diff --git a/nextcloud_mcp_server/auth/static/vector-viz.css b/nextcloud_mcp_server/auth/static/vector-viz.css
index 3e9398b..9d94d8c 100644
--- a/nextcloud_mcp_server/auth/static/vector-viz.css
+++ b/nextcloud_mcp_server/auth/static/vector-viz.css
@@ -190,3 +190,30 @@
     color: var(--color-text-maxcontrast);
     font-style: italic;
 }
+
+/* PDF highlighted image styles */
+.chunk-image-container {
+    margin-bottom: 16px;
+    border: 1px solid var(--color-border);
+    border-radius: var(--border-radius);
+    overflow: hidden;
+    background: #fff;
+}
+.chunk-image-header {
+    background: var(--color-background-dark);
+    padding: 8px 12px;
+    font-size: 12px;
+    font-weight: 500;
+    color: var(--color-text-maxcontrast);
+    border-bottom: 1px solid var(--color-border);
+    font-family: var(--font-face);
+}
+.chunk-highlighted-image {
+    display: block;
+    max-width: 100%;
+    height: auto;
+    cursor: zoom-in;
+}
+.chunk-highlighted-image:hover {
+    opacity: 0.95;
+}
diff --git a/nextcloud_mcp_server/auth/templates/vector_viz.html b/nextcloud_mcp_server/auth/templates/vector_viz.html
index de91f2f..c36a0eb 100644
--- a/nextcloud_mcp_server/auth/templates/vector_viz.html
+++ b/nextcloud_mcp_server/auth/templates/vector_viz.html
@@ -147,6 +147,20 @@
                                 </template>
                                 <template x-if="!chunkLoading[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]">
                                     <div>
+                                        <!-- Highlighted page image for PDFs -->
+                                        <template x-if="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.highlighted_page_image">
+                                            <div class="chunk-image-container">
+                                                <div class="chunk-image-header">
+                                                    <span>Page <span x-text="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.page_number"></span></span>
+                                                </div>
+                                                <img
+                                                    :src="'data:image/png;base64,' + expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.highlighted_page_image"
+                                                    :alt="'Page ' + expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.page_number"
+                                                    class="chunk-highlighted-image"
+                                                />
+                                            </div>
+                                        </template>
+                                        <!-- Text context -->
                                         <template x-if="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.has_more_before">
                                             <span class="chunk-ellipsis">...</span>
                                         </template>
diff --git a/nextcloud_mcp_server/auth/viz_routes.py b/nextcloud_mcp_server/auth/viz_routes.py
index 1567aff..d3a915d 100644
--- a/nextcloud_mcp_server/auth/viz_routes.py
+++ b/nextcloud_mcp_server/auth/viz_routes.py
@@ -564,17 +564,72 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
             f"after_len={len(chunk_context.after_context)}"
         )
 
+        # For PDF files, also fetch the highlighted page image from Qdrant
+        highlighted_page_image = None
+        page_number = None
+        if doc_type == "file":
+            try:
+                from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+                settings = get_settings()
+                qdrant_client = await get_qdrant_client()
+                username = request.user.display_name
+
+                # Query for this specific chunk's highlighted image
+                points_response = await qdrant_client.scroll(
+                    collection_name=settings.get_collection_name(),
+                    scroll_filter=Filter(
+                        must=[
+                            get_placeholder_filter(),
+                            FieldCondition(
+                                key="doc_id", match=MatchValue(value=doc_id_int)
+                            ),
+                            FieldCondition(
+                                key="user_id", match=MatchValue(value=username)
+                            ),
+                            FieldCondition(
+                                key="chunk_start_offset", match=MatchValue(value=start)
+                            ),
+                            FieldCondition(
+                                key="chunk_end_offset", match=MatchValue(value=end)
+                            ),
+                        ]
+                    ),
+                    limit=1,
+                    with_vectors=False,
+                    with_payload=["highlighted_page_image", "page_number"],
+                )
+
+                points = points_response[0]
+                if points and points[0].payload:
+                    highlighted_page_image = points[0].payload.get(
+                        "highlighted_page_image"
+                    )
+                    page_number = points[0].payload.get("page_number")
+                    if highlighted_page_image:
+                        logger.info(
+                            f"Found highlighted image for chunk: "
+                            f"page={page_number}, image_size={len(highlighted_page_image)}"
+                        )
+            except Exception as e:
+                logger.warning(f"Failed to fetch highlighted image: {e}")
+
         # Return response compatible with frontend expectations
-        return JSONResponse(
-            {
-                "success": True,
-                "chunk_text": chunk_context.chunk_text,
-                "before_context": chunk_context.before_context,
-                "after_context": chunk_context.after_context,
-                "has_more_before": chunk_context.has_before_truncation,
-                "has_more_after": chunk_context.has_after_truncation,
-            }
-        )
+        response_data: dict = {
+            "success": True,
+            "chunk_text": chunk_context.chunk_text,
+            "before_context": chunk_context.before_context,
+            "after_context": chunk_context.after_context,
+            "has_more_before": chunk_context.has_before_truncation,
+            "has_more_after": chunk_context.has_after_truncation,
+        }
+
+        # Add image data if available
+        if highlighted_page_image:
+            response_data["highlighted_page_image"] = highlighted_page_image
+            response_data["page_number"] = page_number
+
+        return JSONResponse(response_data)
 
     except ValueError as e:
         logger.error(f"Invalid parameter format: {e}")
diff --git a/nextcloud_mcp_server/document_processors/pymupdf.py b/nextcloud_mcp_server/document_processors/pymupdf.py
index 4b006c4..a3e45de 100644
--- a/nextcloud_mcp_server/document_processors/pymupdf.py
+++ b/nextcloud_mcp_server/document_processors/pymupdf.py
@@ -102,7 +102,7 @@ class PyMuPDFProcessor(DocumentProcessor):
                 await progress_callback(0, 100, "Processing PDF in background thread")
 
             # Run CPU-bound PDF processing in thread pool to avoid blocking event loop
-            result = await anyio.to_thread.run_sync(
+            result = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
                 self._process_sync,
                 content,
                 filename,
@@ -240,7 +240,7 @@ class PyMuPDFProcessor(DocumentProcessor):
         # Basic document info
         metadata["page_count"] = doc.page_count
         metadata["format"] = "PDF 1." + str(
-            doc.pdf_version() if hasattr(doc, "pdf_version") else "?"
+            doc.pdf_version() if hasattr(doc, "pdf_version") else "?"  # type: ignore[call-non-callable]
         )
 
         if filename:
diff --git a/nextcloud_mcp_server/embedding/bm25_provider.py b/nextcloud_mcp_server/embedding/bm25_provider.py
index df20e07..5567c26 100644
--- a/nextcloud_mcp_server/embedding/bm25_provider.py
+++ b/nextcloud_mcp_server/embedding/bm25_provider.py
@@ -66,7 +66,7 @@ class BM25SparseEmbeddingProvider:
         import anyio
 
         # Run CPU-bound BM25 encoding in thread pool to avoid blocking event loop
-        sparse_embeddings = await anyio.to_thread.run_sync(
+        sparse_embeddings = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
             lambda: list(self.model.embed(texts))
         )
 
diff --git a/nextcloud_mcp_server/search/pdf_highlighter.py b/nextcloud_mcp_server/search/pdf_highlighter.py
new file mode 100644
index 0000000..4a03ae1
--- /dev/null
+++ b/nextcloud_mcp_server/search/pdf_highlighter.py
@@ -0,0 +1,824 @@
+"""PDF chunk highlighting utilities for vector visualization.
+
+This module provides utilities to generate highlighted page images showing
+matched chunks and their context from semantic search results.
+
+The highlighting uses character offsets to precisely locate chunks within
+PDF documents, ensuring accurate highlighting even when text formatting
+varies between indexing and rendering.
+"""
+
+import logging
+import re
+from typing import Optional
+
+import pymupdf
+import pymupdf4llm
+
+logger = logging.getLogger(__name__)
+
+
+class PDFHighlighter:
+    """Generate highlighted page images from PDF chunks."""
+
+    # Color definitions (RGB, 0-1 range)
+    COLORS = {
+        "yellow": [1, 1, 0],
+        "red": [1, 0, 0],
+        "green": [0, 1, 0],
+        "blue": [0, 0, 1],
+        "orange": [1, 0.5, 0],
+        "pink": [1, 0, 1],
+        "gray": [0.7, 0.7, 0.7],
+        "light_blue": [0.7, 0.9, 1.0],
+        "light_green": [0.7, 1.0, 0.7],
+    }
+
+    @staticmethod
+    def strip_markdown(text: str) -> str:
+        """Remove markdown formatting to improve search accuracy.
+
+        Args:
+            text: Text with potential markdown formatting
+
+        Returns:
+            Plain text with markdown removed
+        """
+        # Remove bold/italic markers
+        text = re.sub(r"\*\*(.+?)\*\*", r"\1", text)
+        text = re.sub(r"\*(.+?)\*", r"\1", text)
+        text = re.sub(r"__(.+?)__", r"\1", text)
+        text = re.sub(r"_(.+?)_", r"\1", text)
+
+        # Remove headers
+        text = re.sub(r"^#+\s+", "", text, flags=re.MULTILINE)
+
+        # Remove inline code
+        text = re.sub(r"`(.+?)`", r"\1", text)
+
+        return text.strip()
+
+    @staticmethod
+    def extract_pdf_text_with_boundaries(
+        pdf_doc: pymupdf.Document,
+    ) -> tuple[str, list[dict]]:
+        """Extract full document text with page boundary tracking.
+
+        Uses pymupdf4llm.to_markdown() for consistency with indexing.
+
+        IMPORTANT: Must use write_images=True to match PyMuPDFProcessor behavior!
+        Even though we don't need the images, we need the image references in the
+        markdown text to maintain consistent character offsets with indexing.
+
+        Args:
+            pdf_doc: Open PyMuPDF document
+
+        Returns:
+            Tuple of (full_text, page_boundaries) where page_boundaries is a list of:
+            {"page": 1, "start_offset": 0, "end_offset": 1234}
+        """
+        import tempfile
+        from pathlib import Path
+
+        page_boundaries = []
+        text_parts = []
+        current_offset = 0
+
+        # Use temp directory for image output (images are discarded after extraction)
+        temp_dir = Path(tempfile.mkdtemp(prefix="pdf_highlight_"))
+
+        for page_idx in range(pdf_doc.page_count):
+            page_md = pymupdf4llm.to_markdown(
+                pdf_doc,
+                pages=[page_idx],
+                write_images=True,  # Must match indexing! Otherwise offsets misalign
+                image_path=temp_dir,
+                page_chunks=False,
+            )
+
+            page_boundaries.append(
+                {
+                    "page": page_idx + 1,  # 1-indexed
+                    "start_offset": current_offset,
+                    "end_offset": current_offset + len(page_md),
+                }
+            )
+
+            text_parts.append(page_md)
+            current_offset += len(page_md)
+
+        full_text = "".join(text_parts)
+
+        # Clean up temp directory and extracted images
+        import shutil
+
+        try:
+            shutil.rmtree(temp_dir)
+        except Exception as e:
+            logger.warning(f"Failed to clean up temp directory {temp_dir}: {e}")
+
+        return full_text, page_boundaries
+
+    @staticmethod
+    def find_chunk_page(
+        chunk_start_offset: int,
+        chunk_end_offset: int,
+        page_boundaries: list[dict],
+    ) -> Optional[dict]:
+        """Find which page contains the most of a given chunk.
+
+        Args:
+            chunk_start_offset: Chunk start position in full document
+            chunk_end_offset: Chunk end position in full document
+            page_boundaries: Page boundary list from extract_pdf_text_with_boundaries()
+
+        Returns:
+            Dict with keys: page_num, overlap_chars, page_relative_start, page_relative_end
+            or None if chunk not found on any page
+        """
+        chunk_pages = []
+
+        for boundary in page_boundaries:
+            page_start = boundary["start_offset"]
+            page_end = boundary["end_offset"]
+
+            # Check if chunk overlaps with this page
+            if chunk_start_offset < page_end and chunk_end_offset > page_start:
+                overlap_start = max(chunk_start_offset, page_start)
+                overlap_end = min(chunk_end_offset, page_end)
+                overlap_chars = overlap_end - overlap_start
+
+                chunk_pages.append(
+                    {
+                        "page_num": boundary["page"],
+                        "overlap_chars": overlap_chars,
+                        "page_relative_start": overlap_start - page_start,
+                        "page_relative_end": overlap_end - page_start,
+                    }
+                )
+
+        if not chunk_pages:
+            return None
+
+        # Return page with maximum overlap
+        return max(chunk_pages, key=lambda p: p["overlap_chars"])
+
+    @staticmethod
+    def highlight_chunk_by_word_positions(
+        page: pymupdf.Page,
+        chunk_text: str,
+        color: str = "yellow",
+        search_region: tuple[float, float, float, float] | None = None,
+    ) -> int:
+        """Highlight chunk using word-position matching.
+
+        This method matches words from the chunk to their positions on the PDF page,
+        avoiding text search mismatches between markdown-formatted text and raw PDF text.
+
+        Args:
+            page: PyMuPDF page object
+            chunk_text: Text to highlight (may contain markdown)
+            color: Color name from COLORS dict
+            search_region: Optional (x0, y0, x1, y1) bounding box to constrain search.
+                          If provided, only words within this region are considered.
+
+        Returns:
+            Number of highlight rectangles added
+        """
+        # Tokenize chunk into words (alphanumeric only, lowercase)
+        chunk_words = re.findall(
+            r"\w+", PDFHighlighter.strip_markdown(chunk_text).lower()
+        )
+
+        if not chunk_words:
+            logger.warning("No words found in chunk text")
+            return 0
+
+        # Get all words from page with positions
+        # Format: (x0, y0, x1, y1, "word", block_no, line_no, word_no)
+        try:
+            page_words = page.get_text("words")
+        except Exception as e:
+            logger.error(f"Failed to extract words from page: {e}")
+            return 0
+
+        if not page_words:
+            logger.warning("No words found on page")
+            return 0
+
+        # Filter words by search region if provided
+        if search_region:
+            rx0, ry0, rx1, ry1 = search_region
+            # Allow some tolerance (10 points) for words near region boundary
+            tolerance = 10
+            page_words = [
+                w
+                for w in page_words
+                if (
+                    w[0] >= rx0 - tolerance
+                    and w[2] <= rx1 + tolerance
+                    and w[1] >= ry0 - tolerance
+                    and w[3] <= ry1 + tolerance
+                )
+            ]
+            logger.debug(
+                f"Filtered to {len(page_words)} words in region "
+                f"({rx0:.0f}, {ry0:.0f}, {rx1:.0f}, {ry1:.0f})"
+            )
+
+        if not page_words:
+            logger.warning("No words found in search region")
+            return 0
+
+        # Find matching word sequence - use FIRST match, not longest
+        # This ensures we highlight the actual chunk location, not similar text elsewhere
+        matches = []
+
+        # Build a simple word-to-positions index for the first few chunk words
+        # to find candidate starting positions
+        first_chunk_word = chunk_words[0] if chunk_words else ""
+        candidate_starts = []
+
+        for i, pw in enumerate(page_words):
+            page_word = pw[4].lower()
+            # Check if this could be the start of the chunk
+            if (
+                first_chunk_word == page_word
+                or first_chunk_word in page_word
+                or page_word in first_chunk_word
+            ):
+                candidate_starts.append(i)
+
+        # Try each candidate start position and take the FIRST good match
+        for start_pos in candidate_starts:
+            current_matches = []
+            chunk_idx = 0
+            skip_count = 0
+            max_skips = 3  # Allow some formatting differences
+
+            for page_idx in range(start_pos, len(page_words)):
+                if chunk_idx >= len(chunk_words):
+                    break
+
+                page_word = page_words[page_idx][4].lower()
+                chunk_word = chunk_words[chunk_idx]
+
+                # Check for match (allow partial matches for flexibility)
+                if (
+                    chunk_word == page_word
+                    or chunk_word in page_word
+                    or page_word in chunk_word
+                ):
+                    current_matches.append(page_words[page_idx])
+                    chunk_idx += 1
+                    skip_count = 0
+                elif skip_count < max_skips:
+                    # Allow skipping some words (formatting, punctuation)
+                    skip_count += 1
+                    continue
+                else:
+                    break
+
+            # Accept if we matched at least 50% of chunk words
+            if len(current_matches) >= len(chunk_words) * 0.5:
+                matches = current_matches
+                logger.debug(
+                    f"Found match at position {start_pos}: "
+                    f"{len(matches)}/{len(chunk_words)} words"
+                )
+                break  # Take FIRST match, not best/longest
+
+        if not matches:
+            logger.debug(f"No word matches found (chunk has {len(chunk_words)} words)")
+            return 0
+
+        logger.debug(
+            f"Matched {len(matches)} words out of {len(chunk_words)} chunk words"
+        )
+
+        # Build rectangles from matched words
+        rects = [pymupdf.Rect(w[0], w[1], w[2], w[3]) for w in matches]
+
+        # Check if matches are contiguous (not scattered across the page)
+        # Scattered matches indicate false positives from common words
+        if len(rects) > 1:
+            # Sort by vertical position then horizontal
+            sorted_matches = sorted(matches, key=lambda w: (round(w[1]), w[0]))
+
+            # Check for large vertical gaps (more than ~2 lines apart)
+            # A typical line height is 12-20 points
+            max_line_gap = 50  # Points - allows for ~2-3 lines gap
+            prev_y = sorted_matches[0][1]
+            large_gaps = 0
+
+            for match in sorted_matches[1:]:
+                y_gap = match[1] - prev_y
+                if y_gap > max_line_gap:
+                    large_gaps += 1
+                prev_y = match[1]
+
+            # If matches are scattered (many large gaps), reject this match
+            # A chunk should be mostly contiguous text
+            if large_gaps > len(matches) * 0.3:  # More than 30% have gaps
+                logger.debug(
+                    f"Rejecting scattered matches: {large_gaps} large gaps "
+                    f"out of {len(matches)} matches"
+                )
+                return 0
+
+        # Merge adjacent rectangles on the same line for cleaner highlighting
+        merged_rects = []
+        sorted_rects = sorted(rects, key=lambda r: (round(r.y0), r.x0))
+
+        current_rect = None
+        for rect in sorted_rects:
+            if current_rect is None:
+                current_rect = rect
+            elif abs(rect.y0 - current_rect.y0) < 5:  # Same line (within 5 points)
+                current_rect = current_rect | rect  # Union
+            else:
+                merged_rects.append(current_rect)
+                current_rect = rect
+
+        if current_rect:
+            merged_rects.append(current_rect)
+
+        # Add highlights
+        rgb = PDFHighlighter.COLORS.get(color, PDFHighlighter.COLORS["yellow"])
+        for rect in merged_rects:
+            highlight = page.add_highlight_annot(rect)
+            highlight.set_colors({"stroke": rgb})
+            highlight.set_info(
+                content="Chunk from semantic search",
+                title="PDF Highlighter (word-position)",
+            )
+            highlight.update()
+
+        return len(merged_rects)
+
+    @staticmethod
+    def find_unique_phrase(
+        text: str, min_len: int = 30, max_len: int = 80
+    ) -> str | None:
+        """Find a relatively unique phrase from text for location search.
+
+        Looks for phrases that are likely to be unique on the page:
+        - Prefers phrases with numbers or special terms
+        - Avoids very common words
+
+        Args:
+            text: Source text to extract phrase from
+            min_len: Minimum phrase length
+            max_len: Maximum phrase length
+
+        Returns:
+            A phrase likely to be unique, or None if not found
+        """
+        clean_text = PDFHighlighter.strip_markdown(text).strip()
+        if not clean_text:
+            return None
+
+        # Try first sentence (often unique due to context)
+        sentences = re.split(r"[.!?]\s+", clean_text)
+        for sentence in sentences:
+            sentence = sentence.strip()
+            if min_len <= len(sentence) <= max_len:
+                return sentence
+            elif len(sentence) > max_len:
+                return sentence[:max_len]
+
+        # Fallback: first N chars
+        if len(clean_text) >= min_len:
+            return clean_text[:max_len]
+
+        return clean_text if clean_text else None
+
+    @staticmethod
+    def highlight_chunk_on_page(
+        page: pymupdf.Page,
+        chunk_text: str,
+        color: str = "yellow",
+        page_relative_start: int | None = None,
+        page_relative_end: int | None = None,
+        page_text_length: int | None = None,
+    ) -> int:
+        """Add bounding box highlight to a PDF page for the given chunk text.
+
+        Uses text search to find the chunk's location on the page, then draws
+        a bounding box around that region. Falls back to character offset estimation
+        if text search fails.
+
+        Args:
+            page: PyMuPDF page object
+            chunk_text: Text to highlight (may contain markdown)
+            color: Color name from COLORS dict
+            page_relative_start: Character offset where chunk starts on page (optional)
+            page_relative_end: Character offset where chunk ends on page (optional)
+            page_text_length: Total character length of page text (optional)
+
+        Returns:
+            Number of highlights added (1 for bounding box, 0 if failed)
+        """
+        page_rect = page.rect
+        rgb = PDFHighlighter.COLORS.get(color, PDFHighlighter.COLORS["yellow"])
+
+        # Strip markdown for searching
+        search_text = PDFHighlighter.strip_markdown(chunk_text)
+
+        # Try to find chunk location using text search
+        # Search for progressively shorter phrases until we find a match
+        anchor_rect = None
+        search_phrases = []
+
+        # Build search phrases from chunk text
+        sentences = re.split(r"[.!?]\s+", search_text)
+        for sentence in sentences[:3]:  # Try first 3 sentences
+            sentence = sentence.strip()
+            if len(sentence) >= 20:
+                search_phrases.append(sentence[:80])
+                if len(sentence) >= 40:
+                    search_phrases.append(sentence[:40])
+
+        # Also try first N characters
+        if len(search_text) >= 30:
+            search_phrases.append(search_text[:60])
+            search_phrases.append(search_text[:30])
+
+        for phrase in search_phrases:
+            if not phrase:
+                continue
+            rects = page.search_for(phrase.strip())
+            if rects:
+                anchor_rect = rects[0]  # Use first match
+                logger.debug(f"Found chunk anchor using phrase: '{phrase[:30]}...'")
+                break
+
+        if not anchor_rect:
+            page_num = page.number + 1 if page.number is not None else "unknown"
+            logger.warning(f"Could not find chunk text on page {page_num}")
+            return 0
+
+        # Calculate chunk height based on character count
+        # Estimate ~15 chars per line, ~12pt line height
+        chunk_chars = len(search_text)
+        estimated_lines = max(1, chunk_chars / 60)  # ~60 chars per line typical
+        estimated_height = estimated_lines * 14  # ~14pt per line
+
+        # Build bounding box starting from anchor
+        chunk_rect = pymupdf.Rect(
+            page_rect.x0 + 30,  # Left margin
+            anchor_rect.y0 - 5,  # Start slightly above anchor
+            page_rect.x1 - 30,  # Right margin
+            min(
+                anchor_rect.y0 + estimated_height + 10, page_rect.y1 - 30
+            ),  # Estimated bottom
+        )
+
+        # Draw a visible rectangle around the chunk region
+        shape = page.new_shape()
+        shape.draw_rect(chunk_rect)
+        shape.finish(
+            color=rgb,  # Border color
+            fill=None,  # No fill (transparent)
+            width=2.5,  # Border width
+            dashes="[4 2]",  # Dashed line
+        )
+        shape.commit()
+
+        # Add semi-transparent fill for visibility
+        fill_shape = page.new_shape()
+        fill_shape.draw_rect(chunk_rect)
+        fill_shape.finish(
+            color=None,  # No border
+            fill=[1, 1, 0.7],  # Light yellow fill
+            fill_opacity=0.15,  # Very transparent
+        )
+        fill_shape.commit()
+
+        logger.debug(
+            f"Added bounding box at y={chunk_rect.y0:.0f}-{chunk_rect.y1:.0f} "
+            f"(estimated {estimated_lines:.1f} lines)"
+        )
+
+        return 1
+
+    @staticmethod
+    def highlight_chunk(
+        pdf_bytes: bytes,
+        chunk_start_offset: int,
+        chunk_end_offset: int,
+        stored_page_number: Optional[int] = None,
+        color: str = "yellow",
+        zoom: float = 2.0,
+    ) -> Optional[tuple[bytes, int, int]]:
+        """Generate PNG image of PDF page with highlighted chunk.
+
+        This is the main entry point for highlighting. It:
+        1. Extracts document text with page boundaries
+        2. Finds which page contains the chunk
+        3. Extracts chunk text using character offsets
+        4. Highlights the chunk on the page
+        5. Renders page to PNG
+
+        Args:
+            pdf_bytes: PDF file bytes
+            chunk_start_offset: Chunk start position (document-level)
+            chunk_end_offset: Chunk end position (document-level)
+            stored_page_number: Page number from metadata (optional, for validation)
+            color: Highlight color name
+            zoom: Rendering zoom factor (2.0 = 144 DPI)
+
+        Returns:
+            Tuple of (png_bytes, page_number, highlight_count) or None if failed
+        """
+        import tempfile
+        from pathlib import Path
+
+        temp_pdf_path = None
+        try:
+            # Write PDF to temp file with consistent name "pdf.pdf"
+            # This ensures image references match indexing (e.g., pdf-0001.png)
+            # Different temp filenames would cause different markdown text lengths!
+            temp_dir = Path(tempfile.mkdtemp(prefix="pdf_highlight_"))
+            temp_pdf_path = temp_dir / "pdf.pdf"
+            temp_pdf_path.write_bytes(pdf_bytes)
+
+            # Open PDF from temp file
+            doc = pymupdf.open(temp_pdf_path)
+
+            # Extract text with page boundaries
+            full_text, page_boundaries = (
+                PDFHighlighter.extract_pdf_text_with_boundaries(doc)
+            )
+
+            # Find which page contains the chunk
+            chunk_page_info = PDFHighlighter.find_chunk_page(
+                chunk_start_offset, chunk_end_offset, page_boundaries
+            )
+
+            if not chunk_page_info:
+                logger.error("Chunk not found on any page")
+                doc.close()
+                return None
+
+            page_num = chunk_page_info["page_num"]
+
+            # Log if page differs from stored metadata
+            if stored_page_number and stored_page_number != page_num:
+                logger.info(
+                    f"Chunk primarily on page {page_num}, metadata says {stored_page_number}"
+                )
+
+            # Extract page text
+            page_boundary = page_boundaries[page_num - 1]
+            page_start = page_boundary["start_offset"]
+            page_end = page_boundary["end_offset"]
+            page_text = full_text[page_start:page_end]
+
+            # Extract chunk text using page-relative offsets
+            page_relative_start = chunk_page_info["page_relative_start"]
+            page_relative_end = chunk_page_info["page_relative_end"]
+            chunk_text = page_text[page_relative_start:page_relative_end]
+
+            # Calculate page text length for region estimation
+            page_text_length = page_end - page_start
+
+            logger.debug(
+                f"Extracted {len(chunk_text)} chars on page {page_num} "
+                f"(offsets {page_relative_start}-{page_relative_end} of {page_text_length})"
+            )
+
+            # Get page and add highlights
+            page = doc[page_num - 1]
+            highlight_count = PDFHighlighter.highlight_chunk_on_page(
+                page,
+                chunk_text,
+                color,
+                page_relative_start=page_relative_start,
+                page_relative_end=page_relative_end,
+                page_text_length=page_text_length,
+            )
+
+            if highlight_count == 0:
+                logger.warning("No highlights added")
+                doc.close()
+                return None
+
+            # Render page to PNG
+            mat = pymupdf.Matrix(zoom, zoom)
+            pix = page.get_pixmap(matrix=mat, alpha=False)
+            png_bytes = pix.tobytes("png")
+
+            doc.close()
+
+            logger.info(
+                f"Generated {len(png_bytes):,} byte image with {highlight_count} highlights"
+            )
+
+            return (png_bytes, page_num, highlight_count)
+
+        except Exception as e:
+            logger.error(f"Error highlighting chunk: {e}", exc_info=True)
+            return None
+
+        finally:
+            # Clean up temp directory and PDF file
+            if temp_pdf_path and temp_pdf_path.parent.exists():
+                try:
+                    import shutil
+
+                    shutil.rmtree(temp_pdf_path.parent)
+                except Exception as e:
+                    logger.warning(
+                        f"Failed to delete temp directory {temp_pdf_path.parent}: {e}"
+                    )
+
+    @staticmethod
+    def highlight_chunks_batch(
+        pdf_bytes: bytes,
+        chunks: list[tuple[int, int, int, int | None, str]],
+        page_boundaries: list[dict],
+        full_text: str,
+        color: str = "yellow",
+        zoom: float = 2.0,
+    ) -> dict[int, tuple[bytes, int, int]]:
+        """Generate highlighted images for multiple chunks.
+
+        Opens PDF once for rendering, uses pre-computed page boundaries from the
+        document processor. This ensures consistent character offsets between
+        chunking and highlighting.
+
+        Args:
+            pdf_bytes: PDF file bytes
+            chunks: List of (chunk_index, start_offset, end_offset, stored_page_number, chunk_text)
+                    The chunk_index is used as the key in the returned dict.
+                    chunk_text is the actual text content of the chunk.
+            page_boundaries: Pre-computed page boundaries from document processor.
+                            Each entry: {"page": 1, "start_offset": 0, "end_offset": 1234}
+            full_text: Full document text for extracting page-relative portions.
+            color: Highlight color name
+            zoom: Rendering zoom factor (2.0 = 144 DPI)
+
+        Returns:
+            Dict mapping chunk_index to (png_bytes, page_number, highlight_count)
+            Chunks that fail to highlight are omitted from the result.
+        """
+        import shutil
+        import tempfile
+        from collections import defaultdict
+        from pathlib import Path
+
+        results: dict[int, tuple[bytes, int, int]] = {}
+
+        if not chunks:
+            return results
+
+        temp_pdf_path = None
+        try:
+            # Write PDF to temp file
+            temp_dir = Path(tempfile.mkdtemp(prefix="pdf_highlight_batch_"))
+            temp_pdf_path = temp_dir / "pdf.pdf"
+            temp_pdf_path.write_bytes(pdf_bytes)
+
+            # Open PDF once (only for rendering, not text extraction)
+            doc = pymupdf.open(temp_pdf_path)
+
+            logger.debug(
+                f"Batch highlighting: {len(chunks)} chunks, "
+                f"{len(page_boundaries)} pages"
+            )
+
+            # Group chunks by their target page for efficient rendering
+            # We'll render each page only once with all its highlights
+            chunks_by_page: dict[int, list[tuple[int, dict, str]]] = defaultdict(list)
+
+            for chunk_tuple in chunks:
+                # Unpack chunk tuple - chunk_text is now passed directly
+                chunk_index, start_offset, end_offset, stored_page_num, chunk_text = (
+                    chunk_tuple
+                )
+
+                # Find which page contains this chunk
+                chunk_page_info = PDFHighlighter.find_chunk_page(
+                    start_offset, end_offset, page_boundaries
+                )
+
+                if not chunk_page_info:
+                    logger.warning(f"Chunk {chunk_index}: not found on any page")
+                    continue
+
+                page_num = chunk_page_info["page_num"]
+
+                # Log if page differs from stored metadata
+                if stored_page_num and stored_page_num != page_num:
+                    logger.debug(
+                        f"Chunk {chunk_index}: found on page {page_num}, "
+                        f"metadata says {stored_page_num}"
+                    )
+
+                # Extract page-relative portion of chunk text
+                # This is critical for cross-page chunks where the start
+                # of the chunk might be on a different page
+                page_boundary = page_boundaries[page_num - 1]
+                page_start = page_boundary["start_offset"]
+                page_end = page_boundary["end_offset"]
+                page_text_length = page_end - page_start
+
+                # Calculate what portion of the chunk appears on this page
+                chunk_start_on_page = max(start_offset, page_start)
+                chunk_end_on_page = min(end_offset, page_end)
+
+                # Extract just the text that appears on this page
+                page_relative_text = full_text[chunk_start_on_page:chunk_end_on_page]
+
+                chunks_by_page[page_num].append(
+                    (chunk_index, chunk_page_info, page_relative_text, page_text_length)
+                )
+
+            logger.debug(
+                f"Chunks distributed across {len(chunks_by_page)} unique pages"
+            )
+
+            # Process each chunk, rendering with only its own highlights
+            # Store original page contents to restore between chunks
+            page_contents_cache: dict[int, list[bytes]] = {}
+
+            for page_num, page_chunks in chunks_by_page.items():
+                page = doc[page_num - 1]
+
+                # Cache original page contents (before any highlights added)
+                # xref is the PDF object reference for each content stream
+                if page_num not in page_contents_cache:
+                    page_contents_cache[page_num] = []
+                    xrefs = page.get_contents()
+                    for xref in xrefs:
+                        page_contents_cache[page_num].append(doc.xref_stream(xref))
+
+                for (
+                    chunk_index,
+                    chunk_page_info,
+                    chunk_text,
+                    page_text_length,
+                ) in page_chunks:
+                    try:
+                        # Restore original page contents to remove previous highlights
+                        # Highlights are drawn shapes, not annotations, so we must
+                        # restore the content stream to clear them
+                        xrefs = page.get_contents()
+                        for i, xref in enumerate(xrefs):
+                            if i < len(page_contents_cache[page_num]):
+                                doc.update_stream(
+                                    xref, page_contents_cache[page_num][i]
+                                )
+
+                        # Add highlights for this chunk with region constraint
+                        page_relative_start = chunk_page_info["page_relative_start"]
+                        page_relative_end = chunk_page_info["page_relative_end"]
+                        highlight_count = PDFHighlighter.highlight_chunk_on_page(
+                            page,
+                            chunk_text,
+                            color,
+                            page_relative_start=page_relative_start,
+                            page_relative_end=page_relative_end,
+                            page_text_length=page_text_length,
+                        )
+
+                        if highlight_count == 0:
+                            logger.warning(f"Chunk {chunk_index}: no highlights added")
+                            continue
+
+                        # Render page to PNG
+                        mat = pymupdf.Matrix(zoom, zoom)
+                        pix = page.get_pixmap(matrix=mat, alpha=False)
+                        png_bytes = pix.tobytes("png")
+
+                        results[chunk_index] = (png_bytes, page_num, highlight_count)
+
+                        logger.debug(
+                            f"Chunk {chunk_index}: {len(png_bytes):,} bytes, "
+                            f"page {page_num}, {highlight_count} highlights"
+                        )
+
+                    except Exception as e:
+                        logger.error(f"Chunk {chunk_index}: error - {e}")
+                        continue
+
+            doc.close()
+
+            logger.info(
+                f"Batch highlighted {len(results)}/{len(chunks)} chunks successfully"
+            )
+
+            return results
+
+        except Exception as e:
+            logger.error(f"Error in batch highlighting: {e}", exc_info=True)
+            return results
+
+        finally:
+            # Clean up temp directory
+            if temp_pdf_path and temp_pdf_path.parent.exists():
+                try:
+                    shutil.rmtree(temp_pdf_path.parent)
+                except Exception as e:
+                    logger.warning(f"Failed to clean up temp dir: {e}")
diff --git a/nextcloud_mcp_server/vector/document_chunker.py b/nextcloud_mcp_server/vector/document_chunker.py
index 56a5605..70184f5 100644
--- a/nextcloud_mcp_server/vector/document_chunker.py
+++ b/nextcloud_mcp_server/vector/document_chunker.py
@@ -75,7 +75,7 @@ class DocumentChunker:
             return [ChunkWithPosition(text="", start_offset=0, end_offset=0)]
 
         # Run CPU-bound text splitting in thread pool to avoid blocking event loop
-        docs = await anyio.to_thread.run_sync(
+        docs = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
             self.splitter.create_documents,
             [content],
         )
diff --git a/nextcloud_mcp_server/vector/processor.py b/nextcloud_mcp_server/vector/processor.py
index 6f3fc5c..85becd9 100644
--- a/nextcloud_mcp_server/vector/processor.py
+++ b/nextcloud_mcp_server/vector/processor.py
@@ -256,115 +256,265 @@ async def _index_document(
     settings = get_settings()
 
     # Fetch document content
-    if doc_task.doc_type == "note":
-        document = await nc_client.notes.get_note(int(doc_task.doc_id))
-        content = f"{document['title']}\n\n{document['content']}"
-        title = document["title"]
-        etag = document.get("etag", "")
-        file_metadata = {}  # No file-specific metadata for notes
-        file_path = None  # Notes don't have file paths
-    elif doc_task.doc_type == "file":
-        # For files, doc_id is now the numeric file ID, file_path comes from DocumentTask
-        if not doc_task.file_path:
-            raise ValueError(
-                f"File path required for file indexing but not provided (file_id={doc_task.doc_id})"
-            )
-        file_path = doc_task.file_path
-
-        # Read file content via WebDAV
-        content_bytes, content_type = await nc_client.webdav.read_file(file_path)
-
-        # Use document processor registry to extract text
-        from nextcloud_mcp_server.document_processors import get_registry
-
-        registry = get_registry()
-
-        try:
-            result = await registry.process(
-                content=content_bytes,
-                content_type=content_type,
-                filename=file_path,
-            )
-            content = result.text
-            file_metadata = result.metadata
-            title = file_metadata.get("title") or file_path.split("/")[-1]
-            etag = ""  # WebDAV read_file doesn't return etag
-
-            # Diagnostic: Log page boundary information if available
-            if "page_boundaries" in file_metadata:
-                page_boundaries = file_metadata["page_boundaries"]
-                logger.info(
-                    f"Page boundaries for {file_path}: "
-                    f"{len(page_boundaries)} pages, text length: {len(content)}"
+    with trace_operation(
+        "vector_sync.fetch_content",
+        attributes={
+            "vector_sync.doc_type": doc_task.doc_type,
+            "vector_sync.doc_id": doc_task.doc_id,
+        },
+    ):
+        if doc_task.doc_type == "note":
+            document = await nc_client.notes.get_note(int(doc_task.doc_id))
+            content = f"{document['title']}\n\n{document['content']}"
+            title = document["title"]
+            etag = document.get("etag", "")
+            file_metadata = {}  # No file-specific metadata for notes
+            file_path = None  # Notes don't have file paths
+            content_bytes = None  # Notes don't have binary content
+            content_type = None
+        elif doc_task.doc_type == "file":
+            # For files, doc_id is now the numeric file ID, file_path comes from DocumentTask
+            if not doc_task.file_path:
+                raise ValueError(
+                    f"File path required for file indexing but not provided (file_id={doc_task.doc_id})"
                 )
-                # Log first 3 page boundaries for debugging
-                for boundary in page_boundaries[:3]:
-                    logger.debug(
-                        f"  Page {boundary['page']}: "
-                        f"offsets [{boundary['start_offset']}:{boundary['end_offset']}]"
+            file_path = doc_task.file_path
+
+            # Read file content via WebDAV
+            content_bytes, content_type = await nc_client.webdav.read_file(file_path)
+        else:
+            raise ValueError(f"Unsupported doc_type: {doc_task.doc_type}")
+
+    # Process file content (text extraction)
+    if doc_task.doc_type == "file":
+        # Type narrowing: content_bytes and content_type are set for files
+        assert content_bytes is not None
+        assert content_type is not None
+        assert file_path is not None
+
+        with trace_operation(
+            "vector_sync.document_process",
+            attributes={
+                "vector_sync.content_type": content_type,
+                "vector_sync.file_size": len(content_bytes),
+            },
+        ):
+            # Use document processor registry to extract text
+            from nextcloud_mcp_server.document_processors import get_registry
+
+            registry = get_registry()
+
+            try:
+                result = await registry.process(
+                    content=content_bytes,
+                    content_type=content_type,
+                    filename=file_path,
+                )
+                content = result.text
+                file_metadata = result.metadata
+                title = file_metadata.get("title") or file_path.split("/")[-1]
+                etag = ""  # WebDAV read_file doesn't return etag
+
+                # Diagnostic: Log page boundary information if available
+                if "page_boundaries" in file_metadata:
+                    page_boundaries = file_metadata["page_boundaries"]
+                    logger.info(
+                        f"Page boundaries for {file_path}: "
+                        f"{len(page_boundaries)} pages, text length: {len(content)}"
                     )
-                # Verify last boundary matches text length
-                if page_boundaries:
-                    last_boundary = page_boundaries[-1]
-                    if last_boundary["end_offset"] != len(content):
-                        logger.warning(
-                            f"Text length mismatch: content={len(content)}, "
-                            f"last_boundary_end={last_boundary['end_offset']}"
+                    # Log first 3 page boundaries for debugging
+                    for boundary in page_boundaries[:3]:
+                        logger.debug(
+                            f"  Page {boundary['page']}: "
+                            f"offsets [{boundary['start_offset']}:{boundary['end_offset']}]"
                         )
-            else:
-                logger.debug(f"No page_boundaries in metadata for {file_path}")
-        except Exception as e:
-            logger.error(f"Failed to process file {file_path}: {e}")
-            raise
-    else:
-        raise ValueError(f"Unsupported doc_type: {doc_task.doc_type}")
+                    # Verify last boundary matches text length
+                    if page_boundaries:
+                        last_boundary = page_boundaries[-1]
+                        if last_boundary["end_offset"] != len(content):
+                            logger.warning(
+                                f"Text length mismatch: content={len(content)}, "
+                                f"last_boundary_end={last_boundary['end_offset']}"
+                            )
+                else:
+                    logger.debug(f"No page_boundaries in metadata for {file_path}")
+            except Exception as e:
+                logger.error(f"Failed to process file {file_path}: {e}")
+                raise
 
     # Tokenize and chunk (using configured chunk size and overlap)
-    chunker = DocumentChunker(
-        chunk_size=settings.document_chunk_size,
-        overlap=settings.document_chunk_overlap,
-    )
-    chunks = await chunker.chunk_text(content)
+    with trace_operation(
+        "vector_sync.chunk_text",
+        attributes={
+            "vector_sync.input_chars": len(content),
+            "vector_sync.chunk_size": settings.document_chunk_size,
+            "vector_sync.overlap": settings.document_chunk_overlap,
+        },
+    ):
+        chunker = DocumentChunker(
+            chunk_size=settings.document_chunk_size,
+            overlap=settings.document_chunk_overlap,
+        )
+        chunks = await chunker.chunk_text(content)
 
     # Assign page numbers to chunks if page boundaries are available (PDFs)
     if doc_task.doc_type == "file" and "page_boundaries" in file_metadata:
-        assign_page_numbers(chunks, file_metadata["page_boundaries"])
+        with trace_operation(
+            "vector_sync.assign_page_numbers",
+            attributes={
+                "vector_sync.chunk_count": len(chunks),
+                "vector_sync.page_count": len(file_metadata["page_boundaries"]),
+            },
+        ):
+            assign_page_numbers(chunks, file_metadata["page_boundaries"])
 
-        # Diagnostic: Verify page number assignment
-        assigned_count = sum(1 for c in chunks if c.page_number is not None)
-        logger.info(
-            f"Assigned page numbers to {assigned_count}/{len(chunks)} chunks "
-            f"for {file_path}"
-        )
-
-        # Log first 3 chunks to see their page assignments
-        for i, chunk in enumerate(chunks[:3]):
-            logger.debug(
-                f"  Chunk {i}: page={chunk.page_number}, "
-                f"offsets=[{chunk.start_offset}:{chunk.end_offset}]"
+            # Diagnostic: Verify page number assignment
+            assigned_count = sum(1 for c in chunks if c.page_number is not None)
+            logger.info(
+                f"Assigned page numbers to {assigned_count}/{len(chunks)} chunks "
+                f"for {file_path}"
             )
 
-        # Warning if NO page numbers were assigned
-        if assigned_count == 0:
-            logger.warning(
-                f"NO page numbers assigned! "
-                f"Text length: {len(content)}, "
-                f"Chunks: {len(chunks)}, "
-                f"Chunk offset range: [{chunks[0].start_offset}:{chunks[-1].end_offset}], "
-                f"Page boundaries: {len(file_metadata['page_boundaries'])} pages, "
-                f"First boundary: {file_metadata['page_boundaries'][0] if file_metadata['page_boundaries'] else 'None'}"
-            )
+            # Log first 3 chunks to see their page assignments
+            for i, chunk in enumerate(chunks[:3]):
+                logger.debug(
+                    f"  Chunk {i}: page={chunk.page_number}, "
+                    f"offsets=[{chunk.start_offset}:{chunk.end_offset}]"
+                )
+
+            # Warning if NO page numbers were assigned
+            if assigned_count == 0:
+                logger.warning(
+                    f"NO page numbers assigned! "
+                    f"Text length: {len(content)}, "
+                    f"Chunks: {len(chunks)}, "
+                    f"Chunk offset range: [{chunks[0].start_offset}:{chunks[-1].end_offset}], "
+                    f"Page boundaries: {len(file_metadata['page_boundaries'])} pages, "
+                    f"First boundary: {file_metadata['page_boundaries'][0] if file_metadata['page_boundaries'] else 'None'}"
+                )
 
     # Extract chunk texts for embedding
     chunk_texts = [chunk.text for chunk in chunks]
 
-    # Generate dense embeddings (I/O bound - external API call)
-    embedding_service = get_embedding_service()
-    dense_embeddings = await embedding_service.embed_batch(chunk_texts)
+    # Initialize results containers
+    dense_embeddings: list = []
+    sparse_embeddings: list = []
+    chunk_images: dict[int, dict] = {}
 
-    # Generate sparse embeddings (BM25 for keyword matching)
-    bm25_service = get_bm25_service()
-    sparse_embeddings = await bm25_service.encode_batch(chunk_texts)
+    # Determine if we need PDF highlighting
+    is_pdf = doc_task.doc_type == "file" and content_type == "application/pdf"
+
+    # Define async tasks for parallel execution
+    async def generate_dense_embeddings():
+        """Generate dense embeddings (I/O bound - external API call)."""
+        nonlocal dense_embeddings
+        with trace_operation(
+            "vector_sync.embed_dense",
+            attributes={
+                "vector_sync.chunk_count": len(chunk_texts),
+                "vector_sync.total_chars": sum(len(t) for t in chunk_texts),
+            },
+        ):
+            embedding_service = get_embedding_service()
+            dense_embeddings = await embedding_service.embed_batch(chunk_texts)
+
+    async def generate_sparse_embeddings():
+        """Generate sparse embeddings (BM25 for keyword matching)."""
+        nonlocal sparse_embeddings
+        with trace_operation(
+            "vector_sync.embed_sparse",
+            attributes={
+                "vector_sync.chunk_count": len(chunk_texts),
+            },
+        ):
+            bm25_service = get_bm25_service()
+            sparse_embeddings = await bm25_service.encode_batch(chunk_texts)
+
+    async def generate_highlights():
+        """Generate highlighted page images for PDF chunks (CPU-bound)."""
+        nonlocal chunk_images
+        if not is_pdf:
+            return
+
+        # Type narrowing: content_bytes is set for PDF files
+        assert content_bytes is not None
+
+        with trace_operation(
+            "vector_sync.generate_highlights",
+            attributes={
+                "vector_sync.chunk_count": len(chunks),
+                "vector_sync.pdf_size": len(content_bytes),
+            },
+        ):
+            import base64
+
+            from nextcloud_mcp_server.search.pdf_highlighter import PDFHighlighter
+
+            # Build chunk data for batch processing
+            # Format: (chunk_index, start_offset, end_offset, page_number, chunk_text)
+            chunk_data: list[tuple[int, int, int, int | None, str]] = [
+                (i, chunk.start_offset, chunk.end_offset, chunk.page_number, chunk.text)
+                for i, chunk in enumerate(chunks)
+                if chunk.page_number is not None
+            ]
+
+            # Get pre-computed page boundaries from document processor
+            page_boundaries = file_metadata.get("page_boundaries")
+            if not page_boundaries:
+                logger.warning("No page boundaries available, skipping highlighting")
+                return
+
+            logger.info(
+                f"Batch generating highlighted page images for {len(chunk_data)} PDF chunks"
+            )
+
+            # Run CPU-bound highlighting in thread pool
+            # Pass pre-computed page boundaries and full text to avoid re-processing the PDF
+            batch_results = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+                lambda: PDFHighlighter.highlight_chunks_batch(
+                    pdf_bytes=content_bytes,
+                    chunks=chunk_data,
+                    page_boundaries=page_boundaries,
+                    full_text=content,
+                    color="yellow",
+                    zoom=2.0,
+                )
+            )
+
+            # Convert results to storage format
+            for chunk_index, (
+                png_bytes,
+                actual_page_num,
+                highlight_count,
+            ) in batch_results.items():
+                image_base64 = base64.b64encode(png_bytes).decode("utf-8")
+                chunk_images[chunk_index] = {
+                    "image": image_base64,
+                    "page": actual_page_num,
+                    "highlights": highlight_count,
+                    "size": len(png_bytes),
+                }
+
+            logger.info(
+                f"Generated {len(chunk_images)}/{len(chunks)} highlighted page images "
+                f"(avg {sum(img['size'] for img in chunk_images.values()) // max(len(chunk_images), 1):,} bytes)"
+            )
+
+    # Run all embedding/highlighting operations in parallel
+    # - Dense embeddings: I/O bound (API call)
+    # - Sparse embeddings: CPU bound (local BM25)
+    # - Highlighting: CPU bound (PyMuPDF rendering, runs in thread pool)
+    with trace_operation(
+        "vector_sync.parallel_processing",
+        attributes={
+            "vector_sync.is_pdf": is_pdf,
+            "vector_sync.chunk_count": len(chunks),
+        },
+    ):
+        async with anyio.create_task_group() as tg:
+            tg.start_soon(generate_dense_embeddings)
+            tg.start_soon(generate_sparse_embeddings)
+            tg.start_soon(generate_highlights)
 
     # Prepare Qdrant points
     indexed_at = int(time.time())
@@ -416,6 +566,16 @@ async def _index_document(
                         if doc_task.doc_type == "file"
                         else {}
                     ),
+                    # Highlighted page image (PDF only)
+                    **(
+                        {
+                            "highlighted_page_image": chunk_images[i]["image"],
+                            "highlighted_page_number": chunk_images[i]["page"],
+                            "highlight_count": chunk_images[i]["highlights"],
+                        }
+                        if i in chunk_images
+                        else {}
+                    ),
                 },
             )
         )
@@ -434,12 +594,30 @@ async def _index_document(
             f"Failed to delete placeholder for {doc_task.doc_type}_{doc_task.doc_id}: {e}"
         )
 
-    # Upsert to Qdrant
-    await qdrant_client.upsert(
-        collection_name=settings.get_collection_name(),
-        points=points,
-        wait=True,
-    )
+    # Upsert to Qdrant in batches to avoid timeout with large payloads
+    # Each batch is limited to avoid WriteTimeout when sending large image payloads
+    BATCH_SIZE = 10  # ~2MB per batch with images
+    with trace_operation(
+        "vector_sync.qdrant_upsert",
+        attributes={
+            "vector_sync.point_count": len(points),
+            "vector_sync.collection": settings.get_collection_name(),
+            "vector_sync.images_count": len(chunk_images),
+            "vector_sync.batch_size": BATCH_SIZE,
+        },
+    ):
+        for batch_start in range(0, len(points), BATCH_SIZE):
+            batch_end = min(batch_start + BATCH_SIZE, len(points))
+            batch = points[batch_start:batch_end]
+            await qdrant_client.upsert(
+                collection_name=settings.get_collection_name(),
+                points=batch,
+                wait=True,
+            )
+            if batch_end < len(points):
+                logger.debug(
+                    f"Upserted batch {batch_start // BATCH_SIZE + 1}/{(len(points) + BATCH_SIZE - 1) // BATCH_SIZE}"
+                )
 
     logger.info(
         f"Indexed {doc_task.doc_type}_{doc_task.doc_id} for {doc_task.user_id} "