Update README

fix: Async/await patterns, PDF metadata, and vector visualization improvements

.github/workflows/bump-version.yml

@@ -15,7 +15,7 @@ jobs:
       packages: write
     steps:
       - name: Check out
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
         with:
           fetch-depth: 0
           token: "${{ secrets.PERSONAL_ACCESS_TOKEN }}"

.github/workflows/docker-build-publish.yml

@@ -12,7 +12,7 @@ jobs:
       packages: write
     steps:
       - name: Checkout repository
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
 
       - name: Docker meta
         id: meta

.github/workflows/helm-release.yml

@@ -14,7 +14,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
         with:
           fetch-depth: 0
 

.github/workflows/release.yml

@@ -18,9 +18,9 @@ jobs:
       contents: read
     steps:
       - name: Checkout
-        uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5
+        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6
       - name: Install uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
       - name: Install Python 3.11
         run: uv python install 3.11
       - name: Build

.github/workflows/test.yml

@@ -9,9 +9,9 @@ jobs:
   linting:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
+      - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
       - name: Check format
         run: |
           uv run --frozen ruff format --diff
@@ -27,7 +27,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
+      - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
         with:
           submodules: 'true'
 
@@ -56,7 +56,7 @@ jobs:
           up-flags: "--build"
 
       - name: Install the latest version of uv
-        uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
+        uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
 
       - name: Install Playwright dependencies
         run: |

CHANGELOG.md

@@ -1,3 +1,42 @@
+## v0.45.0 (2025-11-22)
+
+### Feat
+
+- Add context expansion to semantic search with chunk overlap removal
+- Use Ollama native batch API in embed_batch()
+- Implement Qdrant placeholder state management
+- Switch files to use numeric IDs with file_path resolution
+- Implement per-chunk vector visualization with context expansion
+
+### Fix
+
+- Use alpha_composite for proper RGBA highlight blending
+- Remove pymupdf.layout.activate() to fix page_chunks behavior
+- Centralize PDF processing and generate separate images per chunk
+- Set is_placeholder=False in processor to fix search filtering
+- Increase placeholder staleness threshold to 5x scan interval
+- Add placeholder staleness check to prevent duplicate processing
+- Use empty SparseVector instead of None for placeholders
+- Return empty array instead of null for query_coords when no results
+- Align PDF text extraction between indexing and context expansion
+- Update models and viz to use int-only doc_id
+- Reconstruct full content for notes to match indexed offsets
+- Add async/await, PDF metadata, and type safety fixes
+
+### Refactor
+
+- Simplify PDF text extraction with single to_markdown call
+
+### Perf
+
+- Optimize PDF processing with parallel extraction and single-render highlights
+
+## v0.44.1 (2025-11-21)
+
+### Fix
+
+- **deps**: update dependency mcp to >=1.22,<1.23
+
 ## v0.44.0 (2025-11-19)
 
 ### Feat

Dockerfile

@@ -1,12 +1,13 @@
 FROM docker.io/library/python:3.12-slim-trixie@sha256:2e683fc3e18a248aa23b8022f2a3474b072b04fb851efe9b49f6b516a8944939
 
-COPY --from=ghcr.io/astral-sh/uv:0.9.10@sha256:29bd45092ea8902c0bbb7f0a338f0494a382b1f4b18355df5be270ade679ff1d /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.9.11@sha256:5aa820129de0a600924f166aec9cb51613b15b68f1dcd2a02f31a500d2ede568 /uv /uvx /bin/
 
 # Install dependencies
 # 1. git (required for caldav dependency from git)
 # 2. sqlite for development with token db
 RUN apt update && apt install --no-install-recommends --no-install-suggests -y \
     git \
+    tesseract-ocr \
     sqlite3 && apt clean
 
 WORKDIR /app
@@ -17,5 +18,7 @@ RUN uv sync --locked --no-dev --no-editable --no-cache
 
 ENV PYTHONUNBUFFERED=1
 ENV VIRTUAL_ENV=/app/.venv
+ENV PATH=/app/.vnev/bin:$PATH
+ENV TESSDATA_PREFIX=/usr/share/tesseract-ocr/5/tessdata
 
 ENTRYPOINT ["/app/.venv/bin/nextcloud-mcp-server", "--host", "0.0.0.0"]

README.md

@@ -1,9 +1,11 @@
+```markdown
 <p align="center">
   <img src="astrolabe.svg" alt="Nextcloud MCP Server" width="128" height="128">
 </p>
 
 # Nextcloud MCP Server
 
+[![smithery badge](https://smithery.ai/badge/@cbcoutinho/nextcloud-mcp-server)](https://smithery.ai/server/@cbcoutinho/nextcloud-mcp-server)
 [![Docker Image](https://img.shields.io/badge/docker-ghcr.io/cbcoutinho/nextcloud--mcp--server-blue)](https://github.com/cbcoutinho/nextcloud-mcp-server/pkgs/container/nextcloud-mcp-server)
 
 **A production-ready MCP server that connects AI assistants to your Nextcloud instance.**
@@ -210,3 +212,4 @@ This project is licensed under the AGPL-3.0 License. See [LICENSE](./LICENSE) fo
 - [Model Context Protocol](https://github.com/modelcontextprotocol)
 - [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
 - [Nextcloud](https://nextcloud.com/)
+```

charts/nextcloud-mcp-server/Chart.lock

@@ -4,6 +4,6 @@ dependencies:
   version: 1.16.0
 - name: ollama
   repository: https://otwld.github.io/ollama-helm
-  version: 1.34.0
-digest: sha256:9dfb8d6e3d5488f669d4c37f3a766213b598ff3de2aead2c734789736c7835b4
-generated: "2025-11-17T17:08:48.055530019Z"
+  version: 1.35.0
+digest: sha256:da8db198b12ce0252df220fabb297cfe69186edb8e67952c52e05de778189b92
+generated: "2025-11-21T11:09:07.997781541Z"

charts/nextcloud-mcp-server/Chart.yaml

@@ -2,8 +2,8 @@ apiVersion: v2
 name: nextcloud-mcp-server
 description: A Helm chart for Nextcloud MCP Server - enables AI assistants to interact with Nextcloud
 type: application
-version: 0.44.0
-appVersion: "0.44.0"
+version: 0.45.0
+appVersion: "0.45.0"
 keywords:
   - nextcloud
   - mcp
@@ -31,6 +31,6 @@ dependencies:
     repository: https://qdrant.github.io/qdrant-helm
     condition: qdrant.networkMode.deploySubchart
   - name: ollama
-    version: "1.34.0"
+    version: "1.35.0"
     repository: https://otwld.github.io/ollama-helm
     condition: ollama.enabled

docker-compose.yml

@@ -17,11 +17,11 @@ services:
   # Note: Redis is an external service. You can find more information about the configuration here:
   # https://hub.docker.com/_/redis
   redis:
-    image: docker.io/library/redis:alpine@sha256:5013e94192ef18a5d8368179c7522e5300f9265cc339cadac76c7b93303a2752
+    image: docker.io/library/redis:alpine@sha256:6cbef353e480a8a6e7f10ec545f13d7d3fa85a212cdcc5ffaf5a1c818b9d3798
     restart: always
 
   app:
-    image: docker.io/library/nextcloud:32.0.1@sha256:d572839eeb693026d72a0c6aa48076df0bb8930797ea321e604936ef7189d06e
+    image: docker.io/library/nextcloud:32.0.2@sha256:ac08482d73ffd85d94069ba291bbd5fb39a70ff21502030a2e3e2d89a7246a48
     restart: always
     ports:
       - 0.0.0.0:8080:80

nextcloud_mcp_server/app.py

@@ -122,6 +122,26 @@ def initialize_document_processors():
         except Exception as e:
             logger.warning(f"Failed to register Tesseract processor: {e}")
 
+    # Register PyMuPDF processor (high priority, local, no API required)
+    if "pymupdf" in config["processors"]:
+        pymupdf_config = config["processors"]["pymupdf"]
+        try:
+            from nextcloud_mcp_server.document_processors.pymupdf import (
+                PyMuPDFProcessor,
+            )
+
+            processor = PyMuPDFProcessor(
+                extract_images=pymupdf_config.get("extract_images", True),
+                image_dir=pymupdf_config.get("image_dir"),
+            )
+            registry.register(processor, priority=15)  # Higher than unstructured
+            logger.info(
+                f"Registered PyMuPDF processor: extract_images={pymupdf_config.get('extract_images', True)}"
+            )
+            registered_count += 1
+        except Exception as e:
+            logger.warning(f"Failed to register PyMuPDF processor: {e}")
+
     # Register custom processor
     if "custom" in config["processors"]:
         custom_config = config["processors"]["custom"]

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;
+}

nextcloud_mcp_server/auth/static/vector-viz.js

@@ -217,7 +217,7 @@ function vizApp() {
         },
 
         async toggleChunk(result) {
-            const resultKey = `${result.doc_type}_${result.id}`;
+            const resultKey = `${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`;
 
             if (this.isChunkExpanded(resultKey)) {
                 delete this.expandedChunks[resultKey];

nextcloud_mcp_server/auth/templates/vector_viz.html

@@ -117,12 +117,13 @@
 
         <template x-if="!loading && results.length > 0">
             <div x-transition.opacity.duration.200ms>
-                <template x-for="result in results" :key="result.id">
+                <template x-for="result in results" :key="`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`">
                     <div style="padding: 12px; border-bottom: 1px solid #eee;">
                         <a :href="getNextcloudUrl(result)" target="_blank" style="font-weight: 500; color: #0066cc; text-decoration: none;">
                             <span x-text="result.title"></span>
                         </a>
-                        <div style="font-size: 14px; color: #666; margin-top: 4px;" x-text="result.excerpt"></div>
+                        <div style="font-size: 14px; color: #666; margin-top: 4px;"
+                             x-text="result.excerpt.length > 200 ? result.excerpt.substring(0, 200) + '...' : result.excerpt"></div>
                         <div style="font-size: 12px; color: #999; margin-top: 4px;">
                             Raw Score: <span x-text="result.original_score.toFixed(3)"></span>
                             (<span x-text="(result.score * 100).toFixed(0)"></span>% relative) |
@@ -134,22 +135,36 @@
                             <button
                                 class="chunk-toggle-btn"
                                 @click="toggleChunk(result)"
-                                x-text="isChunkExpanded(`${result.doc_type}_${result.id}`) ? 'Hide Chunk' : 'Show Chunk'"
+                                x-text="isChunkExpanded(`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`) ? 'Hide Chunk' : 'Show Chunk'"
                             ></button>
                         </template>
 
                         <!-- Chunk context (expanded inline) -->
-                        <template x-if="isChunkExpanded(`${result.doc_type}_${result.id}`)">
+                        <template x-if="isChunkExpanded(`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`)">
                             <div class="chunk-context" x-transition.opacity.duration.200ms>
-                                <template x-if="chunkLoading[`${result.doc_type}_${result.id}`]">
+                                <template x-if="chunkLoading[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]">
                                     <div style="color: #666; font-style: italic;">Loading chunk...</div>
                                 </template>
-                                <template x-if="!chunkLoading[`${result.doc_type}_${result.id}`]">
+                                <template x-if="!chunkLoading[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]">
                                     <div>
-                                        <template x-if="expandedChunks[`${result.doc_type}_${result.id}`]?.has_more_before">
+                                        <!-- Highlighted page image for PDFs -->
+                                        <template x-if="expandedChunks[`${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>
-                                        <span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.before_context"></span><span class="chunk-matched" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.chunk_text"></span><span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}`]?.after_context"></span><template x-if="expandedChunks[`${result.doc_type}_${result.id}`]?.has_more_after">
+                                        <span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.before_context"></span><span class="chunk-matched" x-text="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.chunk_text"></span><span class="chunk-text" x-text="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.after_context"></span><template x-if="expandedChunks[`${result.doc_type}_${result.id}_${result.chunk_start_offset || 0}`]?.has_more_after">
                                             <span class="chunk-ellipsis">...</span>
                                         </template>
                                     </div>

nextcloud_mcp_server/auth/userinfo_routes.py

@@ -18,6 +18,8 @@ from starlette.authentication import requires
 from starlette.requests import Request
 from starlette.responses import HTMLResponse, JSONResponse
 
+from nextcloud_mcp_server.client import NextcloudClient
+
 logger = logging.getLogger(__name__)
 
 # Setup Jinja2 environment for templates
@@ -25,14 +27,20 @@ _template_dir = Path(__file__).parent / "templates"
 _jinja_env = Environment(loader=FileSystemLoader(_template_dir))
 
 
-async def _get_authenticated_client_for_userinfo(request: Request) -> httpx.AsyncClient:
-    """Get an authenticated HTTP client for user info page operations.
+async def _get_authenticated_client_for_userinfo(request: Request) -> NextcloudClient:
+    """Get an authenticated Nextcloud client for user info page operations.
+
+    This is a shared helper for authenticated routes that need to access
+    Nextcloud APIs. It handles both BasicAuth and OAuth authentication modes.
 
     Args:
         request: Starlette request object
 
     Returns:
-        Authenticated httpx.AsyncClient
+        Authenticated NextcloudClient
+
+    Raises:
+        RuntimeError: If credentials/session not configured
     """
     oauth_ctx = getattr(request.app.state, "oauth_context", None)
 
@@ -45,11 +53,15 @@ async def _get_authenticated_client_for_userinfo(request: Request) -> httpx.Asyn
         if not all([nextcloud_host, username, password]):
             raise RuntimeError("BasicAuth credentials not configured")
 
-        assert nextcloud_host is not None  # Type narrowing for type checker
-        return httpx.AsyncClient(
+        from httpx import BasicAuth
+
+        assert nextcloud_host is not None
+        assert username is not None
+        assert password is not None
+        return NextcloudClient(
             base_url=nextcloud_host,
-            auth=(username, password),
-            timeout=30.0,
+            username=username,
+            auth=BasicAuth(username, password),
         )
 
     # OAuth mode - get token from session
@@ -64,15 +76,14 @@ async def _get_authenticated_client_for_userinfo(request: Request) -> httpx.Asyn
         raise RuntimeError("No access token found in session")
 
     access_token = token_data["access_token"]
+    username = token_data.get("username")
     nextcloud_host = oauth_ctx.get("config", {}).get("nextcloud_host", "")
 
-    if not nextcloud_host:
-        raise RuntimeError("Nextcloud host not configured")
+    if not nextcloud_host or not username:
+        raise RuntimeError("Nextcloud host or username not configured")
 
-    return httpx.AsyncClient(
-        base_url=nextcloud_host,
-        headers={"Authorization": f"Bearer {access_token}"},
-        timeout=30.0,
+    return NextcloudClient.from_token(
+        base_url=nextcloud_host, token=access_token, username=username
     )
 
 
@@ -423,10 +434,10 @@ async def user_info_html(request: Request) -> HTMLResponse:
     try:
         from nextcloud_mcp_server.auth.permissions import is_nextcloud_admin
 
-        # Get authenticated HTTP client
-        http_client = await _get_authenticated_client_for_userinfo(request)
-        is_admin = await is_nextcloud_admin(request, http_client)
-        await http_client.aclose()
+        # Get authenticated Nextcloud client
+        nc_client = await _get_authenticated_client_for_userinfo(request)
+        is_admin = await is_nextcloud_admin(request, nc_client._client)
+        await nc_client.close()
     except Exception as e:
         logger.warning(f"Failed to check admin status: {e}")
         # Default to not admin if check fails

nextcloud_mcp_server/auth/viz_routes.py

@@ -27,6 +27,7 @@ from nextcloud_mcp_server.search import (
     SemanticSearchAlgorithm,
 )
 from nextcloud_mcp_server.vector.pca import PCA
+from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
@@ -138,7 +139,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             _get_authenticated_client_for_userinfo,
         )
 
-        async with await _get_authenticated_client_for_userinfo(request) as http_client:  # noqa: F841
+        async with await _get_authenticated_client_for_userinfo(request) as nc_client:  # noqa: F841
             # Create search algorithm (no client needed - verification removed)
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
@@ -212,75 +213,81 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                     "success": True,
                     "results": [],
                     "coordinates_3d": [],
-                    "query_coords": None,
+                    "query_coords": [],
                     "message": "No results found",
                 }
             )
 
-        # Fetch vectors for matching results from Qdrant
+        # Fetch vectors for specific matching chunks from Qdrant
         vector_fetch_start = time.perf_counter()
         qdrant_client = await get_qdrant_client()
-        doc_ids = [r.id for r in search_results]
 
-        # Retrieve vectors for the matching documents
-        from qdrant_client.models import FieldCondition, Filter, MatchAny
+        # Build filters for each specific chunk
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
 
-        points_response = await qdrant_client.scroll(
-            collection_name=settings.get_collection_name(),
-            scroll_filter=Filter(
-                must=[
+        chunk_vectors_map = {}  # Map (doc_id, chunk_start, chunk_end) -> vector
+
+        # Fetch vectors in batches by filtering on chunk-specific fields
+        for result in search_results:
+            chunk_start = result.chunk_start_offset
+            chunk_end = result.chunk_end_offset
+
+            # Build filter for this specific chunk
+            must_conditions = [
+                get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
+                FieldCondition(
+                    key="doc_id",
+                    match=MatchValue(value=result.id),
+                ),
+                FieldCondition(
+                    key="user_id",
+                    match=MatchValue(value=username),
+                ),
+            ]
+
+            # Add chunk position filters if available
+            if chunk_start is not None:
+                must_conditions.append(
                     FieldCondition(
-                        key="doc_id",
-                        match=MatchAny(any=[str(doc_id) for doc_id in doc_ids]),
-                    ),
+                        key="chunk_start_offset",
+                        match=MatchValue(value=chunk_start),
+                    )
+                )
+            if chunk_end is not None:
+                must_conditions.append(
                     FieldCondition(
-                        key="user_id",
-                        match={"value": username},
-                    ),
-                ]
-            ),
-            limit=len(doc_ids) * 2,  # Account for multiple chunks per doc
-            with_vectors=["dense"],  # Only fetch dense vectors for visualization
-            with_payload=["doc_id"],  # Need doc_id to map vectors to results
-        )
+                        key="chunk_end_offset",
+                        match=MatchValue(value=chunk_end),
+                    )
+                )
 
-        points = points_response[0]
-
-        if not points:
-            return JSONResponse(
-                {
-                    "success": True,
-                    "results": [],
-                    "coordinates_2d": [],
-                    "message": "No vectors found for results",
-                }
+            # Fetch this specific chunk vector
+            points_response = await qdrant_client.scroll(
+                collection_name=settings.get_collection_name(),
+                scroll_filter=Filter(must=must_conditions),
+                limit=1,  # Only need the first match
+                with_vectors=["dense"],
+                with_payload=False,
             )
 
-        # Extract dense vectors and group by document
-        def extract_dense_vector(point):
-            if point.vector is None:
-                return None
-            # If named vectors (dict), extract "dense"
-            if isinstance(point.vector, dict):
-                return point.vector.get("dense")
-            # If unnamed vector (array), use directly
-            return point.vector
+            points = points_response[0]
+            if points:
+                # Extract dense vector
+                point = points[0]
+                if point.vector is not None:
+                    # If named vectors (dict), extract "dense"
+                    if isinstance(point.vector, dict):
+                        vector = point.vector.get("dense")
+                    else:
+                        vector = point.vector
 
-        # Group chunk vectors by doc_id
-        from collections import defaultdict
-
-        doc_chunks = defaultdict(list)
-        for point in points:
-            if point.payload:
-                doc_id = int(point.payload.get("doc_id", 0))
-                vector = extract_dense_vector(point)
-                if vector is not None:
-                    doc_chunks[doc_id].append(vector)
+                    chunk_key = (result.id, chunk_start, chunk_end)
+                    chunk_vectors_map[chunk_key] = vector
 
         vector_fetch_duration = time.perf_counter() - vector_fetch_start
 
-        if len(doc_chunks) < 2:
-            # Not enough documents for PCA
+        if len(chunk_vectors_map) < 2:
+            # Not enough chunks for PCA
             return JSONResponse(
                 {
                     "success": True,
@@ -296,15 +303,15 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                     ],
                     "coordinates_3d": [[0, 0, 0]] * len(search_results),
                     "query_coords": [0, 0, 0],
-                    "message": "Not enough documents for PCA",
+                    "message": "Not enough chunks for PCA",
                 }
             )
 
         # Detect embedding dimension from first available vector
         embedding_dim = None
-        for chunks in doc_chunks.values():
-            if chunks:
-                embedding_dim = len(chunks[0])
+        for vector in chunk_vectors_map.values():
+            if vector is not None:
+                embedding_dim = len(vector)
                 break
 
         if embedding_dim is None:
@@ -318,23 +325,21 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         logger.info(f"Detected embedding dimension: {embedding_dim}")
 
-        # Average chunk vectors per document to create document-level embeddings
-        # Maintain order of search_results for coordinate mapping
-        doc_vectors = []
+        # Build chunk vectors array in search_results order (1:1 mapping)
+        chunk_vectors = []
         for result in search_results:
-            if result.id in doc_chunks:
-                # Average all chunk embeddings for this document
-                chunk_vectors = np.array(doc_chunks[result.id])
-                avg_vector = np.mean(chunk_vectors, axis=0)
-                doc_vectors.append(avg_vector)
-                logger.debug(f"Doc {result.id}: averaged {len(chunk_vectors)} chunks")
+            chunk_key = (result.id, result.chunk_start_offset, result.chunk_end_offset)
+            if chunk_key in chunk_vectors_map:
+                chunk_vectors.append(chunk_vectors_map[chunk_key])
             else:
-                # Document not found in vectors (shouldn't happen)
-                logger.warning(f"Doc {result.id} not found in fetched vectors")
-                # Use zero vector as fallback with detected dimension
-                doc_vectors.append(np.zeros(embedding_dim))
+                # Chunk not found in vectors (shouldn't happen)
+                logger.warning(
+                    f"Chunk {chunk_key} not found in fetched vectors, using zero vector"
+                )
+                # Use zero vector as fallback
+                chunk_vectors.append(np.zeros(embedding_dim))
 
-        doc_vectors = np.array(doc_vectors)
+        chunk_vectors = np.array(chunk_vectors)
 
         # Generate query embedding for visualization
         query_embed_start = time.perf_counter()
@@ -346,9 +351,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
         logger.info(f"Generated query embedding (dimension={len(query_embedding)})")
 
-        # Combine query vector with document vectors for PCA
+        # Combine query vector with chunk vectors for PCA
         # Query will be the last point in the array
-        all_vectors = np.vstack([doc_vectors, np.array([query_embedding])])
+        all_vectors = np.vstack([chunk_vectors, np.array([query_embedding])])
 
         # Normalize vectors to unit length (L2 normalization)
         # This is critical because Qdrant uses COSINE distance, which only measures
@@ -394,17 +399,12 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             # Replace NaN with 0 to allow JSON serialization
             coords_3d = np.nan_to_num(coords_3d, nan=0.0)
 
-        # Split query coords from document coords
+        # Split query coords from chunk coords
         # Round to 2 decimal places for cleaner display
         query_coords_3d = [
             round(float(x), 2) for x in coords_3d[-1]
         ]  # Last point is query
-        doc_coords_3d = coords_3d[:-1]  # All but last are documents
-
-        total_chunks = sum(len(chunks) for chunks in doc_chunks.values())
-        avg_chunks_per_doc = (
-            total_chunks / len(doc_vectors) if doc_vectors.size > 0 else 0
-        )
+        chunk_coords_3d = coords_3d[:-1]  # All but last are chunks
 
         logger.info(
             f"PCA explained variance: PC1={pca.explained_variance_ratio_[0]:.3f}, "
@@ -412,13 +412,14 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             f"PC3={pca.explained_variance_ratio_[2]:.3f}"
         )
         logger.info(
-            f"Embedding stats: documents={len(doc_vectors)}, "
-            f"total_chunks={total_chunks}, avg_chunks_per_doc={avg_chunks_per_doc:.1f}, "
-            f"query_dim={len(query_embedding)}, doc_vector_dim={doc_vectors.shape[1] if doc_vectors.size > 0 else 0}"
+            f"Embedding stats: chunks={len(chunk_vectors)}, "
+            f"query_dim={len(query_embedding)}, chunk_vector_dim={chunk_vectors.shape[1] if chunk_vectors.size > 0 else 0}"
         )
 
         # Coordinates already match search_results order (1:1 mapping)
-        result_coords = [[round(float(x), 2) for x in coord] for coord in doc_coords_3d]
+        result_coords = [
+            [round(float(x), 2) for x in coord] for coord in chunk_coords_3d
+        ]
 
         # Build response
         response_results = [
@@ -447,7 +448,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             f"vector_fetch={vector_fetch_duration * 1000:.1f}ms ({vector_fetch_duration / total_duration * 100:.1f}%), "
             f"query_embed={query_embed_duration * 1000:.1f}ms ({query_embed_duration / total_duration * 100:.1f}%), "
             f"pca={pca_duration * 1000:.1f}ms ({pca_duration / total_duration * 100:.1f}%), "
-            f"results={len(search_results)}, doc_vectors={len(doc_vectors)}"
+            f"results={len(search_results)}, chunk_vectors={len(chunk_vectors)}"
         )
 
         return JSONResponse(
@@ -468,7 +469,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                     "query_embed_ms": round(query_embed_duration * 1000, 2),
                     "pca_ms": round(pca_duration * 1000, 2),
                     "num_results": len(search_results),
-                    "num_doc_vectors": len(doc_vectors),
+                    "num_chunk_vectors": len(chunk_vectors),
                 },
             }
         )
@@ -517,77 +518,118 @@ async def chunk_context_endpoint(request: Request) -> JSONResponse:
                 status_code=400,
             )
 
+        # Type assertions - we validated these above
+        assert doc_type is not None
+        assert doc_id is not None
+        assert start_str is not None
+        assert end_str is not None
+
         start = int(start_str)
         end = int(end_str)
+        # Convert doc_id to int (all document types use int IDs)
+        doc_id_int = int(doc_id)
 
-        # Currently only support notes
-        if doc_type != "note":
-            return JSONResponse(
-                {"success": False, "error": f"Unsupported doc_type: {doc_type}"},
-                status_code=400,
-            )
-
-        # Get authenticated HTTP client and fetch note
+        # Get authenticated Nextcloud client
         from nextcloud_mcp_server.auth.userinfo_routes import (
             _get_authenticated_client_for_userinfo,
         )
-        from nextcloud_mcp_server.client.notes import NotesClient
+        from nextcloud_mcp_server.search.context import get_chunk_with_context
 
-        # Get username from request auth
-        username = (
-            request.user.display_name
-            if hasattr(request.user, "display_name")
-            else "unknown"
-        )
+        # Use context expansion module to fetch chunk with surrounding context
+        async with await _get_authenticated_client_for_userinfo(request) as nc_client:
+            chunk_context = await get_chunk_with_context(
+                nc_client=nc_client,
+                user_id=request.user.display_name,  # User ID from auth
+                doc_id=doc_id_int,
+                doc_type=doc_type,
+                chunk_start=start,
+                chunk_end=end,
+                context_chars=context_chars,
+            )
 
-        # Create notes client with authenticated HTTP client
-        http_client = await _get_authenticated_client_for_userinfo(request)
-        notes_client = NotesClient(http_client, username)
-
-        # Fetch full note content
-        note = await notes_client.get_note(int(doc_id))
-        full_content = f"{note['title']}\n\n{note['content']}"
-
-        # Validate offsets
-        if start < 0 or end > len(full_content) or start >= end:
+        # Check if context expansion succeeded
+        if chunk_context is None:
             return JSONResponse(
                 {
                     "success": False,
-                    "error": f"Invalid offsets: start={start}, end={end}, content_length={len(full_content)}",
+                    "error": f"Failed to fetch chunk context for {doc_type} {doc_id}",
                 },
-                status_code=400,
+                status_code=404,
             )
 
-        # Extract chunk
-        chunk_text = full_content[start:end]
-
-        # Extract context before and after
-        before_start = max(0, start - context_chars)
-        before_context = full_content[before_start:start]
-
-        after_end = min(len(full_content), end + context_chars)
-        after_context = full_content[end:after_end]
-
-        # Determine if there's more content
-        has_more_before = before_start > 0
-        has_more_after = after_end < len(full_content)
-
         logger.info(
             f"Fetched chunk context for {doc_type}_{doc_id}: "
-            f"chunk_len={len(chunk_text)}, before_len={len(before_context)}, "
-            f"after_len={len(after_context)}"
+            f"chunk_len={len(chunk_context.chunk_text)}, "
+            f"before_len={len(chunk_context.before_context)}, "
+            f"after_len={len(chunk_context.after_context)}"
         )
 
-        return JSONResponse(
-            {
-                "success": True,
-                "chunk_text": chunk_text,
-                "before_context": before_context,
-                "after_context": after_context,
-                "has_more_before": has_more_before,
-                "has_more_after": has_more_after,
-            }
-        )
+        # 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
+        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}")

nextcloud_mcp_server/client/__init__.py

@@ -130,10 +130,75 @@ class NextcloudClient:
         all_notes = self.notes.get_all_notes()
         return await self._notes_search.search_notes(all_notes, query)
 
+    async def find_files_by_tag(
+        self, tag_name: str, mime_type_filter: str | None = None
+    ) -> list[dict]:
+        """Find files by system tag name, optionally filtered by MIME type.
+
+        This method coordinates tag lookup and file retrieval via WebDAV:
+        1. Look up the tag ID by name
+        2. Get all files with that tag (via REPORT with full metadata)
+        3. Optionally filter by MIME type
+
+        Args:
+            tag_name: Name of the system tag to search for (e.g., "vector-index")
+            mime_type_filter: Optional MIME type filter (e.g., "application/pdf")
+
+        Returns:
+            List of file dictionaries with WebDAV properties (path, size, content_type, etc.)
+
+        Raises:
+            RuntimeError: If tag lookup or file query fails
+
+        Examples:
+            # Find all files with "vector-index" tag
+            files = await nc_client.find_files_by_tag("vector-index")
+
+            # Find only PDFs with the tag
+            pdfs = await nc_client.find_files_by_tag("vector-index", "application/pdf")
+        """
+        # Look up tag by name using WebDAV
+        tag = await self.webdav.get_tag_by_name(tag_name)
+        if not tag:
+            logger.debug(f"Tag '{tag_name}' not found, returning empty list")
+            return []
+
+        # Get files with this tag (returns full file info from REPORT)
+        files = await self.webdav.get_files_by_tag(tag["id"])
+        if not files:
+            logger.debug(f"No files found with tag '{tag_name}'")
+            return []
+
+        logger.debug(f"Found {len(files)} files with tag '{tag_name}'")
+
+        # Apply MIME type filter if specified
+        if mime_type_filter:
+            filtered_files = [
+                f
+                for f in files
+                if f.get("content_type", "").startswith(mime_type_filter)
+            ]
+            logger.info(
+                f"Returning {len(filtered_files)} files with tag '{tag_name}' (filtered by {mime_type_filter})"
+            )
+            return filtered_files
+
+        logger.info(f"Returning {len(files)} files with tag '{tag_name}'")
+        return files
+
     def _get_webdav_base_path(self) -> str:
         """Helper to get the base WebDAV path for the authenticated user."""
         return f"/remote.php/dav/files/{self.username}"
 
+    async def __aenter__(self):
+        """Async context manager entry."""
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit - closes all clients."""
+        await self.close()
+        return False  # Don't suppress exceptions
+
     async def close(self):
         """Close the HTTP client and CalDAV client."""
         await self._client.aclose()

nextcloud_mcp_server/client/webdav.py

@@ -821,6 +821,20 @@ class WebDAVClient(BaseNextcloudClient):
                     item["file_id"] = int(value) if value else None
                 elif tag == "favorite":
                     item["is_favorite"] = value == "1"
+                elif tag == "tags":
+                    # Tags can be comma-separated or have multiple child elements
+                    if value:
+                        # Handle comma-separated tags
+                        item["tags"] = [
+                            t.strip() for t in value.split(",") if t.strip()
+                        ]
+                    else:
+                        # Check for child tag elements (alternative format)
+                        tag_elements = child.findall(".//{http://owncloud.org/ns}tag")
+                        if tag_elements:
+                            item["tags"] = [t.text for t in tag_elements if t.text]
+                        else:
+                            item["tags"] = []
                 elif tag == "permissions":
                     item["permissions"] = value
                 elif tag == "size":
@@ -948,3 +962,336 @@ class WebDAVClient(BaseNextcloudClient):
             properties=properties,
             limit=limit,
         )
+
+    async def find_by_tag(
+        self, tag_name: str, scope: str = "", limit: Optional[int] = None
+    ) -> List[Dict[str, Any]]:
+        """Find files by tag name.
+
+        DEPRECATED: Use NextcloudClient.find_files_by_tag() instead, which uses
+        the proper OCS Tags API rather than WebDAV SEARCH.
+
+        Args:
+            tag_name: Tag to filter by (e.g., "vector-index")
+            scope: Directory path to search in (empty string for user root)
+            limit: Maximum number of results to return
+
+        Returns:
+            List of files/directories with the specified tag
+
+        Examples:
+            # Find all files tagged with "vector-index"
+            results = await find_by_tag("vector-index")
+
+            # Find tagged files in a specific folder
+            results = await find_by_tag("vector-index", scope="Documents")
+        """
+        # Use LIKE for tag matching since tags can be comma-separated
+        where_conditions = f"""
+            <d:like>
+                <d:prop>
+                    <oc:tags/>
+                </d:prop>
+                <d:literal>%{tag_name}%</d:literal>
+            </d:like>
+        """
+
+        # Request tag property along with standard properties
+        properties = [
+            "displayname",
+            "getcontentlength",
+            "getcontenttype",
+            "getlastmodified",
+            "resourcetype",
+            "getetag",
+            "fileid",
+            "tags",
+        ]
+
+        return await self.search_files(
+            scope=scope,
+            where_conditions=where_conditions,
+            properties=properties,
+            limit=limit,
+        )
+
+    async def _get_file_info_by_id(self, file_id: int) -> Dict[str, Any]:
+        """Get file information by Nextcloud file ID using WebDAV.
+
+        Args:
+            file_id: Nextcloud internal file ID
+
+        Returns:
+            File information dictionary with path, size, content_type, etc.
+
+        Raises:
+            HTTPStatusError: If file not found or request fails
+        """
+        # Nextcloud allows accessing files by ID via special meta endpoint
+        meta_path = f"/remote.php/dav/meta/{file_id}/"
+
+        propfind_body = """<?xml version="1.0"?>
+        <d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+            <d:prop>
+                <d:displayname/>
+                <d:getcontentlength/>
+                <d:getcontenttype/>
+                <d:getlastmodified/>
+                <d:resourcetype/>
+                <d:getetag/>
+                <oc:fileid/>
+            </d:prop>
+        </d:propfind>"""
+
+        headers = {"Depth": "0", "Content-Type": "text/xml", "OCS-APIRequest": "true"}
+
+        response = await self._make_request(
+            "PROPFIND", meta_path, content=propfind_body, headers=headers
+        )
+        response.raise_for_status()
+
+        # Parse the XML response
+        root = ET.fromstring(response.content)
+        responses = root.findall(".//{DAV:}response")
+
+        if not responses:
+            raise RuntimeError(f"File ID {file_id} not found")
+
+        response_elem = responses[0]
+        href = response_elem.find(".//{DAV:}href")
+        if href is None:
+            raise RuntimeError(f"No href in response for file ID {file_id}")
+
+        propstat = response_elem.find(".//{DAV:}propstat")
+        if propstat is None:
+            raise RuntimeError(f"No propstat for file ID {file_id}")
+
+        prop = propstat.find(".//{DAV:}prop")
+        if prop is None:
+            raise RuntimeError(f"No prop for file ID {file_id}")
+
+        # Extract file path from displayname or construct from file ID
+        displayname_elem = prop.find(".//{DAV:}displayname")
+        name = (
+            displayname_elem.text if displayname_elem is not None else f"file_{file_id}"
+        )
+
+        # Get file properties
+        size_elem = prop.find(".//{DAV:}getcontentlength")
+        size = int(size_elem.text) if size_elem is not None and size_elem.text else 0
+
+        content_type_elem = prop.find(".//{DAV:}getcontenttype")
+        content_type = content_type_elem.text if content_type_elem is not None else None
+
+        modified_elem = prop.find(".//{DAV:}getlastmodified")
+        modified = modified_elem.text if modified_elem is not None else None
+
+        etag_elem = prop.find(".//{DAV:}getetag")
+        etag = (
+            etag_elem.text.strip('"')
+            if etag_elem is not None and etag_elem.text
+            else None
+        )
+
+        # Check if it's a directory
+        resourcetype = prop.find(".//{DAV:}resourcetype")
+        is_directory = (
+            resourcetype is not None
+            and resourcetype.find(".//{DAV:}collection") is not None
+        )
+
+        # Try to get actual file path - meta endpoint doesn't give us the real path
+        # so we'll construct a reasonable path from the name
+        # The calling code in NextcloudClient will have the context to determine the actual path
+        file_info = {
+            "name": name,
+            "path": f"/{name}",  # Placeholder - caller should use WebDAV to get real path if needed
+            "size": size,
+            "content_type": content_type,
+            "last_modified": modified,
+            "etag": etag,
+            "is_directory": is_directory,
+            "file_id": file_id,
+        }
+
+        logger.debug(f"Retrieved file info for ID {file_id}: {name}")
+        return file_info
+
+    async def get_tag_by_name(self, tag_name: str) -> dict[str, Any] | None:
+        """Get a system tag by its name via WebDAV.
+
+        Args:
+            tag_name: Name of the tag to find (case-sensitive)
+
+        Returns:
+            Tag dictionary if found, None otherwise
+        """
+        # Use WebDAV PROPFIND to list all systemtags
+        propfind_body = """<?xml version="1.0"?>
+<d:propfind xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+  <d:prop>
+    <oc:id/>
+    <oc:display-name/>
+    <oc:user-visible/>
+    <oc:user-assignable/>
+  </d:prop>
+</d:propfind>"""
+
+        response = await self._client.request(
+            "PROPFIND",
+            "/remote.php/dav/systemtags/",
+            headers={"Depth": "1"},
+            content=propfind_body,
+        )
+        response.raise_for_status()
+
+        # Parse XML response
+        root = ET.fromstring(response.content)
+        ns = {
+            "d": "DAV:",
+            "oc": "http://owncloud.org/ns",
+        }
+
+        for response_elem in root.findall("d:response", ns):
+            href = response_elem.find("d:href", ns)
+            if href is None or href.text == "/remote.php/dav/systemtags/":
+                # Skip the collection itself
+                continue
+
+            propstat = response_elem.find("d:propstat", ns)
+            if propstat is None:
+                continue
+
+            prop = propstat.find("d:prop", ns)
+            if prop is None:
+                continue
+
+            # Extract tag properties
+            tag_id_elem = prop.find("oc:id", ns)
+            display_name_elem = prop.find("oc:display-name", ns)
+            user_visible_elem = prop.find("oc:user-visible", ns)
+            user_assignable_elem = prop.find("oc:user-assignable", ns)
+
+            if display_name_elem is not None and display_name_elem.text == tag_name:
+                tag_info = {
+                    "id": int(tag_id_elem.text) if tag_id_elem is not None else None,
+                    "name": display_name_elem.text,
+                    "userVisible": user_visible_elem.text.lower() == "true"
+                    if user_visible_elem is not None
+                    else True,
+                    "userAssignable": user_assignable_elem.text.lower() == "true"
+                    if user_assignable_elem is not None
+                    else True,
+                }
+                logger.debug(f"Found tag '{tag_name}' with ID {tag_info['id']}")
+                return tag_info
+
+        logger.debug(f"Tag '{tag_name}' not found")
+        return None
+
+    async def get_files_by_tag(self, tag_id: int) -> list[dict[str, Any]]:
+        """Get all files tagged with a specific system tag via WebDAV REPORT.
+
+        Args:
+            tag_id: Numeric ID of the tag
+
+        Returns:
+            List of file info dictionaries with path, size, content_type, etc.
+        """
+        # Use WebDAV REPORT method with systemtag filter, requesting all properties
+        report_body = f"""<?xml version="1.0"?>
+<oc:filter-files xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns" xmlns:nc="http://nextcloud.org/ns">
+  <d:prop>
+    <oc:fileid/>
+    <d:displayname/>
+    <d:getcontentlength/>
+    <d:getcontenttype/>
+    <d:getlastmodified/>
+    <d:getetag/>
+  </d:prop>
+  <oc:filter-rules>
+    <oc:systemtag>{tag_id}</oc:systemtag>
+  </oc:filter-rules>
+</oc:filter-files>"""
+
+        response = await self._client.request(
+            "REPORT",
+            f"{self._get_webdav_base_path()}/",
+            content=report_body,
+        )
+        response.raise_for_status()
+
+        # Parse XML response
+        root = ET.fromstring(response.content)
+        ns = {
+            "d": "DAV:",
+            "oc": "http://owncloud.org/ns",
+        }
+
+        files = []
+        for response_elem in root.findall("d:response", ns):
+            # Extract href (file path)
+            href_elem = response_elem.find("d:href", ns)
+            if href_elem is None or not href_elem.text:
+                continue
+
+            propstat = response_elem.find("d:propstat", ns)
+            if propstat is None:
+                continue
+
+            prop = propstat.find("d:prop", ns)
+            if prop is None:
+                continue
+
+            # Extract all properties
+            fileid_elem = prop.find("oc:fileid", ns)
+            displayname_elem = prop.find("d:displayname", ns)
+            contentlength_elem = prop.find("d:getcontentlength", ns)
+            contenttype_elem = prop.find("d:getcontenttype", ns)
+            lastmodified_elem = prop.find("d:getlastmodified", ns)
+            etag_elem = prop.find("d:getetag", ns)
+
+            if fileid_elem is None or not fileid_elem.text:
+                continue
+
+            # Decode href path and extract the file path
+            from urllib.parse import unquote
+
+            href_path = unquote(href_elem.text)
+            # Remove WebDAV prefix to get user-relative path
+            webdav_prefix = f"/remote.php/dav/files/{self.username}/"
+            file_path = href_path.replace(webdav_prefix, "/")
+
+            # Parse last modified timestamp
+            last_modified_timestamp = None
+            if lastmodified_elem is not None and lastmodified_elem.text:
+                from email.utils import parsedate_to_datetime
+
+                try:
+                    dt = parsedate_to_datetime(lastmodified_elem.text)
+                    last_modified_timestamp = int(dt.timestamp())
+                except Exception:
+                    pass
+
+            file_info = {
+                "id": int(fileid_elem.text),
+                "path": file_path,
+                "name": displayname_elem.text
+                if displayname_elem is not None
+                else file_path.split("/")[-1],
+                "size": int(contentlength_elem.text)
+                if contentlength_elem is not None and contentlength_elem.text
+                else 0,
+                "content_type": contenttype_elem.text
+                if contenttype_elem is not None
+                else "",
+                "last_modified": lastmodified_elem.text
+                if lastmodified_elem is not None
+                else None,
+                "last_modified_timestamp": last_modified_timestamp,
+                "etag": etag_elem.text if etag_elem is not None else None,
+            }
+            files.append(file_info)
+
+        logger.debug(f"Found {len(files)} files with tag ID {tag_id}")
+        return files

nextcloud_mcp_server/config.py

@@ -102,6 +102,14 @@ def get_document_processor_config() -> dict[str, Any]:
             "lang": os.getenv("TESSERACT_LANG", "eng"),
         }
 
+    # PyMuPDF configuration (local PDF processing)
+    if os.getenv("ENABLE_PYMUPDF", "true").lower() == "true":  # Enabled by default
+        config["processors"]["pymupdf"] = {
+            "extract_images": os.getenv("PYMUPDF_EXTRACT_IMAGES", "true").lower()
+            == "true",
+            "image_dir": os.getenv("PYMUPDF_IMAGE_DIR"),  # None = use temp directory
+        }
+
     # Custom processor (via HTTP API)
     if os.getenv("ENABLE_CUSTOM_PROCESSOR", "false").lower() == "true":
         custom_url = os.getenv("CUSTOM_PROCESSOR_URL")

nextcloud_mcp_server/document_processors/__init__.py

@@ -1,12 +1,18 @@
 """Document processing plugins for extracting text from various file formats."""
 
 from .base import DocumentProcessor, ProcessingResult, ProcessorError
+from .pymupdf import PyMuPDFProcessor
 from .registry import ProcessorRegistry, get_registry
 
+# Register processors at module initialization
+_registry = get_registry()
+_registry.register(PyMuPDFProcessor(), priority=10)
+
 __all__ = [
     "DocumentProcessor",
     "ProcessingResult",
     "ProcessorError",
     "ProcessorRegistry",
     "get_registry",
+    "PyMuPDFProcessor",
 ]

nextcloud_mcp_server/document_processors/pymupdf.py

@@ -0,0 +1,253 @@
+"""Document processor using PyMuPDF (fitz) library."""
+
+import logging
+import pathlib
+import tempfile
+from collections.abc import Awaitable, Callable
+from typing import Any, Optional
+
+# NOTE: Do NOT call pymupdf.layout.activate() here!
+# It changes the behavior of pymupdf4llm.to_markdown() when page_chunks=True,
+# causing it to return a string instead of a list[dict].
+# See: https://github.com/pymupdf/pymupdf4llm/issues/323
+import pymupdf
+import pymupdf4llm
+
+from .base import DocumentProcessor, ProcessingResult, ProcessorError
+
+logger = logging.getLogger(__name__)
+
+
+class PyMuPDFProcessor(DocumentProcessor):
+    """Document processor using PyMuPDF library for PDF processing.
+
+    PyMuPDF (fitz) is a fast, local PDF processing library that extracts text,
+    metadata, and images without requiring external API calls.
+
+    Features:
+    - Fast text extraction with layout preservation
+    - PDF metadata extraction (title, author, creation date, page count)
+    - Image extraction for future multimodal support
+    - Page number tracking for precise citations
+    """
+
+    SUPPORTED_TYPES = {
+        "application/pdf",
+    }
+
+    def __init__(
+        self,
+        extract_images: bool = True,
+        image_dir: Optional[str | pathlib.Path] = None,
+    ):
+        """Initialize PyMuPDF processor.
+
+        Args:
+            extract_images: Whether to extract embedded images from PDFs
+            image_dir: Directory to store extracted images (defaults to temp directory)
+        """
+        self.extract_images = extract_images
+
+        if image_dir is None:
+            self.image_dir = pathlib.Path(tempfile.gettempdir()) / "pdf-images"
+        else:
+            self.image_dir = pathlib.Path(image_dir)
+
+        # Create image directory if it doesn't exist
+        if self.extract_images:
+            self.image_dir.mkdir(exist_ok=True, parents=True)
+            logger.info(
+                f"Initialized PyMuPDFProcessor with image extraction to {self.image_dir}"
+            )
+        else:
+            logger.info("Initialized PyMuPDFProcessor without image extraction")
+
+    @property
+    def name(self) -> str:
+        return "pymupdf"
+
+    @property
+    def supported_mime_types(self) -> set[str]:
+        return self.SUPPORTED_TYPES
+
+    async def process(
+        self,
+        content: bytes,
+        content_type: str,
+        filename: Optional[str] = None,
+        options: Optional[dict[str, Any]] = None,
+        progress_callback: Optional[
+            Callable[[float, Optional[float], Optional[str]], Awaitable[None]]
+        ] = None,
+    ) -> ProcessingResult:
+        """Process a PDF document and extract text, metadata, and images.
+
+        Args:
+            content: PDF document bytes
+            content_type: MIME type (should be application/pdf)
+            filename: Optional filename for better error messages
+            options: Processing options (currently unused)
+            progress_callback: Optional callback for progress updates
+
+        Returns:
+            ProcessingResult with extracted text and metadata
+
+        Raises:
+            ProcessorError: If PDF processing fails
+        """
+        import anyio
+
+        try:
+            if progress_callback:
+                await progress_callback(0, 100, "Opening PDF document")
+
+            # Open document and extract metadata in thread
+            doc = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+                lambda: pymupdf.open("pdf", content)
+            )
+
+            metadata = self._extract_metadata(doc, filename)
+            metadata["file_size"] = len(content)
+            page_count = doc.page_count
+
+            if progress_callback:
+                await progress_callback(10, 100, f"Extracting {page_count} pages")
+
+            # Prepare image directory if needed
+            pdf_image_dir = None
+            if self.extract_images:
+                pdf_id = filename.replace("/", "_") if filename else "unknown"
+                pdf_image_dir = self.image_dir / pdf_id
+                pdf_image_dir.mkdir(exist_ok=True, parents=True)
+
+            # Extract all pages in a single call with page_chunks=True
+            def do_extract() -> list[dict[str, Any]]:
+                # When page_chunks=True, to_markdown returns list[dict] not str
+                return pymupdf4llm.to_markdown(  # type: ignore[return-value]
+                    doc,
+                    write_images=self.extract_images,
+                    image_path=pdf_image_dir if self.extract_images else None,
+                    page_chunks=True,
+                )
+
+            page_chunks: list[dict[str, Any]] = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+                do_extract
+            )
+
+            if progress_callback:
+                await progress_callback(90, 100, "Building result")
+
+            # Extract page texts and build boundaries from chunks
+            page_texts: list[str] = []
+            page_boundaries: list[dict[str, Any]] = []
+            current_offset = 0
+            for chunk in page_chunks:
+                text = chunk.get("text", "")
+                page_num = chunk.get("metadata", {}).get("page", len(page_texts) + 1)
+                page_texts.append(text)
+                page_boundaries.append(
+                    {
+                        "page": page_num,
+                        "start_offset": current_offset,
+                        "end_offset": current_offset + len(text),
+                    }
+                )
+                current_offset += len(text)
+
+            # Collect image paths
+            image_paths = []
+            if pdf_image_dir and pdf_image_dir.exists():
+                image_paths = [str(p) for p in pdf_image_dir.glob("*")]
+
+            # Build final text and metadata
+            md_text = "".join(page_texts)
+            metadata["has_images"] = len(image_paths) > 0
+            if image_paths:
+                metadata["image_count"] = len(image_paths)
+                metadata["image_paths"] = image_paths
+            metadata["page_boundaries"] = page_boundaries
+
+            # Close document
+            doc.close()
+
+            if progress_callback:
+                await progress_callback(100, 100, "Processing complete")
+
+            logger.info(
+                f"Successfully processed PDF {filename or '<bytes>'}: "
+                f"{metadata['page_count']} pages, {len(md_text)} chars, "
+                f"{metadata.get('image_count', 0)} images"
+            )
+
+            return ProcessingResult(
+                text=md_text,
+                metadata=metadata,
+                processor=self.name,
+                success=True,
+            )
+
+        except Exception as e:
+            error_msg = f"Failed to process PDF {filename or '<bytes>'}: {e}"
+            logger.error(error_msg, exc_info=True)
+            raise ProcessorError(error_msg) from e
+
+    def _extract_metadata(
+        self, doc: pymupdf.Document, filename: Optional[str]
+    ) -> dict[str, Any]:
+        """Extract metadata from PDF document.
+
+        Args:
+            doc: Opened PyMuPDF document
+            filename: Optional filename
+
+        Returns:
+            Dictionary with PDF metadata
+        """
+        metadata: dict[str, Any] = {}
+
+        # Basic document info
+        metadata["page_count"] = doc.page_count
+        metadata["format"] = "PDF 1." + str(
+            doc.pdf_version() if hasattr(doc, "pdf_version") else "?"  # type: ignore[call-non-callable]
+        )
+
+        if filename:
+            metadata["filename"] = filename
+
+        # Extract PDF metadata dictionary
+        pdf_metadata = doc.metadata
+        if pdf_metadata:
+            # Standard PDF metadata fields
+            if pdf_metadata.get("title"):
+                metadata["title"] = pdf_metadata["title"]
+            if pdf_metadata.get("author"):
+                metadata["author"] = pdf_metadata["author"]
+            if pdf_metadata.get("subject"):
+                metadata["subject"] = pdf_metadata["subject"]
+            if pdf_metadata.get("keywords"):
+                metadata["keywords"] = pdf_metadata["keywords"]
+            if pdf_metadata.get("creator"):
+                metadata["creator"] = pdf_metadata["creator"]
+            if pdf_metadata.get("producer"):
+                metadata["producer"] = pdf_metadata["producer"]
+            if pdf_metadata.get("creationDate"):
+                metadata["creation_date"] = pdf_metadata["creationDate"]
+            if pdf_metadata.get("modDate"):
+                metadata["modification_date"] = pdf_metadata["modDate"]
+
+        return metadata
+
+    async def health_check(self) -> bool:
+        """Check if PyMuPDF is available and working.
+
+        Returns:
+            True if processor is ready to use
+        """
+        try:
+            # Try to create a simple PDF in memory
+            test_doc = pymupdf.open()
+            test_doc.close()
+            return True
+        except Exception as e:
+            logger.error(f"PyMuPDF health check failed: {e}")
+            return False

nextcloud_mcp_server/embedding/bm25_provider.py

@@ -53,7 +53,7 @@ class BM25SparseEmbeddingProvider:
             "values": sparse_embedding.values.tolist(),
         }
 
-    def encode_batch(self, texts: list[str]) -> list[dict[str, Any]]:
+    async def encode_batch(self, texts: list[str]) -> list[dict[str, Any]]:
         """
         Generate BM25 sparse embeddings for multiple texts (batched).
 
@@ -63,7 +63,12 @@ class BM25SparseEmbeddingProvider:
         Returns:
             List of dictionaries with 'indices' and 'values' for each text
         """
-        sparse_embeddings = list(self.model.embed(texts))
+        import anyio
+
+        # Run CPU-bound BM25 encoding in thread pool to avoid blocking event loop
+        sparse_embeddings = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+            lambda: list(self.model.embed(texts))
+        )
 
         return [
             {

nextcloud_mcp_server/models/semantic.py

@@ -10,7 +10,7 @@ from .base import BaseResponse
 class SemanticSearchResult(BaseModel):
     """Model for semantic search results with additional metadata."""
 
-    id: int = Field(description="Document ID")
+    id: int = Field(description="Document ID (int for all document types)")
     doc_type: str = Field(
         description="Document type (note, calendar_event, deck_card, etc.)"
     )
@@ -35,6 +35,29 @@ class SemanticSearchResult(BaseModel):
     chunk_end_offset: Optional[int] = Field(
         default=None, description="Character position where chunk ends in document"
     )
+    page_number: Optional[int] = Field(
+        default=None, description="Page number for PDF documents"
+    )
+    # Context expansion fields (optional, populated when include_context=True)
+    has_context_expansion: bool = Field(
+        default=False, description="Whether context expansion was performed"
+    )
+    marked_text: Optional[str] = Field(
+        default=None,
+        description="Full text with position markers around matched chunk",
+    )
+    before_context: Optional[str] = Field(
+        default=None, description="Text before the matched chunk"
+    )
+    after_context: Optional[str] = Field(
+        default=None, description="Text after the matched chunk"
+    )
+    has_before_truncation: Optional[bool] = Field(
+        default=None, description="Whether before_context was truncated"
+    )
+    has_after_truncation: Optional[bool] = Field(
+        default=None, description="Whether after_context was truncated"
+    )
 
 
 class SemanticSearchResponse(BaseResponse):

nextcloud_mcp_server/observability/logging_config.py

@@ -37,7 +37,7 @@ class HealthCheckFilter(logging.Filter):
         """
         # Check if the log message contains health check endpoints
         message = record.getMessage()
-        return not any(
+        health_check = any(
             endpoint in message
             for endpoint in [
                 "/health/live",
@@ -47,6 +47,8 @@ class HealthCheckFilter(logging.Filter):
             ]
         )
 
+        return not health_check
+
 
 class TraceContextFormatter(JsonFormatter):
     """

nextcloud_mcp_server/providers/ollama.py

@@ -92,14 +92,21 @@ class OllamaProvider(Provider):
         response.raise_for_status()
         return response.json()["embedding"]
 
-    async def embed_batch(self, texts: list[str]) -> list[list[float]]:
+    async def embed_batch(
+        self, texts: list[str], batch_size: int = 32
+    ) -> list[list[float]]:
         """
-        Generate embeddings for multiple texts (batched requests).
+        Generate embeddings for multiple texts using Ollama's batch API.
 
-        Note: Ollama doesn't have native batch API, so we send requests sequentially.
+        Uses /api/embed endpoint with array input for efficient batch processing.
+        Conservative batch size (32) prevents quality degradation observed in
+        Ollama issue #6262 with larger batches.
+
+        Note: Ollama processes batches serially, not in parallel.
 
         Args:
             texts: List of texts to embed
+            batch_size: Maximum texts per batch (default: 32)
 
         Returns:
             List of vector embeddings
@@ -112,11 +119,17 @@ class OllamaProvider(Provider):
                 "Embedding not supported - no embedding_model configured"
             )
 
-        embeddings = []
-        for text in texts:
-            embedding = await self.embed(text)
-            embeddings.append(embedding)
-        return embeddings
+        all_embeddings = []
+        for i in range(0, len(texts), batch_size):
+            batch = texts[i : i + batch_size]
+            response = await self.client.post(
+                f"{self.base_url}/api/embed",
+                json={"model": self.embedding_model, "input": batch},
+            )
+            response.raise_for_status()
+            all_embeddings.extend(response.json()["embeddings"])
+
+        return all_embeddings
 
     async def _detect_dimension(self):
         """

nextcloud_mcp_server/search/algorithms.py

@@ -83,6 +83,7 @@ async def get_indexed_doc_types(user_id: str) -> set[str]:
     from qdrant_client.models import FieldCondition, Filter, MatchValue
 
     from nextcloud_mcp_server.config import get_settings
+    from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
     from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
     logger = logging.getLogger(__name__)
@@ -97,7 +98,10 @@ async def get_indexed_doc_types(user_id: str) -> set[str]:
         scroll_results, _next_offset = await qdrant_client.scroll(
             collection_name=collection,
             scroll_filter=Filter(
-                must=[FieldCondition(key="user_id", match=MatchValue(value=user_id))]
+                must=[
+                    get_placeholder_filter(),  # Exclude placeholders from doc_type discovery
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                ]
             ),
             limit=1000,  # Sample size to discover types
             with_payload=["doc_type"],
@@ -123,7 +127,7 @@ class SearchResult:
     """A single search result with metadata and score.
 
     Attributes:
-        id: Document ID
+        id: Document ID (int for all document types)
         doc_type: Document type (note, file, calendar, contact, etc.)
         title: Document title
         excerpt: Content excerpt showing match context
@@ -133,6 +137,9 @@ class SearchResult:
         metadata: Additional algorithm-specific metadata
         chunk_start_offset: Character position where chunk starts (None if not available)
         chunk_end_offset: Character position where chunk ends (None if not available)
+        page_number: Page number for PDF documents (None for other doc types)
+        chunk_index: Zero-based index of this chunk in the document
+        total_chunks: Total number of chunks in the document
     """
 
     id: int
@@ -143,6 +150,9 @@ class SearchResult:
     metadata: dict[str, Any] | None = None
     chunk_start_offset: int | None = None
     chunk_end_offset: int | None = None
+    page_number: int | None = None
+    chunk_index: int = 0
+    total_chunks: int = 1
 
     def __post_init__(self):
         """Validate score is non-negative.

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -10,6 +10,7 @@ from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.embedding import get_bm25_service, get_embedding_service
 from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
 from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
+from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
@@ -72,6 +73,9 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
         Returns unverified results from Qdrant. Access verification should be
         performed separately at the final output stage using verify_search_results().
 
+        Deduplicates by (doc_id, doc_type, chunk_start_offset, chunk_end_offset)
+        to show multiple chunks from the same document while avoiding duplicate chunks.
+
         Args:
             query: Natural language or keyword search query
             user_id: User ID for filtering
@@ -109,10 +113,11 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         # Build Qdrant filter
         filter_conditions = [
+            get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
             FieldCondition(
                 key="user_id",
                 match=MatchValue(value=user_id),
-            )
+            ),
         ]
 
         # Add doc_type filter if specified
@@ -176,20 +181,24 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                 f"Top 3 {self.fusion_name.upper()} fusion scores: {top_scores}"
             )
 
-        # Deduplicate by (doc_id, doc_type) - multiple chunks per document
-        seen_docs = set()
+        # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
+        # This allows multiple chunks from same doc, but removes duplicate chunks
+        seen_chunks = set()
         results = []
 
         for result in search_response.points:
-            doc_id = int(result.payload["doc_id"])
+            # doc_id can be int (notes) or str (files - file paths)
+            doc_id = result.payload["doc_id"]
             doc_type = result.payload.get("doc_type", "note")
-            doc_key = (doc_id, doc_type)
+            chunk_start = result.payload.get("chunk_start_offset")
+            chunk_end = result.payload.get("chunk_end_offset")
+            chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
 
-            # Skip if we've already seen this document
-            if doc_key in seen_docs:
+            # Skip if we've already seen this exact chunk
+            if chunk_key in seen_chunks:
                 continue
 
-            seen_docs.add(doc_key)
+            seen_chunks.add(chunk_key)
 
             # Return unverified results (verification happens at output stage)
             results.append(
@@ -206,6 +215,9 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                     },
                     chunk_start_offset=result.payload.get("chunk_start_offset"),
                     chunk_end_offset=result.payload.get("chunk_end_offset"),
+                    page_number=result.payload.get("page_number"),
+                    chunk_index=result.payload.get("chunk_index", 0),
+                    total_chunks=result.payload.get("total_chunks", 1),
                 )
             )
 

nextcloud_mcp_server/search/context.py

@@ -0,0 +1,598 @@
+"""Context expansion for search results.
+
+Provides utilities to expand matched chunks with surrounding context and
+position markers for better visualization and understanding of search results.
+"""
+
+import logging
+from dataclasses import dataclass
+
+from nextcloud_mcp_server.client import NextcloudClient
+
+logger = logging.getLogger(__name__)
+
+
+async def _get_chunk_from_qdrant(
+    user_id: str, doc_id: int, doc_type: str, chunk_start: int, chunk_end: int
+) -> str | None:
+    """Retrieve full chunk text from Qdrant payload.
+
+    This avoids re-fetching and re-parsing documents by using the cached
+    chunk content already stored in Qdrant.
+
+    Args:
+        user_id: User ID who owns the document
+        doc_id: Document ID
+        doc_type: Document type (e.g., "note", "file")
+        chunk_start: Character offset where chunk starts
+        chunk_end: Character offset where chunk ends
+
+    Returns:
+        Full chunk text from Qdrant excerpt field, or None if not found
+    """
+    try:
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+        from nextcloud_mcp_server.config import get_settings
+        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for the specific chunk
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(
+                        key="chunk_start_offset", match=MatchValue(value=chunk_start)
+                    ),
+                    FieldCondition(
+                        key="chunk_end_offset", match=MatchValue(value=chunk_end)
+                    ),
+                ]
+            ),
+            limit=1,
+            with_payload=["excerpt"],
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            excerpt = point.payload.get("excerpt")
+            if excerpt:
+                logger.debug(
+                    f"Retrieved chunk from Qdrant for {doc_type} {doc_id}: "
+                    f"{len(excerpt)} chars"
+                )
+                return str(excerpt)
+
+        logger.debug(
+            f"Chunk not found in Qdrant for {doc_type} {doc_id}, "
+            f"chunk [{chunk_start}:{chunk_end}]. Will fall back to document fetch."
+        )
+        return None
+
+    except Exception as e:
+        logger.error(
+            f"Error querying Qdrant for chunk: {e}. Falling back to document fetch.",
+            exc_info=True,
+        )
+        return None
+
+
+async def _get_chunk_by_index_from_qdrant(
+    user_id: str, doc_id: int, doc_type: str, chunk_index: int
+) -> str | None:
+    """Retrieve chunk text by chunk_index from Qdrant payload.
+
+    Used to fetch adjacent chunks for context expansion.
+
+    Args:
+        user_id: User ID who owns the document
+        doc_id: Document ID
+        doc_type: Document type (e.g., "note", "file")
+        chunk_index: Zero-based chunk index in document
+
+    Returns:
+        Full chunk text from Qdrant excerpt field, or None if not found
+    """
+    try:
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+        from nextcloud_mcp_server.config import get_settings
+        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for chunk by index
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(
+                        key="chunk_index", match=MatchValue(value=chunk_index)
+                    ),
+                ]
+            ),
+            limit=1,
+            with_payload=["excerpt"],
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            excerpt = point.payload.get("excerpt")
+            if excerpt:
+                logger.debug(
+                    f"Retrieved adjacent chunk {chunk_index} from Qdrant for "
+                    f"{doc_type} {doc_id}: {len(excerpt)} chars"
+                )
+                return str(excerpt)
+
+        return None
+
+    except Exception as e:
+        logger.debug(
+            f"Could not retrieve adjacent chunk {chunk_index} for "
+            f"{doc_type} {doc_id}: {e}"
+        )
+        return None
+
+
+async def _get_file_path_from_qdrant(
+    user_id: str, file_id: int, chunk_start: int, chunk_end: int
+) -> str | None:
+    """Resolve file_id to file_path by querying Qdrant payload.
+
+    Args:
+        user_id: User ID who owns the file
+        file_id: Numeric file ID
+        chunk_start: Character offset where chunk starts
+        chunk_end: Character offset where chunk ends
+
+    Returns:
+        File path string, or None if not found in Qdrant
+    """
+    try:
+        from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+        from nextcloud_mcp_server.config import get_settings
+        from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for the specific chunk
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=file_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value="file")),
+                    FieldCondition(
+                        key="chunk_start_offset", match=MatchValue(value=chunk_start)
+                    ),
+                    FieldCondition(
+                        key="chunk_end_offset", match=MatchValue(value=chunk_end)
+                    ),
+                ]
+            ),
+            limit=1,
+            with_payload=["file_path"],
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            file_path = point.payload.get("file_path")
+            if file_path:
+                logger.debug(f"Resolved file_id {file_id} to file_path {file_path}")
+                return str(file_path)
+
+        logger.warning(
+            f"Could not find file_path in Qdrant for file_id {file_id}, "
+            f"chunk [{chunk_start}:{chunk_end}]"
+        )
+        return None
+
+    except Exception as e:
+        logger.error(f"Error querying Qdrant for file_path: {e}", exc_info=True)
+        return None
+
+
+@dataclass
+class ChunkContext:
+    """Expanded chunk with surrounding context and position markers.
+
+    Attributes:
+        chunk_text: The matched chunk text
+        before_context: Text before the chunk (up to context_chars)
+        after_context: Text after the chunk (up to context_chars)
+        chunk_start_offset: Character position where chunk starts in document
+        chunk_end_offset: Character position where chunk ends in document
+        page_number: Page number for PDFs (None for other doc types)
+        chunk_index: Zero-based chunk index (N in "chunk N of M")
+        total_chunks: Total number of chunks in document
+        marked_text: Full text with position markers around the chunk
+        has_before_truncation: True if before_context was truncated
+        has_after_truncation: True if after_context was truncated
+    """
+
+    chunk_text: str
+    before_context: str
+    after_context: str
+    chunk_start_offset: int
+    chunk_end_offset: int
+    page_number: int | None
+    chunk_index: int
+    total_chunks: int
+    marked_text: str
+    has_before_truncation: bool
+    has_after_truncation: bool
+
+
+async def get_chunk_with_context(
+    nc_client: NextcloudClient,
+    user_id: str,
+    doc_id: str | int,
+    doc_type: str,
+    chunk_start: int,
+    chunk_end: int,
+    page_number: int | None = None,
+    chunk_index: int = 0,
+    total_chunks: int = 1,
+    context_chars: int = 300,
+) -> ChunkContext | None:
+    """Fetch chunk with surrounding context.
+
+    First tries to retrieve the chunk from Qdrant (fast, cached). If that fails
+    (e.g., legacy data with truncated excerpts), falls back to fetching and
+    parsing the full document (slower, for PDFs especially).
+
+    Args:
+        nc_client: Authenticated Nextcloud client
+        user_id: User ID who owns the document
+        doc_id: Document ID (int for notes/files)
+        doc_type: Type of document ("note", "file", etc.)
+        chunk_start: Character offset where chunk starts
+        chunk_end: Character offset where chunk ends
+        page_number: Optional page number for PDFs
+        chunk_index: Zero-based chunk index in document
+        total_chunks: Total number of chunks in document
+        context_chars: Number of characters to include before/after chunk
+
+    Returns:
+        ChunkContext with expanded context and markers, or None if document
+        cannot be retrieved
+    """
+    # Convert doc_id to int for Qdrant query
+    doc_id_int = (
+        int(doc_id)
+        if isinstance(doc_id, str) and doc_id.isdigit()
+        else (doc_id if isinstance(doc_id, int) else None)
+    )
+
+    # Try to get chunk from Qdrant first (fast path)
+    if doc_id_int is not None:
+        chunk_text = await _get_chunk_from_qdrant(
+            user_id, doc_id_int, doc_type, chunk_start, chunk_end
+        )
+        if chunk_text:
+            logger.info(
+                f"Retrieved chunk from Qdrant cache for {doc_type} {doc_id} "
+                f"(avoids document re-fetch/re-parse)"
+            )
+
+            # Fetch adjacent chunks for context expansion
+            # Get chunk overlap from config to remove duplicate text
+            from nextcloud_mcp_server.config import get_settings
+
+            settings = get_settings()
+            chunk_overlap = settings.document_chunk_overlap
+
+            before_context = ""
+            after_context = ""
+            has_before_truncation = False
+            has_after_truncation = False
+
+            # Fetch previous chunk if not first chunk
+            if chunk_index > 0:
+                before_chunk = await _get_chunk_by_index_from_qdrant(
+                    user_id, doc_id_int, doc_type, chunk_index - 1
+                )
+                if before_chunk:
+                    # Remove overlap: the last chunk_overlap chars of previous chunk
+                    # overlap with the first chunk_overlap chars of current chunk
+                    before_context = (
+                        before_chunk[:-chunk_overlap]
+                        if len(before_chunk) > chunk_overlap
+                        else ""
+                    )
+                    # Truncate if requested context_chars < remaining length
+                    if before_context and len(before_context) > context_chars:
+                        before_context = before_context[-context_chars:]
+                        has_before_truncation = True
+                else:
+                    # Could not fetch previous chunk, but we're not at start
+                    has_before_truncation = True
+
+            # Fetch next chunk if not last chunk
+            if chunk_index < total_chunks - 1:
+                after_chunk = await _get_chunk_by_index_from_qdrant(
+                    user_id, doc_id_int, doc_type, chunk_index + 1
+                )
+                if after_chunk:
+                    # Remove overlap: the first chunk_overlap chars of next chunk
+                    # overlap with the last chunk_overlap chars of current chunk
+                    after_context = (
+                        after_chunk[chunk_overlap:]
+                        if len(after_chunk) > chunk_overlap
+                        else ""
+                    )
+                    # Truncate if requested context_chars < remaining length
+                    if after_context and len(after_context) > context_chars:
+                        after_context = after_context[:context_chars]
+                        has_after_truncation = True
+                else:
+                    # Could not fetch next chunk, but we're not at end
+                    has_after_truncation = True
+
+            marked_text = _insert_position_markers(
+                before_context=before_context,
+                chunk_text=chunk_text,
+                after_context=after_context,
+                page_number=page_number,
+                chunk_index=chunk_index,
+                total_chunks=total_chunks,
+                has_before_truncation=has_before_truncation,
+                has_after_truncation=has_after_truncation,
+            )
+            return ChunkContext(
+                chunk_text=chunk_text,
+                before_context=before_context,
+                after_context=after_context,
+                chunk_start_offset=chunk_start,
+                chunk_end_offset=chunk_end,
+                page_number=page_number,
+                chunk_index=chunk_index,
+                total_chunks=total_chunks,
+                marked_text=marked_text,
+                has_before_truncation=has_before_truncation,
+                has_after_truncation=has_after_truncation,
+            )
+
+    # Fallback: Fetch full document and extract chunk with context
+    # This path is taken for:
+    # 1. Legacy data with truncated excerpts in Qdrant
+    # 2. Failed Qdrant queries
+    logger.info(
+        f"Falling back to document fetch for {doc_type} {doc_id} "
+        f"(Qdrant cache miss, possibly legacy data)"
+    )
+
+    # For files, retrieve file_path from Qdrant payload
+    resolved_doc_id = doc_id
+    if doc_type == "file" and isinstance(doc_id, int):
+        file_path = await _get_file_path_from_qdrant(
+            user_id, doc_id, chunk_start, chunk_end
+        )
+        if not file_path:
+            logger.warning(
+                f"Could not resolve file_id {doc_id} to file_path from Qdrant"
+            )
+            return None
+        resolved_doc_id = file_path
+        logger.debug(f"Resolved file_id {doc_id} to file_path {file_path}")
+
+    # Fetch full document text
+    full_text = await _fetch_document_text(nc_client, resolved_doc_id, doc_type)
+    if full_text is None:
+        logger.warning(
+            f"Could not fetch document text for {doc_type} {doc_id}, "
+            "skipping context expansion"
+        )
+        return None
+
+    # Validate offsets
+    if chunk_start < 0 or chunk_end > len(full_text) or chunk_start >= chunk_end:
+        logger.warning(
+            f"Invalid chunk offsets for {doc_type} {doc_id}: "
+            f"start={chunk_start}, end={chunk_end}, doc_len={len(full_text)}"
+        )
+        return None
+
+    # Extract chunk text
+    chunk_text = full_text[chunk_start:chunk_end]
+
+    # Calculate context boundaries
+    context_start = max(0, chunk_start - context_chars)
+    context_end = min(len(full_text), chunk_end + context_chars)
+
+    # Extract context
+    before_context = full_text[context_start:chunk_start]
+    after_context = full_text[chunk_end:context_end]
+
+    # Check for truncation
+    has_before_truncation = context_start > 0
+    has_after_truncation = context_end < len(full_text)
+
+    # Create marked text with position markers
+    marked_text = _insert_position_markers(
+        before_context=before_context,
+        chunk_text=chunk_text,
+        after_context=after_context,
+        page_number=page_number,
+        chunk_index=chunk_index,
+        total_chunks=total_chunks,
+        has_before_truncation=has_before_truncation,
+        has_after_truncation=has_after_truncation,
+    )
+
+    return ChunkContext(
+        chunk_text=chunk_text,
+        before_context=before_context,
+        after_context=after_context,
+        chunk_start_offset=chunk_start,
+        chunk_end_offset=chunk_end,
+        page_number=page_number,
+        chunk_index=chunk_index,
+        total_chunks=total_chunks,
+        marked_text=marked_text,
+        has_before_truncation=has_before_truncation,
+        has_after_truncation=has_after_truncation,
+    )
+
+
+async def _fetch_document_text(
+    nc_client: NextcloudClient, doc_id: str | int, doc_type: str
+) -> str | None:
+    """Fetch full text content of a document.
+
+    Args:
+        nc_client: Authenticated Nextcloud client
+        doc_id: Document ID (note ID or file path)
+        doc_type: Type of document ("note", "file", etc.)
+
+    Returns:
+        Full document text, or None if document cannot be retrieved
+    """
+    try:
+        if doc_type == "note":
+            # Fetch note by ID
+            note = await nc_client.notes.get_note(note_id=int(doc_id))
+            # Reconstruct full content as indexed: title + "\n\n" + content
+            # This ensures chunk offsets align with indexed content structure
+            title = note.get("title", "")
+            content = note.get("content", "")
+            return f"{title}\n\n{content}"
+        elif doc_type == "file":
+            # Fetch file content via WebDAV
+            try:
+                file_path = str(doc_id)
+                file_content, content_type = await nc_client.webdav.read_file(file_path)
+
+                # Check if it's a PDF (by content type or file extension)
+                is_pdf = (
+                    content_type and "pdf" in content_type.lower()
+                ) or file_path.lower().endswith(".pdf")
+
+                if is_pdf:
+                    # Extract text from PDF using PyMuPDF
+                    # IMPORTANT: Use pymupdf4llm.to_markdown() to match indexing extraction
+                    # This ensures character offsets align between indexed chunks and retrieval
+                    import pymupdf
+                    import pymupdf4llm
+
+                    logger.debug(f"Extracting text from PDF: {file_path}")
+                    pdf_doc = pymupdf.open(stream=file_content, filetype="pdf")
+                    text_parts = []
+
+                    # Extract each page as markdown (same as indexing)
+                    for page_num in range(pdf_doc.page_count):
+                        page_md = pymupdf4llm.to_markdown(
+                            pdf_doc,
+                            pages=[page_num],
+                            write_images=False,  # Don't need images for context
+                            page_chunks=False,
+                        )
+                        text_parts.append(page_md)
+
+                    pdf_doc.close()
+
+                    # Join pages (no separator - matches indexing)
+                    full_text = "".join(text_parts)
+                    logger.debug(
+                        f"Extracted {len(full_text)} characters from "
+                        f"{pdf_doc.page_count} pages in {file_path}"
+                    )
+                    return full_text
+                else:
+                    # Assume it's a text file, decode to string
+                    logger.debug(f"Decoding text file: {file_path}")
+                    return file_content.decode("utf-8", errors="replace")
+            except Exception as e:
+                logger.error(
+                    f"Error fetching file content for {doc_id}: {e}", exc_info=True
+                )
+                return None
+        else:
+            logger.warning(f"Unsupported doc_type for context expansion: {doc_type}")
+            return None
+    except Exception as e:
+        logger.error(f"Error fetching document {doc_type} {doc_id}: {e}", exc_info=True)
+        return None
+
+
+def _insert_position_markers(
+    before_context: str,
+    chunk_text: str,
+    after_context: str,
+    page_number: int | None,
+    chunk_index: int,
+    total_chunks: int,
+    has_before_truncation: bool,
+    has_after_truncation: bool,
+) -> str:
+    """Insert position markers around matched chunk.
+
+    Creates markdown-formatted text with visual markers indicating chunk
+    boundaries and metadata.
+
+    Args:
+        before_context: Text before chunk
+        chunk_text: The matched chunk
+        after_context: Text after chunk
+        page_number: Optional page number
+        chunk_index: Zero-based chunk index
+        total_chunks: Total chunks in document
+        has_before_truncation: Whether before_context is truncated
+        has_after_truncation: Whether after_context is truncated
+
+    Returns:
+        Formatted text with position markers
+    """
+    # Build position metadata
+    position_parts = []
+    if page_number is not None:
+        position_parts.append(f"Page {page_number}")
+    position_parts.append(f"Chunk {chunk_index + 1} of {total_chunks}")
+    position_metadata = ", ".join(position_parts)
+
+    # Build marked text
+    parts = []
+
+    # Add truncation indicator for before context
+    if has_before_truncation:
+        parts.append("**[...]**\n\n")
+
+    # Add before context if present
+    if before_context:
+        parts.append(before_context)
+
+    # Add chunk start marker
+    parts.append(f"\n\n🔍 **MATCHED CHUNK START** ({position_metadata})\n\n")
+
+    # Add chunk text
+    parts.append(chunk_text)
+
+    # Add chunk end marker
+    parts.append("\n\n🔍 **MATCHED CHUNK END**\n\n")
+
+    # Add after context if present
+    if after_context:
+        parts.append(after_context)
+
+    # Add truncation indicator for after context
+    if has_after_truncation:
+        parts.append("\n\n**[...]**")
+
+    return "".join(parts)

nextcloud_mcp_server/search/pdf_highlighter.py

@@ -0,0 +1,907 @@
+"""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 _find_chunk_bbox(
+        page: pymupdf.Page,
+        chunk_text: str,
+        page_relative_start: int,
+        page_relative_end: int,
+        page_text_length: int,
+    ) -> tuple[float, float, float, float] | None:
+        """Find bounding box for a chunk without modifying the page.
+
+        Returns (x0, y0, x1, y1) in page coordinates, or None if not found.
+        """
+        page_rect = page.rect
+
+        # Strip markdown for searching
+        search_text = PDFHighlighter.strip_markdown(chunk_text)
+
+        # Try to find chunk location using text search
+        anchor_rect = None
+        search_phrases = []
+
+        # Build search phrases from chunk text
+        sentences = re.split(r"[.!?]\s+", search_text)
+        for sentence in sentences[:3]:
+            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]
+                break
+
+        if not anchor_rect:
+            return None
+
+        # Calculate chunk height based on character count
+        chunk_chars = len(search_text)
+        estimated_lines = max(1, chunk_chars / 60)
+        estimated_height = estimated_lines * 14
+
+        # Build bounding box
+        return (
+            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),
+        )
+
+    @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"
+            )
+
+            # OPTIMIZATION: Render each page ONCE, then draw highlights using PIL
+            # This avoids expensive page.get_pixmap() calls per chunk
+            from io import BytesIO
+
+            from PIL import Image, ImageDraw
+
+            # PIL color for bounding box (RGB tuple)
+            rgb = PDFHighlighter.COLORS.get(color, PDFHighlighter.COLORS["yellow"])
+            pil_color = tuple(int(c * 255) for c in rgb)
+            fill_color = (255, 255, 178, 38)  # Light yellow with alpha
+
+            for page_num, page_chunks in chunks_by_page.items():
+                page = doc[page_num - 1]
+
+                # Render page ONCE to get base image (most expensive operation)
+                mat = pymupdf.Matrix(zoom, zoom)
+                base_pix = page.get_pixmap(matrix=mat, alpha=False)
+                base_png = base_pix.tobytes("png")
+
+                # Convert to PIL Image for fast highlight drawing
+                base_image = Image.open(BytesIO(base_png)).convert("RGBA")
+                page_rect = page.rect
+
+                logger.debug(
+                    f"Page {page_num}: rendered once, processing {len(page_chunks)} chunks"
+                )
+
+                for (
+                    chunk_index,
+                    chunk_page_info,
+                    chunk_text,
+                    page_text_length,
+                ) in page_chunks:
+                    try:
+                        # Find chunk bounding box using text search
+                        bbox = PDFHighlighter._find_chunk_bbox(
+                            page,
+                            chunk_text,
+                            chunk_page_info["page_relative_start"],
+                            chunk_page_info["page_relative_end"],
+                            page_text_length,
+                        )
+
+                        if bbox is None:
+                            logger.warning(f"Chunk {chunk_index}: could not find bbox")
+                            continue
+
+                        # Copy base image for this chunk
+                        chunk_image = base_image.copy()
+
+                        # Scale bbox coordinates to pixmap coordinates
+                        scale_x = base_pix.width / page_rect.width
+                        scale_y = base_pix.height / page_rect.height
+                        pil_bbox = (
+                            int(bbox[0] * scale_x),
+                            int(bbox[1] * scale_y),
+                            int(bbox[2] * scale_x),
+                            int(bbox[3] * scale_y),
+                        )
+
+                        # Create transparent overlay for fill (proper alpha blending)
+                        overlay = Image.new("RGBA", chunk_image.size, (0, 0, 0, 0))
+                        overlay_draw = ImageDraw.Draw(overlay)
+                        overlay_draw.rectangle(pil_bbox, fill=fill_color)
+
+                        # Alpha composite the overlay onto the chunk image
+                        chunk_image = Image.alpha_composite(chunk_image, overlay)
+
+                        # Draw border on top (solid, not transparent)
+                        border_draw = ImageDraw.Draw(chunk_image)
+                        border_draw.rectangle(pil_bbox, outline=pil_color, width=3)
+
+                        # Convert back to PNG bytes
+                        output = BytesIO()
+                        chunk_image.convert("RGB").save(output, format="PNG")
+                        png_bytes = output.getvalue()
+
+                        results[chunk_index] = (png_bytes, page_num, 1)
+
+                        logger.debug(
+                            f"Chunk {chunk_index}: {len(png_bytes):,} bytes, "
+                            f"page {page_num}, bbox {pil_bbox}"
+                        )
+
+                    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}")

nextcloud_mcp_server/search/semantic.py

@@ -9,6 +9,7 @@ from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.embedding import get_embedding_service
 from nextcloud_mcp_server.observability.metrics import record_qdrant_operation
 from nextcloud_mcp_server.search.algorithms import SearchAlgorithm, SearchResult
+from nextcloud_mcp_server.vector.placeholder import get_placeholder_filter
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
@@ -50,6 +51,9 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
         Returns unverified results from Qdrant. Access verification should be
         performed separately at the final output stage using verify_search_results().
 
+        Deduplicates by (doc_id, doc_type, chunk_start_offset, chunk_end_offset)
+        to show multiple chunks from the same document while avoiding duplicate chunks.
+
         Args:
             query: Natural language search query
             user_id: User ID for filtering
@@ -80,10 +84,11 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
 
         # Build Qdrant filter
         filter_conditions = [
+            get_placeholder_filter(),  # Always exclude placeholders from user-facing queries
             FieldCondition(
                 key="user_id",
                 match=MatchValue(value=user_id),
-            )
+            ),
         ]
 
         # Add doc_type filter if specified
@@ -123,20 +128,24 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
             top_scores = [p.score for p in search_response.points[:3]]
             logger.debug(f"Top 3 similarity scores: {top_scores}")
 
-        # Deduplicate by (doc_id, doc_type) - multiple chunks per document
-        seen_docs = set()
+        # Deduplicate by (doc_id, doc_type, chunk_start, chunk_end)
+        # This allows multiple chunks from same doc, but removes duplicate chunks
+        seen_chunks = set()
         results = []
 
         for result in search_response.points:
-            doc_id = int(result.payload["doc_id"])
+            # doc_id can be int (notes) or str (files - file paths)
+            doc_id = result.payload["doc_id"]
             doc_type = result.payload.get("doc_type", "note")
-            doc_key = (doc_id, doc_type)
+            chunk_start = result.payload.get("chunk_start_offset")
+            chunk_end = result.payload.get("chunk_end_offset")
+            chunk_key = (doc_id, doc_type, chunk_start, chunk_end)
 
-            # Skip if we've already seen this document
-            if doc_key in seen_docs:
+            # Skip if we've already seen this exact chunk
+            if chunk_key in seen_chunks:
                 continue
 
-            seen_docs.add(doc_key)
+            seen_chunks.add(chunk_key)
 
             # Return unverified results (verification happens at output stage)
             results.append(
@@ -152,6 +161,9 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
                     },
                     chunk_start_offset=result.payload.get("chunk_start_offset"),
                     chunk_end_offset=result.payload.get("chunk_end_offset"),
+                    page_number=result.payload.get("page_number"),
+                    chunk_index=result.payload.get("chunk_index", 0),
+                    total_chunks=result.payload.get("total_chunks", 1),
                 )
             )
 

nextcloud_mcp_server/server/semantic.py

@@ -26,6 +26,7 @@ from nextcloud_mcp_server.observability.metrics import (
     instrument_tool,
 )
 from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
+from nextcloud_mcp_server.search.context import get_chunk_with_context
 
 logger = logging.getLogger(__name__)
 
@@ -43,6 +44,8 @@ def configure_semantic_tools(mcp: FastMCP):
         doc_types: list[str] | None = None,
         score_threshold: float = 0.0,
         fusion: str = "rrf",
+        include_context: bool = False,
+        context_chars: int = 300,
     ) -> SemanticSearchResponse:
         """
         Search Nextcloud content using BM25 hybrid search with cross-app support.
@@ -66,6 +69,8 @@ def configure_semantic_tools(mcp: FastMCP):
             fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
                    RRF: Good general-purpose fusion using reciprocal ranks
                    DBSF: Uses distribution-based normalization, may better balance different score ranges
+            include_context: Whether to expand results with surrounding context (default: False)
+            context_chars: Number of characters to include before/after matched chunk (default: 300)
 
         Returns:
             SemanticSearchResponse with matching documents ranked by fusion scores
@@ -128,18 +133,16 @@ def configure_semantic_tools(mcp: FastMCP):
                 # Sort combined results by score
                 all_results.sort(key=lambda r: r.score, reverse=True)
 
-            # Deduplicate results (hybrid search may return same doc from dense + sparse)
-            # Qdrant already filters by user_id for multi-tenant isolation
-            # Sampling tool will verify access when fetching full content
-            seen = set()
-            unique_results = []
-            for result in all_results:
-                key = (result.id, result.doc_type)
-                if key not in seen:
-                    seen.add(key)
-                    unique_results.append(result)
-
-            search_results = unique_results[:limit]  # Final limit after deduplication
+            # Note: BM25HybridSearchAlgorithm already deduplicates at chunk level
+            # (doc_id, doc_type, chunk_start, chunk_end), which allows multiple
+            # chunks from the same document while preventing duplicate chunks.
+            # No additional deduplication needed here - multiple chunks per document
+            # are valuable for RAG contexts.
+            # Qdrant already filters by user_id for multi-tenant isolation.
+            # Sampling tool will verify access when fetching full content.
+            search_results = all_results[
+                :limit
+            ]  # Final limit after chunk-level dedup in algorithm
 
             # Convert SearchResult objects to SemanticSearchResult for response
             results = []
@@ -160,9 +163,99 @@ def configure_semantic_tools(mcp: FastMCP):
                         else 1,
                         chunk_start_offset=r.chunk_start_offset,
                         chunk_end_offset=r.chunk_end_offset,
+                        page_number=r.page_number,
                     )
                 )
 
+            # Expand results with surrounding context if requested
+            if include_context and results:
+                logger.info(
+                    f"Expanding {len(results)} results with context "
+                    f"(context_chars={context_chars})"
+                )
+
+                # Fetch context for all results in parallel
+                # Limit concurrent requests to prevent connection pool exhaustion
+                max_concurrent = 20
+                semaphore = anyio.Semaphore(max_concurrent)
+                expanded_results = [None] * len(results)
+
+                async def fetch_context(index: int, result: SemanticSearchResult):
+                    """Fetch context for a single result (parallel with semaphore)."""
+                    async with semaphore:
+                        # Only expand if we have valid chunk offsets
+                        if (
+                            result.chunk_start_offset is None
+                            or result.chunk_end_offset is None
+                        ):
+                            # Keep result as-is without context expansion
+                            expanded_results[index] = result
+                            return
+
+                        try:
+                            chunk_context = await get_chunk_with_context(
+                                nc_client=client,
+                                user_id=username,
+                                doc_id=result.id,
+                                doc_type=result.doc_type,
+                                chunk_start=result.chunk_start_offset,
+                                chunk_end=result.chunk_end_offset,
+                                page_number=result.page_number,
+                                chunk_index=result.chunk_index,
+                                total_chunks=result.total_chunks,
+                                context_chars=context_chars,
+                            )
+
+                            if chunk_context:
+                                # Create new result with context fields populated
+                                expanded_results[index] = SemanticSearchResult(
+                                    id=result.id,
+                                    doc_type=result.doc_type,
+                                    title=result.title,
+                                    category=result.category,
+                                    excerpt=result.excerpt,
+                                    score=result.score,
+                                    chunk_index=result.chunk_index,
+                                    total_chunks=result.total_chunks,
+                                    chunk_start_offset=result.chunk_start_offset,
+                                    chunk_end_offset=result.chunk_end_offset,
+                                    page_number=result.page_number,
+                                    # Context expansion fields
+                                    has_context_expansion=True,
+                                    marked_text=chunk_context.marked_text,
+                                    before_context=chunk_context.before_context,
+                                    after_context=chunk_context.after_context,
+                                    has_before_truncation=chunk_context.has_before_truncation,
+                                    has_after_truncation=chunk_context.has_after_truncation,
+                                )
+                                logger.debug(
+                                    f"Expanded context for {result.doc_type} {result.id}"
+                                )
+                            else:
+                                # Context expansion failed, keep original result
+                                expanded_results[index] = result
+                                logger.debug(
+                                    f"Failed to expand context for {result.doc_type} {result.id}, "
+                                    "keeping original result"
+                                )
+                        except Exception as e:
+                            # Context expansion failed, keep original result
+                            expanded_results[index] = result
+                            logger.warning(
+                                f"Error expanding context for {result.doc_type} {result.id}: {e}"
+                            )
+
+                # Run all context fetches in parallel using anyio task group
+                async with anyio.create_task_group() as tg:
+                    for idx, result in enumerate(results):
+                        tg.start_soon(fetch_context, idx, result)
+
+                # Replace results with expanded versions
+                results = [r for r in expanded_results if r is not None]
+                logger.info(
+                    f"Context expansion completed: {len(results)} results with context"
+                )
+
             logger.info(f"Returning {len(results)} results from BM25 hybrid search")
 
             return SemanticSearchResponse(
@@ -202,6 +295,8 @@ def configure_semantic_tools(mcp: FastMCP):
         score_threshold: float = 0.7,
         max_answer_tokens: int = 500,
         fusion: str = "rrf",
+        include_context: bool = False,
+        context_chars: int = 300,
     ) -> SamplingSearchResponse:
         """
         Semantic search with LLM-generated answer using MCP sampling.
@@ -227,6 +322,8 @@ def configure_semantic_tools(mcp: FastMCP):
             score_threshold: Minimum similarity score 0-1 (default: 0.7)
             max_answer_tokens: Maximum tokens for generated answer (default: 500)
             fusion: Fusion algorithm: "rrf" (Reciprocal Rank Fusion, default) or "dbsf" (Distribution-Based Score Fusion)
+            include_context: Whether to expand results with surrounding context (default: False)
+            context_chars: Number of characters to include before/after matched chunk (default: 300)
 
         Returns:
             SamplingSearchResponse containing:
@@ -267,6 +364,8 @@ def configure_semantic_tools(mcp: FastMCP):
             limit=limit,
             score_threshold=score_threshold,
             fusion=fusion,
+            include_context=include_context,
+            context_chars=context_chars,
         )
 
         # 2. Handle no results case - don't waste a sampling call

nextcloud_mcp_server/vector/document_chunker.py

@@ -15,6 +15,8 @@ class ChunkWithPosition:
     text: str
     start_offset: int  # Character position where chunk starts
     end_offset: int  # Character position where chunk ends (exclusive)
+    page_number: int | None = None  # Page number for PDF chunks (optional)
+    metadata: dict | None = None  # Additional processor-specific metadata (optional)
 
 
 class DocumentChunker:
@@ -50,7 +52,7 @@ class DocumentChunker:
             strip_whitespace=True,
         )
 
-    def chunk_text(self, content: str) -> list[ChunkWithPosition]:
+    async def chunk_text(self, content: str) -> list[ChunkWithPosition]:
         """
         Split text into overlapping chunks with position tracking.
 
@@ -66,12 +68,17 @@ class DocumentChunker:
         Returns:
             List of chunks with their character positions in the original content
         """
+        import anyio
+
         # Handle empty content - return single empty chunk for backward compatibility
         if not content:
             return [ChunkWithPosition(text="", start_offset=0, end_offset=0)]
 
-        # Use LangChain to create documents with position tracking
-        docs = self.splitter.create_documents([content])
+        # Run CPU-bound text splitting in thread pool to avoid blocking event loop
+        docs = await anyio.to_thread.run_sync(  # type: ignore[attr-defined]
+            self.splitter.create_documents,
+            [content],
+        )
 
         # Convert LangChain Documents to ChunkWithPosition objects
         chunks = [

nextcloud_mcp_server/vector/placeholder.py

@@ -0,0 +1,306 @@
+"""Placeholder point management for Qdrant state tracking.
+
+Placeholders are zero-vector points stored in Qdrant to track document processing
+state. They prevent duplicate work by marking documents as "in-flight" during the
+gap between scanner queuing and processor completion.
+
+Architecture:
+- Scanner writes placeholders when queuing documents for processing
+- Processor deletes placeholders and writes real vectors after processing
+- All user-facing queries filter out placeholders (is_placeholder: False)
+
+Placeholders contain:
+- Zero vectors (dimension from embedding service)
+- is_placeholder: True flag (for filtering)
+- status: "pending", "processing", "completed", "failed"
+- modified_at, etag from source document
+- queued_at timestamp
+"""
+
+import logging
+import time
+import uuid
+
+from qdrant_client.models import FieldCondition, Filter, MatchValue, PointStruct
+
+from nextcloud_mcp_server.config import get_settings
+from nextcloud_mcp_server.embedding import get_embedding_service
+from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
+
+logger = logging.getLogger(__name__)
+
+
+def _generate_placeholder_id(doc_type: str, doc_id: str | int) -> str:
+    """Generate deterministic UUID for placeholder point.
+
+    Args:
+        doc_type: Document type (note, file, etc.)
+        doc_id: Document ID
+
+    Returns:
+        UUID string for point ID
+    """
+    point_name = f"{doc_type}:{doc_id}:placeholder"
+    return str(uuid.uuid5(uuid.NAMESPACE_DNS, point_name))
+
+
+async def write_placeholder_point(
+    doc_id: str | int,
+    doc_type: str,
+    user_id: str,
+    modified_at: int,
+    etag: str = "",
+    file_path: str | None = None,
+) -> None:
+    """Write a placeholder point to Qdrant to mark document as queued.
+
+    This should be called by the scanner BEFORE queuing a document for processing.
+    The placeholder prevents duplicate work if the scanner runs again before
+    processing completes.
+
+    Args:
+        doc_id: Document ID (int for notes/files)
+        doc_type: Document type (note, file, etc.)
+        user_id: User ID who owns the document
+        modified_at: Document modification timestamp
+        etag: Document ETag (if available)
+        file_path: File path (for files only)
+
+    Raises:
+        Exception: If Qdrant write fails
+    """
+    try:
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+        embedding_service = get_embedding_service()
+
+        # Get dimension dynamically (never hardcode)
+        dimension = embedding_service.get_dimension()
+
+        # Create zero vectors
+        zero_dense = [0.0] * dimension
+
+        # Create empty sparse vector for placeholders
+        # Use models.SparseVector with empty indices/values
+        from qdrant_client import models
+
+        empty_sparse = models.SparseVector(indices=[], values=[])
+
+        # Generate deterministic point ID
+        point_id = _generate_placeholder_id(doc_type, doc_id)
+
+        # Build payload
+        payload = {
+            "user_id": user_id,
+            "doc_id": doc_id,
+            "doc_type": doc_type,
+            "is_placeholder": True,
+            "status": "pending",
+            "modified_at": modified_at,
+            "etag": etag,
+            "queued_at": int(time.time()),
+        }
+
+        # Add file_path for files
+        if doc_type == "file" and file_path:
+            payload["file_path"] = file_path
+
+        # Create placeholder point
+        point = PointStruct(
+            id=point_id,
+            vector={
+                "dense": zero_dense,
+                "sparse": empty_sparse,  # Empty sparse vector for placeholders
+            },
+            payload=payload,
+        )
+
+        # Upsert to Qdrant
+        await qdrant_client.upsert(
+            collection_name=settings.get_collection_name(),
+            points=[point],
+            wait=True,
+        )
+
+        logger.debug(
+            f"Wrote placeholder for {doc_type}_{doc_id} (user={user_id}, "
+            f"modified_at={modified_at})"
+        )
+
+    except Exception as e:
+        logger.error(
+            f"Failed to write placeholder for {doc_type}_{doc_id}: {e}",
+            exc_info=True,
+        )
+        raise
+
+
+async def query_document_metadata(
+    doc_id: str | int,
+    doc_type: str,
+    user_id: str,
+) -> dict | None:
+    """Query Qdrant for existing document entry (placeholder or real).
+
+    Returns the payload of the first matching point, which could be:
+    - A placeholder (is_placeholder: True)
+    - A real indexed document (is_placeholder: False or missing)
+    - None if document not in Qdrant
+
+    Args:
+        doc_id: Document ID
+        doc_type: Document type
+        user_id: User ID
+
+    Returns:
+        Payload dict if found, None otherwise
+    """
+    try:
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Query for any entry matching doc_id, doc_type, user_id
+        scroll_result = await qdrant_client.scroll(
+            collection_name=settings.get_collection_name(),
+            scroll_filter=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                ]
+            ),
+            limit=1,
+            with_payload=True,
+            with_vectors=False,
+        )
+
+        if scroll_result[0]:
+            point = scroll_result[0][0]
+            return dict(point.payload)
+
+        return None
+
+    except Exception as e:
+        logger.warning(f"Error querying document metadata for {doc_type}_{doc_id}: {e}")
+        return None
+
+
+async def delete_placeholder_point(
+    doc_id: str | int,
+    doc_type: str,
+    user_id: str,
+) -> None:
+    """Delete a placeholder point from Qdrant.
+
+    This should be called by the processor BEFORE writing real vectors.
+    We delete the placeholder to avoid duplicates, then write the real chunks.
+
+    Args:
+        doc_id: Document ID
+        doc_type: Document type
+        user_id: User ID
+
+    Raises:
+        Exception: If Qdrant delete fails
+    """
+    try:
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Delete by filter (in case there are multiple chunks from old indexing)
+        await qdrant_client.delete(
+            collection_name=settings.get_collection_name(),
+            points_selector=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(key="is_placeholder", match=MatchValue(value=True)),
+                ]
+            ),
+        )
+
+        logger.debug(f"Deleted placeholder for {doc_type}_{doc_id} (user={user_id})")
+
+    except Exception as e:
+        logger.error(
+            f"Failed to delete placeholder for {doc_type}_{doc_id}: {e}",
+            exc_info=True,
+        )
+        raise
+
+
+async def update_placeholder_status(
+    doc_id: str | int,
+    doc_type: str,
+    user_id: str,
+    status: str,
+) -> None:
+    """Update the status field of a placeholder point.
+
+    Status values:
+    - "pending": Queued for processing
+    - "processing": Currently being processed
+    - "completed": Processing completed successfully
+    - "failed": Processing failed
+
+    Args:
+        doc_id: Document ID
+        doc_type: Document type
+        user_id: User ID
+        status: New status value
+
+    Raises:
+        Exception: If Qdrant update fails
+    """
+    try:
+        qdrant_client = await get_qdrant_client()
+        settings = get_settings()
+
+        # Update payload using set_payload
+        await qdrant_client.set_payload(
+            collection_name=settings.get_collection_name(),
+            payload={"status": status},
+            points=Filter(
+                must=[
+                    FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                    FieldCondition(key="doc_id", match=MatchValue(value=doc_id)),
+                    FieldCondition(key="doc_type", match=MatchValue(value=doc_type)),
+                    FieldCondition(key="is_placeholder", match=MatchValue(value=True)),
+                ]
+            ),
+        )
+
+        logger.debug(
+            f"Updated placeholder status for {doc_type}_{doc_id} to '{status}' "
+            f"(user={user_id})"
+        )
+
+    except Exception as e:
+        logger.warning(
+            f"Failed to update placeholder status for {doc_type}_{doc_id}: {e}"
+        )
+        # Don't raise - status updates are non-critical
+
+
+def get_placeholder_filter() -> FieldCondition:
+    """Get a filter condition to exclude placeholders from queries.
+
+    Add this to all user-facing search/visualization queries to ensure
+    placeholders are never returned to users.
+
+    Returns:
+        FieldCondition that filters out is_placeholder: True
+
+    Example:
+        Filter(
+            must=[
+                get_placeholder_filter(),  # Exclude placeholders
+                FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+            ]
+        )
+    """
+    return FieldCondition(
+        key="is_placeholder",
+        match=MatchValue(value=False),
+    )

nextcloud_mcp_server/vector/processor.py

@@ -23,12 +23,50 @@ from nextcloud_mcp_server.observability.metrics import (
 )
 from nextcloud_mcp_server.observability.tracing import trace_operation
 from nextcloud_mcp_server.vector.document_chunker import DocumentChunker
+from nextcloud_mcp_server.vector.placeholder import delete_placeholder_point
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 from nextcloud_mcp_server.vector.scanner import DocumentTask
 
 logger = logging.getLogger(__name__)
 
 
+def assign_page_numbers(chunks, page_boundaries):
+    """Assign page numbers to chunks based on page boundaries.
+
+    Each chunk gets the page number where most of its content appears.
+    For chunks spanning multiple pages, assigns the page containing the
+    majority of the chunk's characters.
+
+    Args:
+        chunks: List of ChunkWithPosition objects
+        page_boundaries: List of dicts with {page, start_offset, end_offset}
+
+    Returns:
+        None (modifies chunks in place)
+    """
+    if not page_boundaries:
+        return
+
+    for chunk in chunks:
+        # Find which page(s) this chunk overlaps with
+        max_overlap = 0
+        assigned_page = None
+
+        for boundary in page_boundaries:
+            # Calculate overlap between chunk and page
+            overlap_start = max(chunk.start_offset, boundary["start_offset"])
+            overlap_end = min(chunk.end_offset, boundary["end_offset"])
+            overlap = max(0, overlap_end - overlap_start)
+
+            # Assign to page with maximum overlap
+            if overlap > max_overlap:
+                max_overlap = overlap
+                assigned_page = boundary["page"]
+
+        if assigned_page is not None:
+            chunk.page_number = assigned_page
+
+
 async def processor_task(
     worker_id: int,
     receive_stream: MemoryObjectReceiveStream[DocumentTask],
@@ -218,31 +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", "")
-    else:
-        raise ValueError(f"Unsupported doc_type: {doc_task.doc_type}")
+    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})"
+                )
+            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)}"
+                    )
+                    # 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']}]"
+                        )
+                    # 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 = 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:
+        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}]"
+                )
+
+            # 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 = 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())
@@ -267,8 +539,9 @@ async def _index_document(
                     "user_id": doc_task.user_id,
                     "doc_id": doc_task.doc_id,
                     "doc_type": doc_task.doc_type,
+                    "is_placeholder": False,  # Real indexed document (not placeholder)
                     "title": title,
-                    "excerpt": chunk.text[:200],
+                    "excerpt": chunk.text,  # Full chunk text (up to chunk_size, default 2048 chars)
                     "indexed_at": indexed_at,
                     "modified_at": doc_task.modified_at,
                     "etag": etag,
@@ -277,16 +550,74 @@ async def _index_document(
                     "chunk_start_offset": chunk.start_offset,
                     "chunk_end_offset": chunk.end_offset,
                     "metadata_version": 2,  # v2 includes position metadata
+                    # File-specific metadata (PDF, etc.)
+                    **(
+                        {
+                            "file_path": file_path,  # Store file path for retrieval
+                            "mime_type": content_type,  # From WebDAV response
+                            "file_size": file_metadata.get("file_size"),
+                            "page_number": chunk.page_number,
+                            "page_count": file_metadata.get("page_count"),
+                            "author": file_metadata.get("author"),
+                            "creation_date": file_metadata.get("creation_date"),
+                            "has_images": file_metadata.get("has_images", False),
+                            "image_count": file_metadata.get("image_count", 0),
+                        }
+                        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 {}
+                    ),
                 },
             )
         )
 
-    # Upsert to Qdrant
-    await qdrant_client.upsert(
-        collection_name=settings.get_collection_name(),
-        points=points,
-        wait=True,
-    )
+    # Delete placeholder before writing real vectors
+    # This prevents duplicates and cleans up the placeholder state
+    try:
+        await delete_placeholder_point(
+            doc_id=doc_task.doc_id,
+            doc_type=doc_task.doc_type,
+            user_id=doc_task.user_id,
+        )
+    except Exception as e:
+        # Log but don't fail indexing if placeholder deletion fails
+        logger.warning(
+            f"Failed to delete placeholder for {doc_task.doc_type}_{doc_task.doc_id}: {e}"
+        )
+
+    # 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} "

nextcloud_mcp_server/vector/scanner.py

@@ -4,6 +4,7 @@ Periodically scans enabled users' content and queues changed documents for proce
 """
 
 import logging
+import os
 import time
 from dataclasses import dataclass
 
@@ -16,6 +17,10 @@ from nextcloud_mcp_server.client import NextcloudClient
 from nextcloud_mcp_server.config import get_settings
 from nextcloud_mcp_server.observability.metrics import record_vector_sync_scan
 from nextcloud_mcp_server.observability.tracing import trace_operation
+from nextcloud_mcp_server.vector.placeholder import (
+    query_document_metadata,
+    write_placeholder_point,
+)
 from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
@@ -26,10 +31,11 @@ class DocumentTask:
     """Document task for processing queue."""
 
     user_id: str
-    doc_id: str
+    doc_id: int | str  # int for files/notes, str for legacy
     doc_type: str  # "note", "file", "calendar"
     operation: str  # "index" or "delete"
     modified_at: int
+    file_path: str | None = None  # File path for files (when doc_id is file_id)
 
 
 # Track documents potentially deleted (grace period before actual deletion)
@@ -182,8 +188,9 @@ async def scan_user_documents(
                 f"[SCAN-{scan_id}] Using pruneBefore={prune_before} to optimize data transfer"
             )
 
-        # Get indexed state from Qdrant first (for incremental sync)
-        indexed_docs = {}
+        # For deletion tracking, get all doc_ids in Qdrant (for incremental sync)
+        # Note: We no longer bulk-query indexed_at, instead check per-document
+        indexed_doc_ids = set()
         if not initial_sync:
             qdrant_client = await get_qdrant_client()
             scroll_result = await qdrant_client.scroll(
@@ -194,17 +201,14 @@ async def scan_user_documents(
                         FieldCondition(key="doc_type", match=MatchValue(value="note")),
                     ]
                 ),
-                with_payload=["doc_id", "indexed_at"],
+                with_payload=["doc_id"],
                 with_vectors=False,
                 limit=10000,
             )
 
-            indexed_docs = {
-                point.payload["doc_id"]: point.payload["indexed_at"]
-                for point in scroll_result[0]
-            }
+            indexed_doc_ids = {point.payload["doc_id"] for point in scroll_result[0]}
 
-            logger.debug(f"Found {len(indexed_docs)} indexed documents in Qdrant")
+            logger.debug(f"Found {len(indexed_doc_ids)} indexed documents in Qdrant")
 
         # Stream notes from Nextcloud and process immediately
         note_count = 0
@@ -218,7 +222,14 @@ async def scan_user_documents(
             modified_at = note.get("modified", 0)
 
             if initial_sync:
-                # Send everything on first sync
+                # Send everything on first sync - write placeholder first
+                await write_placeholder_point(
+                    doc_id=doc_id,
+                    doc_type="note",
+                    user_id=user_id,
+                    modified_at=modified_at,
+                    etag=note.get("etag", ""),
+                )
                 await send_stream.send(
                     DocumentTask(
                         user_id=user_id,
@@ -230,9 +241,7 @@ async def scan_user_documents(
                 )
                 queued += 1
             else:
-                # Incremental sync: compare with indexed state
-                indexed_at = indexed_docs.get(doc_id)
-
+                # Incremental sync: check if document exists and compare modified_at
                 # If document reappeared, remove from potentially_deleted
                 doc_key = (user_id, doc_id)
                 if doc_key in _potentially_deleted:
@@ -241,8 +250,48 @@ async def scan_user_documents(
                     )
                     del _potentially_deleted[doc_key]
 
+                # Query Qdrant for existing entry (placeholder or real)
+                existing_metadata = await query_document_metadata(
+                    doc_id=doc_id, doc_type="note", user_id=user_id
+                )
+
                 # Send if never indexed or modified since last index
-                if indexed_at is None or modified_at > indexed_at:
+                # Compare against stored modified_at (not indexed_at!)
+                needs_indexing = False
+                if existing_metadata is None:
+                    # Never seen before
+                    needs_indexing = True
+                elif existing_metadata.get("modified_at", 0) < modified_at:
+                    # Document modified since last indexing
+                    needs_indexing = True
+                elif existing_metadata.get("is_placeholder", False):
+                    # Placeholder exists - check if it's stale (processing may have failed)
+                    # Only requeue if placeholder is older than 5x scan interval
+                    # (Large PDFs can take 3-4 minutes to process)
+                    queued_at = existing_metadata.get("queued_at", 0)
+                    placeholder_age = time.time() - queued_at
+                    stale_threshold = get_settings().vector_sync_scan_interval * 5
+                    if placeholder_age > stale_threshold:
+                        logger.debug(
+                            f"Found stale placeholder for note {doc_id} "
+                            f"(age={placeholder_age:.1f}s), requeuing"
+                        )
+                        needs_indexing = True
+                    else:
+                        logger.debug(
+                            f"Skipping note {doc_id} with recent placeholder "
+                            f"(age={placeholder_age:.1f}s < {stale_threshold:.1f}s)"
+                        )
+
+                if needs_indexing:
+                    # Write placeholder before queuing
+                    await write_placeholder_point(
+                        doc_id=doc_id,
+                        doc_type="note",
+                        user_id=user_id,
+                        modified_at=modified_at,
+                        etag=note.get("etag", ""),
+                    )
                     await send_stream.send(
                         DocumentTask(
                             user_id=user_id,
@@ -270,7 +319,7 @@ async def scan_user_documents(
         )  # Allow 1.5 scan intervals
         current_time = time.time()
 
-        for doc_id in indexed_docs:
+        for doc_id in indexed_doc_ids:
             if doc_id not in nextcloud_doc_ids:
                 doc_key = (user_id, doc_id)
 
@@ -309,7 +358,195 @@ async def scan_user_documents(
                     )
                     _potentially_deleted[doc_key] = current_time
 
+        # Scan tagged PDF files (after notes)
+        # Get indexed file IDs from Qdrant (for deletion tracking)
+        indexed_file_ids = set()
+        if not initial_sync:
+            file_scroll_result = await qdrant_client.scroll(
+                collection_name=settings.get_collection_name(),
+                scroll_filter=Filter(
+                    must=[
+                        FieldCondition(key="user_id", match=MatchValue(value=user_id)),
+                        FieldCondition(key="doc_type", match=MatchValue(value="file")),
+                    ]
+                ),
+                limit=10000,  # Reasonable limit for file count
+                with_payload=["doc_id"],
+                with_vectors=False,
+            )
+
+            indexed_file_ids = {
+                point.payload["doc_id"] for point in file_scroll_result[0]
+            }
+
+            logger.debug(f"Found {len(indexed_file_ids)} indexed files in Qdrant")
+
+        # Scan for tagged PDF files
+        file_count = 0
+        file_queued = 0
+        nextcloud_file_ids = set()
+
+        try:
+            # Find files with vector-index tag using OCS Tags API
+            settings = get_settings()
+            tag_name = os.getenv("VECTOR_SYNC_PDF_TAG", "vector-index")
+            # Use NextcloudClient.find_files_by_tag() which uses proper OCS API
+            # and filters by PDF MIME type
+            tagged_files = await nc_client.find_files_by_tag(
+                tag_name, mime_type_filter="application/pdf"
+            )
+
+            for file_info in tagged_files:
+                # Files are already filtered by MIME type in find_files_by_tag()
+                file_count += 1
+                file_id = file_info["id"]  # Use numeric file ID, not path
+                file_path = file_info["path"]  # Keep path for logging
+                nextcloud_file_ids.add(file_id)
+
+                # Use last_modified timestamp if available, otherwise use current time
+                modified_at = file_info.get("last_modified_timestamp", int(time.time()))
+                if isinstance(file_info.get("last_modified"), str):
+                    # Parse RFC 2822 date format if needed
+                    from email.utils import parsedate_to_datetime
+
+                    try:
+                        dt = parsedate_to_datetime(file_info["last_modified"])
+                        modified_at = int(dt.timestamp())
+                    except (ValueError, KeyError):
+                        pass
+
+                if initial_sync:
+                    # Send everything on first sync - write placeholder first
+                    await write_placeholder_point(
+                        doc_id=file_id,
+                        doc_type="file",
+                        user_id=user_id,
+                        modified_at=modified_at,
+                        file_path=file_path,
+                    )
+                    await send_stream.send(
+                        DocumentTask(
+                            user_id=user_id,
+                            doc_id=file_id,  # Use numeric file ID
+                            doc_type="file",
+                            operation="index",
+                            modified_at=modified_at,
+                            file_path=file_path,  # Pass file path for content retrieval
+                        )
+                    )
+                    file_queued += 1
+                else:
+                    # Incremental sync: check if file exists and compare modified_at
+                    # If file reappeared, remove from potentially_deleted
+                    file_key = (user_id, file_id)
+                    if file_key in _potentially_deleted:
+                        logger.debug(
+                            f"File {file_path} (ID: {file_id}) reappeared, removing from deletion grace period"
+                        )
+                        del _potentially_deleted[file_key]
+
+                    # Query Qdrant for existing entry (placeholder or real)
+                    existing_metadata = await query_document_metadata(
+                        doc_id=file_id, doc_type="file", user_id=user_id
+                    )
+
+                    # Send if never indexed or modified since last index
+                    # Compare against stored modified_at (not indexed_at!)
+                    needs_indexing = False
+                    if existing_metadata is None:
+                        # Never seen before
+                        needs_indexing = True
+                    elif existing_metadata.get("modified_at", 0) < modified_at:
+                        # File modified since last indexing
+                        needs_indexing = True
+                    elif existing_metadata.get("is_placeholder", False):
+                        # Placeholder exists - check if it's stale (processing may have failed)
+                        # Only requeue if placeholder is older than 5x scan interval
+                        # (Large PDFs can take 3-4 minutes to process)
+                        queued_at = existing_metadata.get("queued_at", 0)
+                        placeholder_age = time.time() - queued_at
+                        stale_threshold = get_settings().vector_sync_scan_interval * 5
+                        if placeholder_age > stale_threshold:
+                            logger.debug(
+                                f"Found stale placeholder for file {file_path} (ID: {file_id}) "
+                                f"(age={placeholder_age:.1f}s), requeuing"
+                            )
+                            needs_indexing = True
+                        else:
+                            logger.debug(
+                                f"Skipping file {file_path} (ID: {file_id}) with recent placeholder "
+                                f"(age={placeholder_age:.1f}s < {stale_threshold:.1f}s)"
+                            )
+
+                    if needs_indexing:
+                        # Write placeholder before queuing
+                        await write_placeholder_point(
+                            doc_id=file_id,
+                            doc_type="file",
+                            user_id=user_id,
+                            modified_at=modified_at,
+                            file_path=file_path,
+                        )
+                        await send_stream.send(
+                            DocumentTask(
+                                user_id=user_id,
+                                doc_id=file_id,  # Use numeric file ID
+                                doc_type="file",
+                                operation="index",
+                                modified_at=modified_at,
+                                file_path=file_path,  # Pass file path for content retrieval
+                            )
+                        )
+                        file_queued += 1
+
+            logger.info(
+                f"[SCAN-{scan_id}] Found {file_count} tagged PDFs for {user_id}"
+            )
+            record_vector_sync_scan(file_count)
+
+            # Check for deleted files (not initial sync)
+            if not initial_sync:
+                for file_id in indexed_file_ids:
+                    if file_id not in nextcloud_file_ids:
+                        file_key = (user_id, file_id)
+
+                        if file_key in _potentially_deleted:
+                            # Check if grace period elapsed
+                            first_missing_time = _potentially_deleted[file_key]
+                            time_missing = current_time - first_missing_time
+
+                            if time_missing >= grace_period:
+                                # Grace period elapsed, send for deletion
+                                logger.info(
+                                    f"File ID {file_id} missing for {time_missing:.1f}s "
+                                    f"(>{grace_period:.1f}s grace period), sending deletion"
+                                )
+                                await send_stream.send(
+                                    DocumentTask(
+                                        user_id=user_id,
+                                        doc_id=file_id,  # Use numeric file ID
+                                        doc_type="file",
+                                        operation="delete",
+                                        modified_at=0,
+                                    )
+                                )
+                                file_queued += 1
+                                del _potentially_deleted[file_key]
+                        else:
+                            # First time missing, add to grace period tracking
+                            logger.debug(
+                                f"File ID {file_id} missing for first time, starting grace period"
+                            )
+                            _potentially_deleted[file_key] = current_time
+
+        except Exception as e:
+            logger.warning(f"Failed to scan tagged files for {user_id}: {e}")
+
+        queued += file_queued
+
         if queued > 0:
-            logger.info(f"Sent {queued} documents for incremental sync: {user_id}")
+            logger.info(
+                f"Sent {queued} documents ({file_queued} files) for incremental sync: {user_id}"
+            )
         else:
             logger.debug(f"No changes detected for {user_id}")

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.44.0"
+version = "0.45.0"
 description = "Model Context Protocol (MCP) server for Nextcloud integration - enables AI assistants to interact with Nextcloud data"
 authors = [
     {name = "Chris Coutinho", email = "chris@coutinho.io"}
@@ -10,7 +10,7 @@ license = {text = "AGPL-3.0-only"}
 requires-python = ">=3.11"
 keywords = ["nextcloud", "mcp", "model-context-protocol", "llm", "ai", "claude", "webdav", "caldav", "carddav"]
 dependencies = [
-    "mcp[cli] (>=1.21,<1.22)",
+    "mcp[cli] (>=1.22,<1.23)",
     "httpx (>=0.28.1,<0.29.0)",
     "pillow (>=10.3.0,<12.0.0)", # Compatible with fastembed
     "icalendar (>=6.0.0,<7.0.0)",
@@ -36,6 +36,9 @@ dependencies = [
     "python-json-logger>=3.2.0", # Structured JSON logging
     "jinja2>=3.1.6",
     "langchain-text-splitters>=1.0.0",
+    "pymupdf>=1.26.6",
+    "pymupdf4llm>=0.2.2",
+    "pymupdf-layout>=1.26.6",
 ]
 classifiers = [
     "Development Status :: 4 - Beta",

tests/integration/test_pdf_indexing.py

@@ -0,0 +1,361 @@
+"""Integration tests for PDF document indexing and semantic search.
+
+These tests validate the complete PDF processing flow:
+1. Process PDF with PyMuPDFProcessor
+2. Chunk extracted text with page numbers
+3. Index chunks into Qdrant with metadata
+4. Perform semantic search on PDF content
+5. Verify page numbers and metadata are preserved
+"""
+
+import pymupdf
+import pytest
+from qdrant_client import AsyncQdrantClient
+from qdrant_client.models import Distance, PointStruct, VectorParams
+
+from nextcloud_mcp_server.document_processors.pymupdf import PyMuPDFProcessor
+from nextcloud_mcp_server.embedding import SimpleEmbeddingProvider
+from nextcloud_mcp_server.vector.document_chunker import (
+    ChunkWithPosition,
+    RecursiveCharacterTextSplitter,
+)
+
+pytestmark = pytest.mark.integration
+
+
+def create_test_pdf() -> bytes:
+    """Create a small test PDF with multiple pages."""
+    doc = pymupdf.open()
+
+    # Page 1: Introduction
+    page1 = doc.new_page(width=595, height=842)  # A4 size
+    page1.insert_text(
+        (50, 50),
+        "Nextcloud Administration Guide\n\n"
+        "Chapter 1: Introduction\n\n"
+        "Nextcloud is a self-hosted file sharing and collaboration platform. "
+        "It provides secure file storage, sharing, and synchronization across devices. "
+        "This guide covers installation, configuration, and maintenance of Nextcloud.",
+    )
+
+    # Page 2: Installation
+    page2 = doc.new_page(width=595, height=842)
+    page2.insert_text(
+        (50, 50),
+        "Chapter 2: Installation\n\n"
+        "System Requirements:\n"
+        "- PHP 8.0 or higher\n"
+        "- MySQL 8.0 or MariaDB 10.5\n"
+        "- Apache or Nginx web server\n\n"
+        "Installation steps:\n"
+        "1. Download Nextcloud package\n"
+        "2. Extract to web server directory\n"
+        "3. Configure database connection\n"
+        "4. Run installation wizard",
+    )
+
+    # Page 3: Configuration
+    page3 = doc.new_page(width=595, height=842)
+    page3.insert_text(
+        (50, 50),
+        "Chapter 3: Configuration\n\n"
+        "Database Configuration:\n"
+        "Edit config/config.php to set database parameters. "
+        "Configure database host, username, password, and database name. "
+        "For optimal performance, use MySQL or MariaDB.\n\n"
+        "Security Settings:\n"
+        "Enable HTTPS, configure trusted domains, and set up firewall rules.",
+    )
+
+    # Convert to bytes
+    pdf_bytes = doc.tobytes()
+    doc.close()
+
+    return pdf_bytes
+
+
+@pytest.fixture
+async def simple_embedding_provider():
+    """Simple in-process embedding provider for testing."""
+    return SimpleEmbeddingProvider(dimension=384)
+
+
+@pytest.fixture
+async def qdrant_test_client():
+    """Qdrant client for testing (in-memory)."""
+    client = AsyncQdrantClient(":memory:")
+    yield client
+    await client.close()
+
+
+@pytest.fixture
+async def test_collection(qdrant_test_client: AsyncQdrantClient):
+    """Create test collection in Qdrant."""
+    collection_name = "test_pdf_indexing"
+
+    # Create collection
+    await qdrant_test_client.create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=384, distance=Distance.COSINE),
+    )
+
+    yield collection_name
+
+    # Cleanup
+    try:
+        await qdrant_test_client.delete_collection(collection_name)
+    except Exception:
+        pass
+
+
+@pytest.fixture
+def pymupdf_processor():
+    """PyMuPDF processor for testing (without image extraction)."""
+    return PyMuPDFProcessor(extract_images=False)
+
+
+async def test_pymupdf_processor_extracts_text_and_metadata(pymupdf_processor):
+    """Test PyMuPDF processor extracts text and metadata from PDF."""
+    pdf_bytes = create_test_pdf()
+
+    result = await pymupdf_processor.process(
+        content=pdf_bytes,
+        content_type="application/pdf",
+        filename="test-admin-guide.pdf",
+    )
+
+    # Verify result structure
+    assert result.success is True
+    assert result.processor == "pymupdf"
+    assert result.text is not None
+    assert len(result.text) > 0
+
+    # Verify extracted text contains expected content
+    assert "Nextcloud Administration Guide" in result.text
+    assert "Chapter 1: Introduction" in result.text
+    assert "Chapter 2: Installation" in result.text
+    assert "Chapter 3: Configuration" in result.text
+    assert "PHP 8.0 or higher" in result.text
+    assert "MySQL" in result.text
+
+    # Verify metadata
+    assert result.metadata is not None
+    assert result.metadata["page_count"] == 3
+    assert result.metadata["filename"] == "test-admin-guide.pdf"
+    assert "format" in result.metadata
+
+
+async def test_document_chunker_preserves_page_numbers():
+    """Test that document chunker can handle chunks with page number metadata."""
+    # Create chunks with page numbers
+    chunks = [
+        ChunkWithPosition(
+            text="Chapter 1 content on page 1",
+            start_offset=0,
+            end_offset=28,
+            page_number=1,
+        ),
+        ChunkWithPosition(
+            text="Chapter 2 content on page 2",
+            start_offset=29,
+            end_offset=57,
+            page_number=2,
+        ),
+        ChunkWithPosition(
+            text="Chapter 3 content on page 3",
+            start_offset=58,
+            end_offset=86,
+            page_number=3,
+        ),
+    ]
+
+    # Verify page numbers are preserved
+    assert chunks[0].page_number == 1
+    assert chunks[1].page_number == 2
+    assert chunks[2].page_number == 3
+
+
+async def test_pdf_indexing_and_search_flow(
+    pymupdf_processor: PyMuPDFProcessor,
+    qdrant_test_client: AsyncQdrantClient,
+    test_collection: str,
+    simple_embedding_provider: SimpleEmbeddingProvider,
+):
+    """Test complete PDF indexing and semantic search flow."""
+
+    # Step 1: Process PDF with PyMuPDF
+    pdf_bytes = create_test_pdf()
+    result = await pymupdf_processor.process(
+        content=pdf_bytes,
+        content_type="application/pdf",
+        filename="/Documents/admin-guide.pdf",
+    )
+
+    assert result.success is True
+    assert result.metadata["page_count"] == 3
+
+    # Step 2: Chunk the extracted text
+    # Note: In real implementation, we'd track which chunk came from which page
+    # For this test, we'll simulate by creating chunks manually
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    chunks = splitter.split_text(result.text)
+
+    assert len(chunks) > 0
+
+    # Step 3: Index chunks into Qdrant with PDF metadata
+    points = []
+    for idx, chunk_text in enumerate(chunks):
+        embedding = await simple_embedding_provider.embed(chunk_text)
+
+        # Simulate page number assignment (in real implementation, this would be tracked)
+        # For simplicity, assign page based on content
+        page_number = 1
+        if "Chapter 2" in chunk_text or "Installation" in chunk_text:
+            page_number = 2
+        elif "Chapter 3" in chunk_text or "Configuration" in chunk_text:
+            page_number = 3
+
+        points.append(
+            PointStruct(
+                id=idx,
+                vector=embedding,
+                payload={
+                    "user_id": "admin",
+                    "doc_id": "/Documents/admin-guide.pdf",
+                    "doc_type": "file",
+                    "title": "Nextcloud Administration Guide",
+                    "file_path": "/Documents/admin-guide.pdf",
+                    "mime_type": "application/pdf",
+                    "page_number": page_number,
+                    "page_count": result.metadata["page_count"],
+                    "chunk_index": idx,
+                    "excerpt": chunk_text[:200],
+                },
+            )
+        )
+
+    await qdrant_test_client.upsert(
+        collection_name=test_collection, points=points, wait=True
+    )
+
+    # Step 4: Perform semantic search for installation instructions
+    query = "how to install Nextcloud system requirements"
+    query_embedding = await simple_embedding_provider.embed(query)
+
+    response = await qdrant_test_client.query_points(
+        collection_name=test_collection,
+        query=query_embedding,
+        limit=3,
+        score_threshold=0.0,
+    )
+
+    # Verify search results
+    assert len(response.points) > 0
+
+    # Top result should be from installation chapter (page 2)
+    top_result = response.points[0]
+    assert top_result.payload["doc_type"] == "file"
+    assert top_result.payload["file_path"] == "/Documents/admin-guide.pdf"
+    assert (
+        "Installation" in top_result.payload["excerpt"]
+        or top_result.payload["page_number"] == 2
+    )
+
+    # Verify page number is preserved
+    assert top_result.payload["page_number"] in [1, 2, 3]
+    assert top_result.payload["page_count"] == 3
+
+    # Step 5: Search for configuration
+    query = "database configuration settings MySQL"
+    query_embedding = await simple_embedding_provider.embed(query)
+
+    response = await qdrant_test_client.query_points(
+        collection_name=test_collection,
+        query=query_embedding,
+        limit=3,
+        score_threshold=0.0,
+    )
+
+    assert len(response.points) > 0
+
+    # Should find configuration chapter (page 3)
+    found_config = any(
+        "Configuration" in r.payload["excerpt"] or r.payload["page_number"] == 3
+        for r in response.points[:2]
+    )
+    assert found_config
+
+
+async def test_pdf_search_with_filters(
+    pymupdf_processor: PyMuPDFProcessor,
+    qdrant_test_client: AsyncQdrantClient,
+    test_collection: str,
+    simple_embedding_provider: SimpleEmbeddingProvider,
+):
+    """Test PDF search with metadata filters."""
+    from qdrant_client.models import FieldCondition, Filter, MatchValue
+
+    # Process and index PDF
+    pdf_bytes = create_test_pdf()
+    result = await pymupdf_processor.process(
+        content=pdf_bytes,
+        content_type="application/pdf",
+        filename="/Documents/admin-guide.pdf",
+    )
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    chunks = splitter.split_text(result.text)
+
+    # Index with metadata
+    points = []
+    for idx, chunk_text in enumerate(chunks):
+        embedding = await simple_embedding_provider.embed(chunk_text)
+
+        points.append(
+            PointStruct(
+                id=idx,
+                vector=embedding,
+                payload={
+                    "user_id": "admin",
+                    "doc_id": "/Documents/admin-guide.pdf",
+                    "doc_type": "file",
+                    "mime_type": "application/pdf",
+                    "excerpt": chunk_text[:200],
+                },
+            )
+        )
+
+    await qdrant_test_client.upsert(
+        collection_name=test_collection, points=points, wait=True
+    )
+
+    # Search with filter for PDFs only
+    query = "Nextcloud installation"
+    query_embedding = await simple_embedding_provider.embed(query)
+
+    response = await qdrant_test_client.query_points(
+        collection_name=test_collection,
+        query=query_embedding,
+        query_filter=Filter(
+            must=[FieldCondition(key="doc_type", match=MatchValue(value="file"))]
+        ),
+        limit=3,
+    )
+
+    # All results should be from file documents
+    assert len(response.points) > 0
+    for result in response.points:
+        assert result.payload["doc_type"] == "file"
+        assert result.payload["mime_type"] == "application/pdf"
+
+
+async def test_pymupdf_health_check(pymupdf_processor: PyMuPDFProcessor):
+    """Test PyMuPDF processor health check."""
+    is_healthy = await pymupdf_processor.health_check()
+    assert is_healthy is True
+
+
+async def test_pymupdf_supports_pdf_mime_type(pymupdf_processor: PyMuPDFProcessor):
+    """Test PyMuPDF processor declares PDF support."""
+    assert "application/pdf" in pymupdf_processor.supported_mime_types
+    assert pymupdf_processor.name == "pymupdf"

tests/unit/client/test_webdav.py

@@ -0,0 +1,119 @@
+"""Unit tests for WebDAV client."""
+
+from unittest.mock import AsyncMock
+
+import pytest
+
+from nextcloud_mcp_server.client.webdav import WebDAVClient
+
+
+@pytest.mark.unit
+async def test_find_by_tag_calls_search_files(mocker):
+    """Test that find_by_tag constructs correct search query."""
+    # Create mock HTTP client
+    mock_http_client = AsyncMock()
+
+    # Create WebDAVClient instance
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock the search_files method to avoid actual HTTP calls
+    mock_search_files = mocker.patch.object(client, "search_files", return_value=[])
+
+    # Call find_by_tag
+    await client.find_by_tag("vector-index")
+
+    # Verify search_files was called with correct parameters
+    mock_search_files.assert_called_once()
+    call_args = mock_search_files.call_args
+
+    # Check that the where_conditions contains the tag name
+    assert "vector-index" in call_args.kwargs["where_conditions"]
+    assert "<oc:tags/>" in call_args.kwargs["where_conditions"]
+    assert "<d:like>" in call_args.kwargs["where_conditions"]
+
+    # Check that tags property is requested
+    assert "tags" in call_args.kwargs["properties"]
+
+
+@pytest.mark.unit
+async def test_find_by_tag_with_scope_and_limit(mocker):
+    """Test find_by_tag passes scope and limit parameters."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    mock_search_files = mocker.patch.object(client, "search_files", return_value=[])
+
+    # Call with scope and limit
+    await client.find_by_tag("test-tag", scope="Documents", limit=10)
+
+    # Verify parameters were passed through
+    call_args = mock_search_files.call_args
+    assert call_args.kwargs["scope"] == "Documents"
+    assert call_args.kwargs["limit"] == 10
+
+
+@pytest.mark.unit
+def test_parse_search_response_with_tags(mocker):
+    """Test that _parse_search_response correctly parses tags."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock XML response with tags (comma-separated format)
+    xml_content = b"""<?xml version="1.0"?>
+    <d:multistatus xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+        <d:response>
+            <d:href>/remote.php/dav/files/testuser/Documents/test.pdf</d:href>
+            <d:propstat>
+                <d:prop>
+                    <d:displayname>test.pdf</d:displayname>
+                    <d:getcontenttype>application/pdf</d:getcontenttype>
+                    <d:getcontentlength>1024</d:getcontentlength>
+                    <d:getetag>"abc123"</d:getetag>
+                    <oc:fileid>12345</oc:fileid>
+                    <oc:tags>vector-index,important</oc:tags>
+                    <d:resourcetype/>
+                </d:prop>
+            </d:propstat>
+        </d:response>
+    </d:multistatus>"""
+
+    # Parse the response
+    results = client._parse_search_response(xml_content, scope="Documents")
+
+    # Verify tags were parsed correctly
+    assert len(results) == 1
+    assert "tags" in results[0]
+    assert results[0]["tags"] == ["vector-index", "important"]
+    assert results[0]["name"] == "test.pdf"
+    assert results[0]["content_type"] == "application/pdf"
+
+
+@pytest.mark.unit
+def test_parse_search_response_with_empty_tags(mocker):
+    """Test that _parse_search_response handles files without tags."""
+    mock_http_client = AsyncMock()
+    client = WebDAVClient(mock_http_client, "testuser")
+
+    # Mock XML response without tags
+    xml_content = b"""<?xml version="1.0"?>
+    <d:multistatus xmlns:d="DAV:" xmlns:oc="http://owncloud.org/ns">
+        <d:response>
+            <d:href>/remote.php/dav/files/testuser/Documents/test.txt</d:href>
+            <d:propstat>
+                <d:prop>
+                    <d:displayname>test.txt</d:displayname>
+                    <d:getcontenttype>text/plain</d:getcontenttype>
+                    <oc:tags/>
+                    <d:resourcetype/>
+                </d:prop>
+            </d:propstat>
+        </d:response>
+    </d:multistatus>"""
+
+    # Parse the response
+    results = client._parse_search_response(xml_content, scope="Documents")
+
+    # Verify tags field is empty list
+    assert len(results) == 1
+    assert "tags" in results[0]
+    assert results[0]["tags"] == []

tests/unit/test_document_chunker.py

@@ -9,12 +9,12 @@ from nextcloud_mcp_server.vector.document_chunker import (
 class TestDocumentChunkerPositions:
     """Test suite for DocumentChunker position tracking functionality."""
 
-    def test_single_chunk_simple_text(self):
+    async def test_single_chunk_simple_text(self):
         """Test that single-chunk documents return correct positions."""
         chunker = DocumentChunker(chunk_size=2048, overlap=200)
         content = "This is a short document."
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         assert len(chunks) == 1
         assert isinstance(chunks[0], ChunkWithPosition)
@@ -22,7 +22,7 @@ class TestDocumentChunkerPositions:
         assert chunks[0].start_offset == 0
         assert chunks[0].end_offset == len(content)
 
-    def test_multiple_chunks_positions(self):
+    async def test_multiple_chunks_positions(self):
         """Test that multi-chunk documents have correct positions."""
         # Use small chunk size to force multiple chunks
         chunker = DocumentChunker(chunk_size=50, overlap=10)
@@ -34,7 +34,7 @@ class TestDocumentChunkerPositions:
             "This is the fourth sentence adding more context."
         )
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Verify we got multiple chunks
         assert len(chunks) > 1
@@ -61,12 +61,12 @@ class TestDocumentChunkerPositions:
             extracted = content[chunk.start_offset : chunk.end_offset]
             assert extracted == chunk.text
 
-    def test_chunk_positions_with_whitespace(self):
+    async def test_chunk_positions_with_whitespace(self):
         """Test position tracking with various whitespace."""
         chunker = DocumentChunker(chunk_size=30, overlap=5)
         content = "First sentence here.  Second sentence.\n\nThird sentence.\tFourth sentence."
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Verify positions correctly handle whitespace
         for chunk in chunks:
@@ -75,19 +75,19 @@ class TestDocumentChunkerPositions:
             # LangChain strips whitespace by default
             assert len(chunk.text.strip()) > 0
 
-    def test_empty_content(self):
+    async def test_empty_content(self):
         """Test that empty content returns empty chunk."""
         chunker = DocumentChunker(chunk_size=2048, overlap=200)
         content = ""
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         assert len(chunks) == 1
         assert chunks[0].text == ""
         assert chunks[0].start_offset == 0
         assert chunks[0].end_offset == 0
 
-    def test_chunk_overlap_positions(self):
+    async def test_chunk_overlap_positions(self):
         """Test that overlapping chunks have correct positions."""
         chunker = DocumentChunker(chunk_size=50, overlap=15)
         content = (
@@ -97,7 +97,7 @@ class TestDocumentChunkerPositions:
             "This is sentence four adding details."
         )
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Verify overlap exists if we have multiple chunks
         if len(chunks) > 1:
@@ -112,14 +112,14 @@ class TestDocumentChunkerPositions:
                 # With overlap, next chunk may start before current ends
                 assert next_chunk.start_offset <= current_chunk.end_offset
 
-    def test_unicode_content_positions(self):
+    async def test_unicode_content_positions(self):
         """Test position tracking with Unicode characters."""
         chunker = DocumentChunker(chunk_size=50, overlap=10)
         content = (
             "Hello 世界. こんにちは there. мир Привет world. שלום مرحبا 你好 friend."
         )
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Verify all chunks extract correctly
         for chunk in chunks:
@@ -131,7 +131,7 @@ class TestDocumentChunkerPositions:
             assert chunks[0].start_offset == 0
             assert chunks[0].end_offset == len(content)
 
-    def test_realistic_note_content(self):
+    async def test_realistic_note_content(self):
         """Test with realistic note content similar to Nextcloud Notes."""
         chunker = DocumentChunker(chunk_size=200, overlap=50)
         content = """My Project Notes
@@ -152,7 +152,7 @@ position tracking for each chunk.
 This allows us to highlight the exact chunk that matched a search query,
 which builds trust in the RAG system."""
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Should have multiple chunks
         assert len(chunks) > 1
@@ -168,7 +168,7 @@ which builds trust in the RAG system."""
             assert chunk.end_offset <= len(content)
             assert chunk.start_offset < chunk.end_offset
 
-    def test_semantic_boundary_preservation(self):
+    async def test_semantic_boundary_preservation(self):
         """Test that LangChain creates semantically coherent chunks."""
         chunker = DocumentChunker(chunk_size=100, overlap=20)
         content = (
@@ -178,7 +178,7 @@ which builds trust in the RAG system."""
             "Fourth sentence ends."
         )
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Verify all chunks are extractable using their positions
         for chunk in chunks:
@@ -193,7 +193,7 @@ which builds trust in the RAG system."""
             assert chunk.end_offset <= len(content)
             assert chunk.start_offset < chunk.end_offset
 
-    def test_paragraph_boundary_preservation(self):
+    async def test_paragraph_boundary_preservation(self):
         """Test that LangChain preserves paragraph boundaries."""
         chunker = DocumentChunker(chunk_size=80, overlap=15)
         content = """First paragraph here.
@@ -204,7 +204,7 @@ Third paragraph here.
 
 Fourth paragraph here."""
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # LangChain should prefer splitting at paragraph boundaries (\n\n)
         # Verify we got multiple chunks
@@ -215,7 +215,7 @@ Fourth paragraph here."""
             extracted = content[chunk.start_offset : chunk.end_offset]
             assert extracted == chunk.text
 
-    def test_default_parameters(self):
+    async def test_default_parameters(self):
         """Test that default parameters work correctly."""
         chunker = DocumentChunker()  # Use defaults: 2048 chars, 200 overlap
 
@@ -224,14 +224,14 @@ Fourth paragraph here."""
             "This is a short note with a few sentences. It should fit in one chunk."
         )
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         assert len(chunks) == 1
         assert chunks[0].text == content
         assert chunks[0].start_offset == 0
         assert chunks[0].end_offset == len(content)
 
-    def test_large_document_chunking(self):
+    async def test_large_document_chunking(self):
         """Test chunking of a large document."""
         chunker = DocumentChunker(chunk_size=100, overlap=20)
 
@@ -244,7 +244,7 @@ Fourth paragraph here."""
         ]
         content = "\n\n".join(paragraphs)
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         # Should create multiple chunks
         assert len(chunks) > 1
@@ -261,12 +261,12 @@ Fourth paragraph here."""
         assert chunks[0].start_offset == 0
         assert chunks[-1].end_offset == len(content)
 
-    def test_position_tracking_with_overlap(self):
+    async def test_position_tracking_with_overlap(self):
         """Test that position tracking works correctly with overlap."""
         chunker = DocumentChunker(chunk_size=50, overlap=15)
         content = "A" * 25 + ". " + "B" * 25 + ". " + "C" * 25 + ". " + "D" * 25 + "."
 
-        chunks = chunker.chunk_text(content)
+        chunks = await chunker.chunk_text(content)
 
         if len(chunks) > 1:
             # Verify overlap creates correct positions

tools/parse-doc.py

@@ -0,0 +1,41 @@
+import logging
+import pathlib
+
+import anyio
+import pymupdf
+import pymupdf.layout
+
+from nextcloud_mcp_server.client import NextcloudClient
+
+pymupdf.layout.activate()
+import pymupdf4llm  # noqa: E402
+
+client = NextcloudClient.from_env()
+logger = logging.getLogger(__name__)
+
+TMP_DIR = pathlib.Path("/tmp/tmp-images")
+TMP_DIR.mkdir(exist_ok=True, parents=True)
+
+
+async def print_markdown(filename):
+    content, _ = await client.webdav.read_file(filename)
+    doc = pymupdf.open("pdf", content)
+    md_text = pymupdf4llm.to_markdown(doc, write_images=True, image_path=str(TMP_DIR))
+    print(md_text)
+
+
+async def run1():
+    response = await client.webdav.find_by_type("application/pdf")
+    # print(response)
+    for file in response:
+        await print_markdown(file["path"])
+
+
+async def run():
+    tags = await client.tags.get_all_tags()
+    print(tags)
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level="INFO")
+    anyio.run(run)

uv.lock

@@ -1645,7 +1645,7 @@ wheels = [
 
 [[package]]
 name = "mcp"
-version = "1.21.1"
+version = "1.22.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -1663,9 +1663,9 @@ dependencies = [
     { name = "typing-inspection" },
     { name = "uvicorn", marker = "sys_platform != 'emscripten'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f7/25/4df633e7574254ada574822db2245bbee424725d1b01bccae10bf128794e/mcp-1.21.1.tar.gz", hash = "sha256:540e6ac4b12b085c43f14879fde04cbdb10148a09ea9492ff82d8c7ba651a302", size = 469071, upload-time = "2025-11-13T20:33:46.139Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/a3/a2/c5ec0ab38b35ade2ae49a90fada718fbc76811dc5aa1760414c6aaa6b08a/mcp-1.22.0.tar.gz", hash = "sha256:769b9ac90ed42134375b19e777a2858ca300f95f2e800982b3e2be62dfc0ba01", size = 471788, upload-time = "2025-11-20T20:11:28.095Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/49/af/01fb42df59ad15925ffc1e2e609adafddd3ac4572f606faae0dc8b55ba0c/mcp-1.21.1-py3-none-any.whl", hash = "sha256:dd35abe36d68530a8a1291daa25d50276d8731e545c0434d6e250a3700dd2a6d", size = 174852, upload-time = "2025-11-13T20:33:44.502Z" },
+    { url = "https://files.pythonhosted.org/packages/a9/bb/711099f9c6bb52770f56e56401cdfb10da5b67029f701e0df29362df4c8e/mcp-1.22.0-py3-none-any.whl", hash = "sha256:bed758e24df1ed6846989c909ba4e3df339a27b4f30f1b8b627862a4bade4e98", size = 175489, upload-time = "2025-11-20T20:11:26.542Z" },
 ]
 
 [package.optional-dependencies]
@@ -1925,9 +1925,18 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6c/28/dd72947e59a6a8c856448a5e74da6201cb5502ddff644fbc790e4bd40b9a/multiprocess-0.70.18-py39-none-any.whl", hash = "sha256:e78ca805a72b1b810c690b6b4cc32579eba34f403094bbbae962b7b5bf9dfcb8", size = 133478, upload-time = "2025-04-17T03:11:26.253Z" },
 ]
 
+[[package]]
+name = "networkx"
+version = "3.5"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/4f/ccdb8ad3a38e583f214547fd2f7ff1fc160c43a75af88e6aec213404b96a/networkx-3.5.tar.gz", hash = "sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037", size = 2471065, upload-time = "2025-05-29T11:35:07.804Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/eb/8d/776adee7bbf76365fdd7f2552710282c79a4ead5d2a46408c9043a2b70ba/networkx-3.5-py3-none-any.whl", hash = "sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec", size = 2034406, upload-time = "2025-05-29T11:35:04.961Z" },
+]
+
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.44.0"
+version = "0.45.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },
@@ -1952,6 +1961,9 @@ dependencies = [
     { name = "prometheus-client" },
     { name = "pydantic" },
     { name = "pyjwt", extra = ["crypto"] },
+    { name = "pymupdf" },
+    { name = "pymupdf-layout" },
+    { name = "pymupdf4llm" },
     { name = "python-json-logger" },
     { name = "pythonvcard4" },
     { name = "qdrant-client" },
@@ -1986,7 +1998,7 @@ requires-dist = [
     { name = "icalendar", specifier = ">=6.0.0,<7.0.0" },
     { name = "jinja2", specifier = ">=3.1.6" },
     { name = "langchain-text-splitters", specifier = ">=1.0.0" },
-    { name = "mcp", extras = ["cli"], specifier = ">=1.21,<1.22" },
+    { name = "mcp", extras = ["cli"], specifier = ">=1.22,<1.23" },
     { name = "opentelemetry-api", specifier = ">=1.28.2" },
     { name = "opentelemetry-exporter-otlp-proto-grpc", specifier = ">=1.28.2" },
     { name = "opentelemetry-instrumentation-asgi", specifier = ">=0.49b2" },
@@ -1997,6 +2009,9 @@ requires-dist = [
     { name = "prometheus-client", specifier = ">=0.21.0" },
     { name = "pydantic", specifier = ">=2.11.4" },
     { name = "pyjwt", extras = ["crypto"], specifier = ">=2.8.0" },
+    { name = "pymupdf", specifier = ">=1.26.6" },
+    { name = "pymupdf-layout", specifier = ">=1.26.6" },
+    { name = "pymupdf4llm", specifier = ">=0.2.2" },
     { name = "python-json-logger", specifier = ">=3.2.0" },
     { name = "pythonvcard4", specifier = ">=0.2.0" },
     { name = "qdrant-client", specifier = ">=1.7.0" },
@@ -2969,6 +2984,52 @@ crypto = [
     { name = "cryptography" },
 ]
 
+[[package]]
+name = "pymupdf"
+version = "1.26.6"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ec/d7/a6f0e03a117fa2ad79c4b898203bb212b17804f92558a6a339298faca7bb/pymupdf-1.26.6.tar.gz", hash = "sha256:a2b4531cd4ab36d6f1f794bb6d3c33b49bda22f36d58bb1f3e81cbc10183bd2b", size = 84322494, upload-time = "2025-11-05T15:20:46.786Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9e/5c/dec354eee5fe4966c715f33818ed4193e0e6c986cf8484de35b6c167fb8e/pymupdf-1.26.6-cp310-abi3-macosx_10_9_x86_64.whl", hash = "sha256:e46f320a136ad55e5219e8f0f4061bdf3e4c12b126d2740d5a49f73fae7ea176", size = 23178988, upload-time = "2025-11-05T14:31:19.834Z" },
+    { url = "https://files.pythonhosted.org/packages/ec/a0/11adb742d18142bd623556cd3b5d64649816decc5eafd30efc9498657e76/pymupdf-1.26.6-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:6844cd2396553c0fa06de4869d5d5ecb1260e6fc3b9d85abe8fa35f14dd9d688", size = 22469764, upload-time = "2025-11-05T14:32:34.654Z" },
+    { url = "https://files.pythonhosted.org/packages/e4/c8/377cf20e31f58d4c243bfcf2d3cb7466d5b97003b10b9f1161f11eb4a994/pymupdf-1.26.6-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:617ba69e02c44f0da1c0e039ea4a26cf630849fd570e169c71daeb8ac52a81d6", size = 23502227, upload-time = "2025-11-06T11:03:56.934Z" },
+    { url = "https://files.pythonhosted.org/packages/4f/bf/6e02e3d84b32c137c71a0a3dcdba8f2f6e9950619a3bc272245c7c06a051/pymupdf-1.26.6-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:7777d0b7124c2ebc94849536b6a1fb85d158df3b9d873935e63036559391534c", size = 24115381, upload-time = "2025-11-05T14:33:54.338Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/9d/30f7fcb3776bfedde66c06297960debe4883b1667294a1ee9426c942e94d/pymupdf-1.26.6-cp310-abi3-win32.whl", hash = "sha256:8f3ef05befc90ca6bb0f12983200a7048d5bff3e1c1edef1bb3de60b32cb5274", size = 17203613, upload-time = "2025-11-05T17:19:47.494Z" },
+    { url = "https://files.pythonhosted.org/packages/f9/e8/989f4eaa369c7166dc24f0eaa3023f13788c40ff1b96701f7047421554a8/pymupdf-1.26.6-cp310-abi3-win_amd64.whl", hash = "sha256:ce02ca96ed0d1acfd00331a4d41a34c98584d034155b06fd4ec0f051718de7ba", size = 18405680, upload-time = "2025-11-05T14:34:48.672Z" },
+]
+
+[[package]]
+name = "pymupdf-layout"
+version = "1.26.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "networkx" },
+    { name = "numpy" },
+    { name = "onnxruntime" },
+    { name = "pymupdf" },
+    { name = "pyyaml" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/70/86/31f8d05b36ebf43cca88d5c6415de46eb748e487b618a589671a610be8c8/pymupdf_layout-1.26.6-cp310-abi3-macosx_10_9_x86_64.whl", hash = "sha256:d632f83208db8b24600eb8ac54d3135fab6ab1f251a38fa6061e7470e81b9481", size = 12727222, upload-time = "2025-11-05T14:35:44.367Z" },
+    { url = "https://files.pythonhosted.org/packages/ff/d3/0e52d7d1e2f975843f5354ac3b210a98471b690105efc332d3c285bd794b/pymupdf_layout-1.26.6-cp310-abi3-macosx_11_0_arm64.whl", hash = "sha256:f1d45f72ec08ef7f644928487e7a067df6df63172d682d0bb05158896d0d9c71", size = 12725266, upload-time = "2025-11-05T14:36:50.727Z" },
+    { url = "https://files.pythonhosted.org/packages/ae/49/ad1a5edccc45477493d6a53a41df7620d6147febb897c3dd8354f413e154/pymupdf_layout-1.26.6-cp310-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:0561b9485a6ac1a40bb1e2ec7a1648aa64e4be56dab2f39182b11a69e3e43024", size = 12732580, upload-time = "2025-11-06T11:04:09.065Z" },
+    { url = "https://files.pythonhosted.org/packages/a7/bd/3e049b359dd0c3a101ae915484b87ff73bfdedfb24a924e0a8e6783b33f3/pymupdf_layout-1.26.6-cp310-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:ee8e2bfed12d4b6421b27a1f89837ac09d8bc3f783f79670db397ec24614bf3d", size = 12732539, upload-time = "2025-11-05T14:38:01.244Z" },
+    { url = "https://files.pythonhosted.org/packages/f8/7a/69078bf16669f8361360321ea6bede4cbfede35bf3f4ca5842a7c2387825/pymupdf_layout-1.26.6-cp310-abi3-win_amd64.whl", hash = "sha256:2305aac24fd6e12217afaaea8ec95be297be9b250b6077a3f4e92f7f9beeaf92", size = 12734904, upload-time = "2025-11-05T14:39:05.83Z" },
+]
+
+[[package]]
+name = "pymupdf4llm"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pymupdf" },
+    { name = "tabulate" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ec/26/e1226c5329d0c901cd42649e4e8d7544636524c31e95a84f4dcf7c25731d/pymupdf4llm-0.2.2.tar.gz", hash = "sha256:d8dee8451e31ec39daf691687403bf2a98ac7e7b8709400a4e13a582eab835c6", size = 59501, upload-time = "2025-11-17T11:10:20.204Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/26/23/08be1528f3ccb8c245e9a7b247255d6853a8e162b1451f4888f2006c52f0/pymupdf4llm-0.2.2-py3-none-any.whl", hash = "sha256:e7777d083f5f7c7daa804c3423804c309a7e096d682773c01e9dd4bb060f4a56", size = 62063, upload-time = "2025-11-17T11:10:22.452Z" },
+]
+
 [[package]]
 name = "pyreadline3"
 version = "3.5.4"
@@ -3553,6 +3614,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353, upload-time = "2025-04-27T18:04:59.103Z" },
 ]
 
+[[package]]
+name = "tabulate"
+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090, upload-time = "2022-10-06T17:21:48.54Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252, upload-time = "2022-10-06T17:21:44.262Z" },
+]
+
 [[package]]
 name = "tenacity"
 version = "9.1.2"