diff --git a/haiku_rag_slim/haiku/rag/client/processing.py b/haiku_rag_slim/haiku/rag/client/processing.py
index df42befd..1ab9895f 100644
--- a/haiku_rag_slim/haiku/rag/client/processing.py
+++ b/haiku_rag_slim/haiku/rag/client/processing.py
@@ -182,11 +182,9 @@ def build_picture_chunks(
         text = extract_item_text(picture, docling_document) or ""
 
         page_numbers: list[int] = []
-        if prov := getattr(picture, "prov", None):
-            for p in prov:
-                page_no = getattr(p, "page_no", None)
-                if page_no is not None and page_no not in page_numbers:
-                    page_numbers.append(page_no)
+        for p in picture.prov:
+            if p.page_no not in page_numbers:
+                page_numbers.append(p.page_no)
 
         metadata = {
             "doc_item_refs": [picture.self_ref],
diff --git a/haiku_rag_slim/haiku/rag/store/models/document_item.py b/haiku_rag_slim/haiku/rag/store/models/document_item.py
index 744b64d8..d39d8b7b 100644
--- a/haiku_rag_slim/haiku/rag/store/models/document_item.py
+++ b/haiku_rag_slim/haiku/rag/store/models/document_item.py
@@ -1,5 +1,4 @@
 import base64
-import binascii
 from typing import TYPE_CHECKING
 
 from pydantic import BaseModel
@@ -44,27 +43,16 @@ def _decode_picture_bytes(item: "PictureItem") -> bytes | None:
     """Decode a PictureItem's embedded image into raw bytes.
 
     Reads ``item.image.uri`` and base64-decodes it when it is a ``data:`` URI.
-    Returns None for items whose image is absent, stripped, or not a data URI
-    (e.g. file references). Tolerant of malformed data — returns None on any
-    decode failure rather than raising.
+    Returns None for items whose image is absent or stripped, or whose URI is
+    a file reference rather than inline data.
     """
-    image = getattr(item, "image", None)
-    if image is None:
+    if item.image is None:
         return None
-    uri = getattr(image, "uri", None)
-    if uri is None:
-        return None
-    uri_str = str(uri)
-    if not uri_str.startswith("data:"):
-        return None
-    try:
-        _, encoded = uri_str.split(",", 1)
-    except ValueError:
-        return None
-    try:
-        return base64.b64decode(encoded, validate=False)
-    except (ValueError, binascii.Error):
+    uri = str(item.image.uri)
+    if not uri.startswith("data:"):
         return None
+    _, encoded = uri.split(",", 1)
+    return base64.b64decode(encoded, validate=False)
 
 
 def extract_item_text(item: "NodeItem", docling_doc: "DoclingDocument") -> str | None:
diff --git a/haiku_rag_slim/haiku/rag/tools/search.py b/haiku_rag_slim/haiku/rag/tools/search.py
index 4b4e756c..237ef327 100644
--- a/haiku_rag_slim/haiku/rag/tools/search.py
+++ b/haiku_rag_slim/haiku/rag/tools/search.py
@@ -101,13 +101,9 @@ def create_search_toolset(
             for self_ref, b64 in result.image_data.items():
                 if self_ref in seen:
                     continue
-                try:
-                    raw = base64.b64decode(b64)
-                except (ValueError, TypeError):
-                    continue
                 binary_parts.append(
                     BinaryContent(
-                        data=raw,
+                        data=base64.b64decode(b64),
                         media_type="image/png",
                         identifier=self_ref,
                     )
diff --git a/tests/test_embedder.py b/tests/test_embedder.py
index b5d62312..a583deb8 100644
--- a/tests/test_embedder.py
+++ b/tests/test_embedder.py
@@ -160,6 +160,25 @@ async def test_embed_chunks_empty_list():
     assert result == []
 
 
+async def test_embed_chunks_picture_with_text_only_embedder_raises():
+    """A picture chunk fed through a text-only embedder must surface a
+    clear error, not silently drop the chunk or call ``embed_image_query``
+    on something that doesn't support it."""
+    chunk = Chunk(id="pic", content="x")
+    chunk._picture_data = b"\x89PNG\r\n\x1a\nfake"
+
+    config = AppConfig(
+        embeddings=EmbeddingsConfig(
+            model=EmbeddingModelConfig(
+                provider="ollama", name="qwen3-embedding:4b", vector_dim=2560
+            )
+        )
+    )
+
+    with pytest.raises(ValueError, match="multimodal embedder"):
+        await embed_chunks([chunk], config)
+
+
 async def test_embed_chunks_batches_large_inputs(monkeypatch):
     """Test that embed_chunks batches calls when chunk count exceeds batch size."""
     from haiku.rag.embeddings import EMBEDDING_BATCH_SIZE, EmbedderWrapper