reproduce docvqa results and add debug file

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-similarity-map.py

@@ -83,7 +83,7 @@ INDEX_PATH: str = "./indexes/colvision_large.leann"
 # These are now command-line arguments (see CLI overrides section)
 TOPK: int = 3
 FIRST_STAGE_K: int = 500
-REBUILD_INDEX: bool = False
+REBUILD_INDEX: bool = True
 
 # Artifacts
 SAVE_TOP_IMAGE: Optional[str] = "./figures/retrieved_page.png"
@@ -122,11 +122,18 @@ parser.add_argument(
     default="./indexes/colvision_fastplaid",
     help="Path to the Fast-Plaid index. Default: './indexes/colvision_fastplaid'",
 )
+parser.add_argument(
+    "--topk",
+    type=int,
+    default=TOPK,
+    help=f"Number of top results to retrieve. Default: {TOPK}",
+)
 cli_args, _unknown = parser.parse_known_args()
 SEARCH_METHOD: str = cli_args.search_method
 QUERY = cli_args.query  # Override QUERY with CLI argument if provided
 USE_FAST_PLAID: bool = cli_args.use_fast_plaid
 FAST_PLAID_INDEX_PATH: str = cli_args.fast_plaid_index_path
+TOPK: int = cli_args.topk  # Override TOPK with CLI argument if provided
 
 # %%
 
@@ -399,7 +406,7 @@ if need_to_build_index:
                 f"No images found in {PAGES_DIR}. Provide PDF path in PDF variable or ensure images exist."
             )
     print(f"Loaded {len(images)} images")
-    
+
     # Memory check before loading model
     try:
         import psutil
@@ -426,10 +433,10 @@ try:
     import sys
     print(f"  Python version: {sys.version}")
     print(f"  Python executable: {sys.executable}")
-    
+
     model_name, model, processor, device_str, device, dtype = _load_colvision(MODEL)
     print(f"✓ Using model={model_name}, device={device_str}, dtype={dtype}")
-    
+
     # Memory check after loading model
     try:
         import psutil
@@ -457,7 +464,7 @@ if need_to_build_index:
     print("Step 4: Building index...")
     print(f"  Number of images: {len(images)}")
     print(f"  Number of filepaths: {len(filepaths)}")
-    
+
     try:
         print("  Embedding images...")
         doc_vecs = _embed_images(model, processor, images)
@@ -468,7 +475,7 @@ if need_to_build_index:
         import traceback
         traceback.print_exc()
         raise
-    
+
     if USE_FAST_PLAID:
         # Build Fast-Plaid index
         print("  Building Fast-Plaid index...")
@@ -523,13 +530,13 @@ if USE_FAST_PLAID:
         fast_plaid_index = _load_fast_plaid_index_if_exists(FAST_PLAID_INDEX_PATH)
         if fast_plaid_index is None:
             raise RuntimeError(f"Fast-Plaid index not found at {FAST_PLAID_INDEX_PATH}")
-    
+
     results, search_secs = _search_fast_plaid(fast_plaid_index, q_vec, TOPK)
     print(f"[Timing] Fast-Plaid Search: {search_secs:.3f}s")
 else:
     # Original LEANN search
     query_np = q_vec.float().numpy()
-    
+
     if SEARCH_METHOD == "ann":
         results = retriever.search(query_np, topk=TOPK, first_stage_k=FIRST_STAGE_K)
         search_secs = time.perf_counter() - _t0
@@ -548,7 +555,10 @@ if not results:
     print("No results found.")
 else:
     print(f'Top {len(results)} results for query: "{QUERY}"')
+    print("\n[DEBUG] Retrieval details:")
     top_images: list[Image.Image] = []
+    image_hashes = {}  # Track image hashes to detect duplicates
+
     for rank, (score, doc_id) in enumerate(results, start=1):
         # Retrieve image and metadata based on index type
         if USE_FAST_PLAID:
@@ -557,7 +567,7 @@ else:
             if image is None:
                 print(f"Warning: Could not find image for doc_id {doc_id}")
                 continue
-            
+
             metadata = _get_fast_plaid_metadata(FAST_PLAID_INDEX_PATH, doc_id)
             path = metadata.get("filepath", f"doc_{doc_id}") if metadata else f"doc_{doc_id}"
             top_images.append(image)
@@ -571,9 +581,27 @@ else:
             metadata = retriever.get_metadata(doc_id)
             path = metadata.get("filepath", "unknown") if metadata else "unknown"
             top_images.append(image)
-        
-        # For HF dataset, path is a descriptive identifier, not a real file path
-        print(f"{rank}) MaxSim: {score:.4f}, Page: {path}")
+
+        # Calculate image hash to detect duplicates
+        import hashlib
+        import io
+        # Convert image to bytes for hashing
+        img_bytes = io.BytesIO()
+        image.save(img_bytes, format='PNG')
+        image_bytes = img_bytes.getvalue()
+        image_hash = hashlib.md5(image_bytes).hexdigest()[:8]
+
+        # Check if this image was already seen
+        duplicate_info = ""
+        if image_hash in image_hashes:
+            duplicate_info = f" [DUPLICATE of rank {image_hashes[image_hash]}]"
+        else:
+            image_hashes[image_hash] = rank
+
+        # Print detailed information
+        print(f"{rank}) doc_id={doc_id}, MaxSim={score:.4f}, Page={path}, ImageHash={image_hash}{duplicate_info}")
+        if metadata:
+            print(f"   Metadata: {metadata}")
 
     if SAVE_TOP_IMAGE:
         from pathlib import Path as _Path