diff --git a/db.py b/db.py
index 99bf2be..b0e8ecc 100644
--- a/db.py
+++ b/db.py
@@ -81,8 +81,8 @@ def _embed(text: str) -> Vector:
     return np.array(ollama.embeddings(model=MODEL, prompt=text)["embedding"])
 
 
-def _vectorize_record(record: Record) -> Vector:
-    return _embed(record.text)
+def _vectorize_record(record: Record) -> tuple[Record, Vector]:
+    return record, _embed(record.text)
 
 #
 # High-level (exported) functions
@@ -260,40 +260,16 @@ def add_document(db: Database | Path, file: Path, max_workers: int = 4) -> None:
     print(f"Processing {len(records)} chunks with {max_workers} workers...")
 
     db.documents.append(file)
-    for record in records:
-        vector = _vectorize_record(record)
-        db.records[vector.tobytes()] = record
-        db.vectors.append(vector)
 
     # TODO measure with GIL disabled to check if multithreading actually helps
-    # with ThreadPoolExecutor(max_workers=max_workers) as executor:
-    #     # Submit all chunk processing tasks
-    #     vector_futures = {
-    #         executor.submit(_vectorize_record, r): r for r in records
-    #     }
-        
-    #     # Collect results as they complete
-    #     completed_chunks = 0
-    #     for future in as_completed(vector_futures):
-    #         try:
-    #             vector = future.result()
-    #             # Convert vector to bytes for use as dictionary key
-    #             vector_bytes = vector.tobytes()
-    #             # Add to database
-    #             db.vectors.append(vector)
-    #             db.records[vector_bytes] = record
-                
-    #             completed_chunks += 1
-    #             # if completed_chunks % 10 == 0 or completed_chunks == len(chunk_tasks):
-    #             #     print(f"    Completed {completed_chunks}/{len(chunk_tasks)} chunks")
-                    
-    #         except Exception as e:
-    #             chunk_data = future_to_chunk[future]
-    #             print(f"    Error processing chunk {chunk_data}: {e}")
-    
-    print(f"Successfully processed {file}: {len(records)} chunks")
-    
+    with ThreadPoolExecutor(max_workers=max_workers) as pool:
+        futures = [ pool.submit(_vectorize_record, r) for r in records ]
+        for f in as_completed(futures):
+            record, vector = f.result()
+            db.records[vector.tobytes()] = record
+            db.vectors.append(vector)
 
+    print(f"Successfully processed {file}: {len(records)} chunks")
     
     # Save database if we loaded it from file
     if save_to_file and database_file_path: