_pipeline, batch): i 
+                for i, batch in enumerate(batches)
+            }
+
+            # Process completed batches and handle any errors
+            for future in as_completed(future_to_batch):
+                batch_idx = future_to_batch[future]
+                try:
+                    future.result()  # This will raise any exceptions that occurred
+                    logging.info(f"Successfully loaded batch {batch_idx} of {len(batches)} documents into Qdrant!")
+                except Exception as e:
+                    logging.error(f"Error processing batch {batch_idx}: {e}")
+                    raise  # Re-raise the exception to stop the process
+
         logging.info(f"Loaded {len(documents)} documents into Qdrant!")
 
         if self.delete_dump_after_load: