feat: Replace custom document chunker with LangChain MarkdownTextSplitter

Migrates from custom word-based chunking to LangChain's MarkdownTextSplitter
for better semantic search quality. This implements the chunking portion of
ADR-011.

Changes:
- Replace custom regex word chunker with MarkdownTextSplitter
- Optimized for Markdown content (headers, code blocks, lists)
- Convert from word-based (512 words) to character-based (2048 chars) chunking
- Maintain backward-compatible ChunkWithPosition interface
- Update configuration defaults and validation
- Update all unit tests (12/12 passing)

Benefits:
- Respects markdown structure boundaries
- Never breaks code blocks or headers mid-chunk
- Preserves semantic coherence within chunks
- Expected 20-30% improvement in recall quality
- Industry-standard approach (used by production RAG systems)

Note: Full reindex required to apply new chunking to existing documents.
Current vector database still contains old word-based chunks.

Related: ADR-011 (Improving Semantic Search Quality)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

.dockerignore

@@ -5,3 +5,4 @@
 !uv.lock
 
 !nextcloud_mcp_server/**/*.py
+!nextcloud_mcp_server/**/*.html

.github/workflows/test.yml

@@ -9,7 +9,7 @@ jobs:
   linting:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
       - name: Install the latest version of uv
         uses: astral-sh/setup-uv@5a7eac68fb9809dea845d802897dc5c723910fa3 # v7.1.3
       - name: Check format
@@ -27,7 +27,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
+      - uses: actions/checkout@93cb6efe18208431cddfb8368fd83d5badbf9bfd # v5.0.1
         with:
           submodules: 'true'
 
@@ -85,4 +85,4 @@ jobs:
           NEXTCLOUD_USERNAME: "admin"
           NEXTCLOUD_PASSWORD: "admin"
         run: |
-          uv run pytest -v --log-cli-level=WARN -m smoke
+          uv run pytest -v --log-cli-level=WARN -m unit -m smoke

.gitmodules

@@ -1,6 +1,6 @@
-[submodule "oidc"]
-	path = third_party/oidc
-	url = https://github.com/cbcoutinho/oidc
 [submodule "third_party/oidc"]
 	path = third_party/oidc
 	url = https://github.com/cbcoutinho/oidc
+[submodule "third_party/notes"]
+	path = third_party/notes
+	url = https://github.com/cbcoutinho/notes

CHANGELOG.md

@@ -1,3 +1,32 @@
+## v0.42.0 (2025-11-17)
+
+### Feat
+
+- **viz**: Add dual-score display and improve UI controls
+
+## v0.41.0 (2025-11-17)
+
+### Feat
+
+- add configurable fusion algorithms for BM25 hybrid search
+- add chunk position tracking to vector indexing and search
+- add vector viz template and chunk context endpoint
+
+### Fix
+
+- prevent infinite loop in DocumentChunker with position tracking
+- Relax SearchResult validation to support DBSF fusion scores > 1.0
+
+## v0.40.0 (2025-11-16)
+
+### Feat
+
+- add unified provider architecture with Amazon Bedrock support
+
+### Fix
+
+- suppress Starlette middleware type warnings in ty checker
+
 ## v0.39.0 (2025-11-16)
 
 ### Feat

Dockerfile

@@ -1,19 +1,19 @@
-FROM python:3.12-slim-trixie
+FROM docker.io/library/python:3.12-slim-trixie@sha256:d86b4c74b936c438cd4cc3a9f7256b9a7c27ad68c7caf8c205e18d9845af0164
 
-COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
+COPY --from=ghcr.io/astral-sh/uv:0.9.10 /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 \
-    sqlite3
+    sqlite3 && apt clean
 
 WORKDIR /app
 
 COPY . .
 
-RUN uv sync --locked --no-dev --no-editable
+RUN uv sync --locked --no-dev --no-editable --no-cache
 
 ENV PYTHONUNBUFFERED=1
 ENV VIRTUAL_ENV=/app/.venv

app-hooks/post-installation/10-install-notes-app.sh

@@ -2,4 +2,30 @@
 
 set -euox pipefail
 
-php /var/www/html/occ app:enable notes
+echo "Installing and configuring notes app for testing..."
+
+# Check if development notes app is mounted at /opt/apps/notes
+if [ -d /opt/apps/notes ]; then
+    echo "Development notes app found at /opt/apps/notes"
+
+    # Remove any existing notes app in apps (from app store or old symlink)
+    if [ -e /var/www/html/custom_apps/notes ]; then
+        echo "Removing existing notes in apps..."
+        rm -rf /var/www/html/custom_apps/notes
+    fi
+
+    # Create symlink from apps to the mounted development version
+    # Per Nextcloud docs: apps outside server root need symlinks in server root
+    echo "Creating symlink: custom_apps/notes -> /opt/apps/notes"
+    ln -sf /opt/apps/notes /var/www/html/custom_apps/notes
+
+    echo "Enabling notes app from /opt/apps (development mode via symlink)"
+    php /var/www/html/occ app:enable notes
+elif [ -d /var/www/html/custom_apps/notes ]; then
+    echo "notes app directory found in apps (already installed)"
+    php /var/www/html/occ app:enable notes
+else
+    echo "notes app not found, installing from app store..."
+    php /var/www/html/occ app:install notes
+    php /var/www/html/occ app:enable notes
+fi

charts/nextcloud-mcp-server/Chart.lock

@@ -1,9 +1,9 @@
 dependencies:
 - name: qdrant
   repository: https://qdrant.github.io/qdrant-helm
-  version: 1.15.5
+  version: 1.16.0
 - name: ollama
   repository: https://otwld.github.io/ollama-helm
   version: 1.34.0
-digest: sha256:d51c97d05be2614b751c0dd7267ef7dc959eff5ebef859c5f895c5c554b7a874
-generated: "2025-11-09T17:08:02.86648061Z"
+digest: sha256:9dfb8d6e3d5488f669d4c37f3a766213b598ff3de2aead2c734789736c7835b4
+generated: "2025-11-17T17:08:48.055530019Z"

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: 0.39.0
-appVersion: "0.39.0"
+version: 0.42.0
+appVersion: "0.42.0"
 keywords:
   - nextcloud
   - mcp
@@ -27,7 +27,7 @@ annotations:
   grafana_dashboard_folder: "Nextcloud MCP"
 dependencies:
   - name: qdrant
-    version: "1.15.5"
+    version: "1.16.0"
     repository: https://qdrant.github.io/qdrant-helm
     condition: qdrant.networkMode.deploySubchart
   - name: ollama

docker-compose.yml

@@ -34,7 +34,7 @@ services:
       - ./app-hooks:/docker-entrypoint-hooks.d:ro
       # Mount OIDC development directory outside /var/www/html to avoid rsync conflicts
       # The post-installation hook will register /opt/apps as an additional app directory
-      #- ./third_party:/opt/apps:ro
+      - ./third_party:/opt/apps:ro
     environment:
       - NEXTCLOUD_TRUSTED_DOMAINS=app
       - NEXTCLOUD_ADMIN_USER=admin
@@ -225,7 +225,7 @@ services:
       - keycloak-oauth-storage:/app/.oauth
 
   qdrant:
-    image: qdrant/qdrant:v1.15.5@sha256:0fb8897412abc81d1c0430a899b9a81eb8328aa634e7242d1bc804c1fe8fe863
+    image: qdrant/qdrant:v1.16.0@sha256:1005201498cf927d835383d0f918b17d8c9da7db58550f169f694455e42d78f4
     restart: always
     ports:
       - 127.0.0.1:6333:6333  # REST API

docs/ADR-011-improving-semantic-search-quality.md

@@ -1,7 +1,8 @@
 # ADR-011: Improving Semantic Search Quality Through Better Chunking and Embeddings
 
-**Status**: Proposed
+**Status**: Partially Implemented (Chunking Complete, Embeddings Pending)
 **Date**: 2025-11-12
+**Implementation Date**: 2025-11-18 (Chunking)
 **Authors**: Development Team
 **Related**: ADR-003 (Vector Database Architecture), ADR-008 (MCP Sampling for RAG)
 
@@ -893,3 +894,50 @@ This ADR addresses the root causes of poor semantic search recall:
 - No new infrastructure or ongoing costs
 
 **Next Steps**: Approve ADR → Implement changes → Reindex → Validate → Production rollout
+
+## Implementation Status
+
+### Completed (2025-11-18)
+
+**✅ Semantic Markdown-Aware Chunking (Option C1 + C3 Hybrid)**
+
+Implementation details:
+- Replaced custom word-based chunking with `MarkdownTextSplitter` from LangChain
+- Optimized for Nextcloud Notes markdown content with special handling for:
+  - Headers (`#`, `##`, `###`, etc.)
+  - Code blocks (` ``` `)
+  - Lists (`-`, `*`, `1.`)
+  - Horizontal rules (`---`)
+  - Paragraphs and sentences
+- Maintained `ChunkWithPosition` interface for backward compatibility
+- Updated configuration defaults:
+  - `DOCUMENT_CHUNK_SIZE`: 512 words → 2048 characters
+  - `DOCUMENT_CHUNK_OVERLAP`: 50 words → 200 characters
+- Updated unit tests to verify position tracking and boundary preservation
+- All tests passing with markdown-aware character-based chunking
+
+**Files Modified**:
+- `nextcloud_mcp_server/vector/document_chunker.py` - LangChain integration
+- `nextcloud_mcp_server/config.py` - Character-based defaults
+- `tests/unit/test_document_chunker.py` - Updated test suite
+
+**Dependencies Added**:
+- `langchain-text-splitters>=1.0.0` (already present in `pyproject.toml`)
+
+**Migration Required**:
+- ⚠️ Full reindex required to apply new chunking strategy
+- Existing documents in vector database use old word-based chunks
+- See "Migration Strategy" section above for reindexing process
+
+### Pending
+
+**⏳ Embedding Model Upgrade (Option E1)**
+
+Still to be implemented:
+- Switch from `nomic-embed-text` (768-dim) to `mxbai-embed-large-v1` (1024-dim)
+- Implement dynamic dimension detection in `ollama_provider.py`
+- Create migration script for collection reindexing
+- Run benchmarking to validate improvement
+- Deploy to production with atomic collection swap
+
+**Estimated Timeline**: 1-2 weeks for implementation and validation

docs/ADR-014-bm25-search.md

@@ -147,7 +147,95 @@ This decision consolidates our retrieval logic, eliminates the data consistency
 
 **Benefits Realized:**
 - ✅ Consolidated architecture (single Qdrant database for both dense + sparse)
-- ✅ Native RRF fusion (database-level, more efficient)
+- ✅ Native fusion algorithms (database-level, more efficient)
 - ✅ Industry-standard BM25 (replaces custom keyword search)
 - ✅ Simplified codebase (removed 736 lines of legacy code)
 - ✅ Better relevance (handles both semantic and keyword queries)
+- ✅ Configurable fusion methods (RRF and DBSF)
+
+---
+
+### 7. Fusion Algorithm Options
+
+**Update: 2025-11-16**
+
+The BM25 hybrid search now supports two fusion algorithms for combining dense (semantic) and sparse (BM25) search results:
+
+#### Reciprocal Rank Fusion (RRF)
+
+**Default fusion method.** RRF is a widely-used, well-established algorithm that combines rankings from multiple retrieval systems using the reciprocal rank formula:
+
+```
+RRF(doc) = Σ 1/(k + rank_i(doc))
+```
+
+where `k` is a constant (typically 60) and `rank_i(doc)` is the rank of the document in retrieval system `i`.
+
+**Characteristics:**
+- ✅ **General-purpose**: Works well across diverse query types and document collections
+- ✅ **Rank-based**: Focuses on relative rankings rather than absolute scores
+- ✅ **Established**: Well-tested, documented, and understood in IR literature
+- ✅ **Robust**: Less sensitive to score distribution differences between systems
+
+**When to use RRF:**
+- Default choice for most use cases
+- When you have mixed query types (semantic + keyword)
+- When retrieval systems have very different score ranges
+- When you want predictable, well-understood behavior
+
+#### Distribution-Based Score Fusion (DBSF)
+
+**Alternative fusion method.** DBSF normalizes scores from each retrieval system using distribution statistics before combining them:
+
+1. **Normalization**: For each query, calculates mean (μ) and standard deviation (σ) of scores
+2. **Outlier handling**: Uses μ ± 3σ as normalization bounds
+3. **Fusion**: Sums normalized scores across systems
+
+**Characteristics:**
+- ✅ **Score-aware**: Uses actual relevance scores, not just rankings
+- ✅ **Statistical**: Normalizes based on score distribution properties
+- ⚠️ **Experimental**: Newer algorithm, less battle-tested than RRF
+- ⚠️ **Sensitive**: May behave differently depending on score distributions
+
+**When to use DBSF:**
+- When retrieval systems have vastly different score ranges that RRF doesn't balance well
+- When you want to experiment with score-based (vs rank-based) fusion
+- When statistical normalization better matches your use case
+- For A/B testing against RRF to measure retrieval quality improvements
+
+#### Configuration
+
+Both fusion algorithms are exposed via the `fusion` parameter in MCP tools:
+
+```python
+# Use RRF (default)
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="rrf"  # Can be omitted, RRF is default
+)
+
+# Use DBSF
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="dbsf"
+)
+```
+
+The `nc_semantic_search_answer` tool also supports the `fusion` parameter and passes it through to the underlying search.
+
+#### Future: Configurable Weights
+
+**Current limitation**: Neither RRF nor DBSF currently support per-system weights (e.g., 0.8 for semantic, 0.2 for BM25). This is a Qdrant platform limitation tracked in [qdrant/qdrant#6067](https://github.com/qdrant/qdrant/issues/6067).
+
+When Qdrant adds weight support, the `fusion` parameter can be extended to accept weight configurations:
+
+```python
+# Hypothetical future API
+response = await nc_semantic_search(
+    query="async programming",
+    fusion="rrf",
+    fusion_weights={"dense": 0.7, "sparse": 0.3}  # Not yet implemented
+)
+```
+
+**Recommendation**: Start with RRF (default). If you encounter cases where keyword matches are under- or over-weighted, experiment with DBSF. Monitor [qdrant/qdrant#6067](https://github.com/qdrant/qdrant/issues/6067) for configurable weight support.

nextcloud_mcp_server/app.py

@@ -1478,6 +1478,7 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
         vector_sync_status_fragment,
     )
     from nextcloud_mcp_server.auth.viz_routes import (
+        chunk_context_endpoint,
         vector_visualization_html,
         vector_visualization_search,
     )
@@ -1509,6 +1510,11 @@ def get_app(transport: str = "sse", enabled_apps: list[str] | None = None):
             vector_visualization_search,
             methods=["GET"],
         ),  # /app/vector-viz/search
+        Route(
+            "/chunk-context",
+            chunk_context_endpoint,
+            methods=["GET"],
+        ),  # /app/chunk-context
         # Webhook management routes (admin-only)
         Route("/webhooks", webhook_management_pane, methods=["GET"]),  # /app/webhooks
         Route(

nextcloud_mcp_server/auth/templates/vector_viz.html

@@ -0,0 +1,339 @@
+<style>
+    .viz-card {
+        background: white;
+        border-radius: 8px;
+        padding: 20px;
+        margin-bottom: 20px;
+        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
+    }
+    .viz-controls {
+        margin-bottom: 20px;
+    }
+    .viz-control-row {
+        display: grid;
+        grid-template-columns: 2fr 1fr auto;
+        gap: 12px;
+        margin-bottom: 12px;
+        align-items: end;
+    }
+    .viz-control-group {
+        margin-bottom: 15px;
+    }
+    .viz-control-group label {
+        display: block;
+        margin-bottom: 5px;
+        font-weight: 500;
+        color: #333;
+    }
+    .viz-control-group input[type="text"],
+    .viz-control-group input[type="number"],
+    .viz-control-group select {
+        width: 100%;
+        padding: 8px 12px;
+        border: 1px solid #ddd;
+        border-radius: 4px;
+        font-size: 14px;
+    }
+    .viz-control-group input[type="range"] {
+        width: 100%;
+    }
+    .viz-control-group select[multiple] {
+        min-height: 100px;
+    }
+    .viz-weight-display {
+        display: inline-block;
+        min-width: 40px;
+        text-align: right;
+        color: #666;
+    }
+    .viz-btn {
+        background: #0066cc;
+        color: white;
+        border: none;
+        padding: 10px 20px;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 14px;
+        font-weight: 500;
+    }
+    .viz-btn:hover {
+        background: #0052a3;
+    }
+    .viz-btn-secondary {
+        background: #6c757d;
+        color: white;
+        border: none;
+        padding: 6px 12px;
+        border-radius: 4px;
+        cursor: pointer;
+        font-size: 13px;
+        margin-bottom: 12px;
+    }
+    .viz-btn-secondary:hover {
+        background: #5a6268;
+    }
+    #viz-plot-container {
+        width: 100%;
+        height: 600px;
+        position: relative;
+    }
+    #viz-plot {
+        width: 100%;
+        height: 100%;
+    }
+    .viz-loading {
+        text-align: center;
+        padding: 40px;
+        color: #666;
+    }
+    .viz-loading-overlay {
+        position: absolute;
+        inset: 0;
+        display: flex;
+        align-items: center;
+        justify-content: center;
+        background: white;
+        color: #666;
+    }
+    .viz-no-results {
+        text-align: center;
+        padding: 40px;
+        color: #666;
+        font-style: italic;
+    }
+    .viz-advanced-section {
+        margin-top: 16px;
+        padding: 16px;
+        background: #f8f9fa;
+        border-radius: 4px;
+        border: 1px solid #dee2e6;
+    }
+    .viz-advanced-grid {
+        display: grid;
+        grid-template-columns: 1fr 1fr;
+        gap: 20px;
+    }
+    .viz-info-box {
+        background: #e3f2fd;
+        border-left: 4px solid #2196f3;
+        padding: 12px;
+        margin-bottom: 20px;
+        font-size: 14px;
+    }
+    .chunk-toggle-btn {
+        background: #6c757d;
+        color: white;
+        border: none;
+        padding: 4px 10px;
+        border-radius: 3px;
+        cursor: pointer;
+        font-size: 12px;
+        margin-top: 6px;
+    }
+    .chunk-toggle-btn:hover {
+        background: #5a6268;
+    }
+    .chunk-context {
+        background: #f8f9fa;
+        border: 1px solid #dee2e6;
+        border-radius: 4px;
+        padding: 12px;
+        margin-top: 8px;
+        font-family: monospace;
+        font-size: 13px;
+        line-height: 1.6;
+        white-space: pre-wrap;
+        word-wrap: break-word;
+    }
+    .chunk-text {
+        color: #666;
+    }
+    .chunk-matched {
+        background: #fff3cd;
+        border: 1px solid #ffc107;
+        padding: 2px 4px;
+        border-radius: 2px;
+        font-weight: 500;
+        color: #333;
+    }
+    .chunk-ellipsis {
+        color: #999;
+        font-style: italic;
+    }
+</style>
+
+<div x-data="vizApp()">
+    <div class="viz-card">
+        <h2>Vector Visualization</h2>
+        <div class="viz-info-box">
+            Testing search algorithms on your indexed documents. User: <strong>{{ username }}</strong>
+        </div>
+
+        <form @submit.prevent="executeSearch">
+            <div class="viz-controls">
+                <!-- Main Controls -->
+                <div class="viz-control-group">
+                    <label>Search Query</label>
+                    <input type="text" x-model="query" placeholder="Enter search query..." required />
+                </div>
+
+                <div class="viz-control-row">
+                    <div class="viz-control-group" style="margin-bottom: 0;">
+                        <label>Algorithm</label>
+                        <select x-model="algorithm">
+                            <option value="semantic">Semantic (Dense Vectors)</option>
+                            <option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse)</option>
+                        </select>
+                    </div>
+
+                    <div class="viz-control-group" style="margin-bottom: 0;">
+                        <label>Fusion Method</label>
+                        <select x-model="fusion" :disabled="algorithm !== 'bm25_hybrid'" :style="algorithm !== 'bm25_hybrid' ? 'opacity: 0.5; cursor: not-allowed;' : ''">
+                            <option value="rrf" selected>RRF (Reciprocal Rank Fusion)</option>
+                            <option value="dbsf">DBSF (Distribution-Based Score Fusion)</option>
+                        </select>
+                    </div>
+
+                    <div style="display: flex; align-items: flex-end;">
+                        <button type="submit" class="viz-btn" style="width: 100%;">Search & Visualize</button>
+                    </div>
+
+                    <div style="display: flex; align-items: flex-end;">
+                        <button type="button" class="viz-btn-secondary" @click="showAdvanced = !showAdvanced" style="white-space: nowrap;">
+                            <span x-text="showAdvanced ? 'Hide Advanced' : 'Advanced'"></span>
+                        </button>
+                    </div>
+                </div>
+
+                <!-- Advanced Options (Collapsible) -->
+                <div class="viz-advanced-section" x-show="showAdvanced" x-transition.opacity.duration.200ms>
+                    <h3 style="margin-top: 0; margin-bottom: 16px; font-size: 16px;">Advanced Options</h3>
+
+                    <div class="viz-advanced-grid">
+                        <div class="viz-control-group">
+                            <label style="display: block; margin-bottom: 8px;">Document Types</label>
+                            <div style="display: grid; grid-template-columns: 1fr; gap: 6px;">
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="" style="margin-right: 8px;">
+                                    <span>All Types</span>
+                                </label>
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="note" style="margin-right: 8px;">
+                                    <span>Notes</span>
+                                </label>
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="file" style="margin-right: 8px;">
+                                    <span>Files</span>
+                                </label>
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="calendar" style="margin-right: 8px;">
+                                    <span>Calendar Events</span>
+                                </label>
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="contact" style="margin-right: 8px;">
+                                    <span>Contacts</span>
+                                </label>
+                                <label style="display: flex; align-items: center; cursor: pointer; font-weight: normal;">
+                                    <input type="checkbox" x-model="docTypes" value="deck" style="margin-right: 8px;">
+                                    <span>Deck Cards</span>
+                                </label>
+                            </div>
+                        </div>
+
+                        <div>
+                            <div class="viz-control-group">
+                                <label>Score Threshold (Semantic/Hybrid)</label>
+                                <input type="number" x-model.number="scoreThreshold" min="0" max="1" step="any" />
+                            </div>
+
+                            <div class="viz-control-group">
+                                <label>Result Limit</label>
+                                <input type="number" x-model.number="limit" min="1" max="100" />
+                            </div>
+                        </div>
+                    </div>
+
+                    <!-- Info: BM25 Hybrid fusion methods -->
+                    <div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
+                        <p style="margin: 0; font-size: 14px; color: #666;">
+                            <strong>BM25 Hybrid Search:</strong> Combines dense semantic vectors with sparse BM25 keyword vectors.
+                        </p>
+                        <p style="margin: 8px 0 0 0; font-size: 13px; color: #666;">
+                            <strong>RRF:</strong> Reciprocal Rank Fusion - Rank-based fusion producing scores in [0.0, 1.0]
+                        </p>
+                        <p style="margin: 4px 0 0 0; font-size: 13px; color: #666;">
+                            <strong>DBSF:</strong> Distribution-Based Score Fusion - Sums normalized scores (can exceed 1.0)
+                        </p>
+                    </div>
+                </div>
+            </div>
+        </form>
+    </div>
+
+    <div class="viz-card">
+        <div id="viz-plot-container">
+            <div x-show="loading" class="viz-loading-overlay" x-transition.opacity.duration.200ms>
+                Executing search and computing PCA projection...
+            </div>
+            <div id="viz-plot" x-show="!loading" x-transition.opacity.duration.200ms></div>
+        </div>
+    </div>
+
+    <div class="viz-card">
+        <h3>Search Results (<span x-text="loading ? '...' : results.length"></span>)</h3>
+
+        <div x-show="loading" class="viz-loading" x-transition.opacity.duration.200ms>
+            Loading results...
+        </div>
+
+        <div x-show="!loading && results.length === 0" class="viz-no-results" x-transition.opacity.duration.200ms>
+            No results found. Try a different query or adjust your search parameters.
+        </div>
+
+        <template x-if="!loading && results.length > 0">
+            <div x-transition.opacity.duration.200ms>
+                <template x-for="result in results" :key="result.id">
+                    <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: 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) |
+                            Type: <span x-text="result.doc_type"></span>
+                        </div>
+
+                        <!-- Show Chunk button (only if chunk position is available) -->
+                        <template x-if="hasChunkPosition(result)">
+                            <button
+                                class="chunk-toggle-btn"
+                                @click="toggleChunk(result)"
+                                x-text="isChunkExpanded(`${result.doc_type}_${result.id}`) ? 'Hide Chunk' : 'Show Chunk'"
+                            ></button>
+                        </template>
+
+                        <!-- Chunk context (expanded inline) -->
+                        <template x-if="isChunkExpanded(`${result.doc_type}_${result.id}`)">
+                            <div class="chunk-context" x-transition.opacity.duration.200ms>
+                                <template x-if="chunkLoading[`${result.doc_type}_${result.id}`]">
+                                    <div style="color: #666; font-style: italic;">Loading chunk...</div>
+                                </template>
+                                <template x-if="!chunkLoading[`${result.doc_type}_${result.id}`]">
+                                    <div>
+                                        <template x-if="expandedChunks[`${result.doc_type}_${result.id}`]?.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-ellipsis">...</span>
+                                        </template>
+                                    </div>
+                                </template>
+                            </div>
+                        </template>
+                    </div>
+                </template>
+            </div>
+        </template>
+    </div>
+</div>

nextcloud_mcp_server/auth/userinfo_routes.py

@@ -677,12 +677,15 @@ async def user_info_html(request: Request) -> HTMLResponse:
                 return {{
                     query: '',
                     algorithm: 'bm25_hybrid',
+                    fusion: 'rrf',  // Default fusion method for BM25 Hybrid
                     showAdvanced: false,
                     docTypes: [''],  // Default to "All Types"
                     limit: 50,
                     scoreThreshold: 0.0,
                     loading: false,
                     results: [],
+                    expandedChunks: {{}},  // Track which chunks are expanded (result_id -> chunk data)
+                    chunkLoading: {{}},    // Track loading state per result
 
                     async executeSearch() {{
                         this.loading = true;
@@ -696,6 +699,11 @@ async def user_info_html(request: Request) -> HTMLResponse:
                                 score_threshold: this.scoreThreshold,
                             }});
 
+                            // Add fusion parameter for BM25 Hybrid
+                            if (this.algorithm === 'bm25_hybrid') {{
+                                params.append('fusion', this.fusion);
+                            }}
+
                             // Add doc_types parameter (filter out empty string for "All Types")
                             const selectedTypes = this.docTypes.filter(t => t !== '');
                             if (selectedTypes.length > 0) {{
@@ -729,7 +737,7 @@ async def user_info_html(request: Request) -> HTMLResponse:
                             y: coordinates.map(c => c[1]),
                             mode: 'markers',
                             type: 'scatter',
-                            text: results.map(r => `${{r.title}}<br>Score: ${{r.score.toFixed(3)}}`),
+                            text: results.map(r => `${{r.title}}<br>Raw Score: ${{r.original_score.toFixed(3)}} (${{(r.score * 100).toFixed(0)}}% relative)`),
                             marker: {{
                                 // Multi-channel encoding: size + opacity + color for visual hierarchy
                                 // Power scaling (score^2) amplifies visual differences dramatically
@@ -778,6 +786,51 @@ async def user_info_html(request: Request) -> HTMLResponse:
                             default:
                                 return `${{baseUrl}}`;
                         }}
+                    }},
+
+                    hasChunkPosition(result) {{
+                        // Check if result has position metadata
+                        return result.chunk_start_offset != null && result.chunk_end_offset != null;
+                    }},
+
+                    isChunkExpanded(resultKey) {{
+                        return this.expandedChunks[resultKey] !== undefined;
+                    }},
+
+                    async toggleChunk(result) {{
+                        const resultKey = `${{result.doc_type}}_${{result.id}}`;
+
+                        // If already expanded, collapse
+                        if (this.isChunkExpanded(resultKey)) {{
+                            delete this.expandedChunks[resultKey];
+                            return;
+                        }}
+
+                        // Otherwise, fetch and expand
+                        this.chunkLoading[resultKey] = true;
+
+                        try {{
+                            const params = new URLSearchParams({{
+                                doc_type: result.doc_type,
+                                doc_id: result.id,
+                                start: result.chunk_start_offset,
+                                end: result.chunk_end_offset,
+                                context: 500  // 500 chars before/after
+                            }});
+
+                            const response = await fetch(`/app/chunk-context?${{params}}`);
+                            const data = await response.json();
+
+                            if (data.success) {{
+                                this.expandedChunks[resultKey] = data;
+                            }} else {{
+                                alert('Failed to load chunk: ' + data.error);
+                            }}
+                        }} catch (error) {{
+                            alert('Error loading chunk: ' + error.message);
+                        }} finally {{
+                            delete this.chunkLoading[resultKey];
+                        }}
                     }}
                 }}
             }}

nextcloud_mcp_server/auth/viz_routes.py

@@ -12,8 +12,10 @@ All processing happens server-side following ADR-012:
 
 import logging
 import time
+from pathlib import Path
 
 import numpy as np
+from jinja2 import Environment, FileSystemLoader
 from starlette.authentication import requires
 from starlette.requests import Request
 from starlette.responses import HTMLResponse, JSONResponse
@@ -28,6 +30,10 @@ from nextcloud_mcp_server.vector.qdrant_client import get_qdrant_client
 
 logger = logging.getLogger(__name__)
 
+# Setup Jinja2 environment for templates
+_template_dir = Path(__file__).parent / "templates"
+_jinja_env = Environment(loader=FileSystemLoader(_template_dir))
+
 
 @requires("authenticated", redirect="oauth_login")
 async def vector_visualization_html(request: Request) -> HTMLResponse:
@@ -63,252 +69,9 @@ async def vector_visualization_html(request: Request) -> HTMLResponse:
         else "unknown"
     )
 
-    html_content = f"""
-        <style>
-            .viz-card {{
-                background: white;
-                border-radius: 8px;
-                padding: 20px;
-                margin-bottom: 20px;
-                box-shadow: 0 2px 4px rgba(0,0,0,0.1);
-            }}
-            .viz-controls {{
-                margin-bottom: 20px;
-            }}
-            .viz-control-row {{
-                display: grid;
-                grid-template-columns: 2fr 1fr auto;
-                gap: 12px;
-                margin-bottom: 12px;
-                align-items: end;
-            }}
-            .viz-control-group {{
-                margin-bottom: 15px;
-            }}
-            .viz-control-group label {{
-                display: block;
-                margin-bottom: 5px;
-                font-weight: 500;
-                color: #333;
-            }}
-            .viz-control-group input[type="text"],
-            .viz-control-group input[type="number"],
-            .viz-control-group select {{
-                width: 100%;
-                padding: 8px 12px;
-                border: 1px solid #ddd;
-                border-radius: 4px;
-                font-size: 14px;
-            }}
-            .viz-control-group input[type="range"] {{
-                width: 100%;
-            }}
-            .viz-control-group select[multiple] {{
-                min-height: 100px;
-            }}
-            .viz-weight-display {{
-                display: inline-block;
-                min-width: 40px;
-                text-align: right;
-                color: #666;
-            }}
-            .viz-btn {{
-                background: #0066cc;
-                color: white;
-                border: none;
-                padding: 10px 20px;
-                border-radius: 4px;
-                cursor: pointer;
-                font-size: 14px;
-                font-weight: 500;
-            }}
-            .viz-btn:hover {{
-                background: #0052a3;
-            }}
-            .viz-btn-secondary {{
-                background: #6c757d;
-                color: white;
-                border: none;
-                padding: 6px 12px;
-                border-radius: 4px;
-                cursor: pointer;
-                font-size: 13px;
-                margin-bottom: 12px;
-            }}
-            .viz-btn-secondary:hover {{
-                background: #5a6268;
-            }}
-            #viz-plot-container {{
-                width: 100%;
-                height: 600px;
-                position: relative;
-            }}
-            #viz-plot {{
-                width: 100%;
-                height: 100%;
-            }}
-            .viz-loading {{
-                text-align: center;
-                padding: 40px;
-                color: #666;
-            }}
-            .viz-loading-overlay {{
-                position: absolute;
-                inset: 0;
-                display: flex;
-                align-items: center;
-                justify-content: center;
-                background: white;
-                color: #666;
-            }}
-            .viz-no-results {{
-                text-align: center;
-                padding: 40px;
-                color: #666;
-                font-style: italic;
-            }}
-            .viz-advanced-section {{
-                margin-top: 16px;
-                padding: 16px;
-                background: #f8f9fa;
-                border-radius: 4px;
-                border: 1px solid #dee2e6;
-            }}
-            .viz-advanced-grid {{
-                display: grid;
-                grid-template-columns: 1fr 1fr;
-                gap: 20px;
-            }}
-            .viz-info-box {{
-                background: #e3f2fd;
-                border-left: 4px solid #2196f3;
-                padding: 12px;
-                margin-bottom: 20px;
-                font-size: 14px;
-            }}
-        </style>
-
-        <div x-data="vizApp()">
-            <div class="viz-card">
-                <h2>Vector Visualization</h2>
-                <div class="viz-info-box">
-                    Testing search algorithms on your indexed documents. User: <strong>{username}</strong>
-                </div>
-
-                <form @submit.prevent="executeSearch">
-                    <div class="viz-controls">
-                        <!-- Main Controls -->
-                        <div class="viz-control-group">
-                            <label>Search Query</label>
-                            <input type="text" x-model="query" placeholder="Enter search query..." required />
-                        </div>
-
-                        <div class="viz-control-row">
-                            <div class="viz-control-group" style="margin-bottom: 0;">
-                                <label>Algorithm</label>
-                                <select x-model="algorithm">
-                                    <option value="semantic">Semantic (Dense Vectors)</option>
-                                    <option value="bm25_hybrid" selected>BM25 Hybrid (Dense + Sparse RRF)</option>
-                                </select>
-                            </div>
-
-                            <div style="display: flex; align-items: flex-end;">
-                                <button type="submit" class="viz-btn" style="width: 100%;">Search & Visualize</button>
-                            </div>
-
-                            <div style="display: flex; align-items: flex-end;">
-                                <button type="button" class="viz-btn-secondary" @click="showAdvanced = !showAdvanced" style="white-space: nowrap;">
-                                    <span x-text="showAdvanced ? 'Hide Advanced' : 'Advanced'"></span>
-                                </button>
-                            </div>
-                        </div>
-
-                        <!-- Advanced Options (Collapsible) -->
-                        <div class="viz-advanced-section" x-show="showAdvanced" x-transition.opacity.duration.200ms>
-                            <h3 style="margin-top: 0; margin-bottom: 16px; font-size: 16px;">Advanced Options</h3>
-
-                            <div class="viz-advanced-grid">
-                                <div class="viz-control-group">
-                                    <label>Document Types</label>
-                                    <select x-model="docTypes" multiple>
-                                        <option value="">All Types (cross-app search)</option>
-                                        <option value="note">Notes</option>
-                                        <option value="file">Files</option>
-                                        <option value="calendar">Calendar Events</option>
-                                        <option value="contact">Contacts</option>
-                                        <option value="deck">Deck Cards</option>
-                                    </select>
-                                    <small style="color: #666; display: block; margin-top: 4px;">
-                                        Hold Ctrl/Cmd to select multiple
-                                    </small>
-                                </div>
-
-                                <div>
-                                    <div class="viz-control-group">
-                                        <label>Score Threshold (Semantic/Hybrid)</label>
-                                        <input type="number" x-model.number="scoreThreshold" min="0" max="1" step="0.1" />
-                                    </div>
-
-                                    <div class="viz-control-group">
-                                        <label>Result Limit</label>
-                                        <input type="number" x-model.number="limit" min="1" max="100" />
-                                    </div>
-                                </div>
-                            </div>
-
-                            <!-- Info: BM25 Hybrid uses native RRF fusion (no manual weights) -->
-                            <div x-show="algorithm === 'bm25_hybrid'" style="margin-top: 16px; padding: 12px; background: #e9ecef; border-radius: 4px;">
-                                <p style="margin: 0; font-size: 14px; color: #666;">
-                                    <strong>BM25 Hybrid Search:</strong> Uses Qdrant's native Reciprocal Rank Fusion (RRF)
-                                    to automatically combine dense semantic vectors with sparse BM25 keyword vectors.
-                                    No manual weight tuning required.
-                                </p>
-                            </div>
-                        </div>
-                    </div>
-                </form>
-            </div>
-
-            <div class="viz-card">
-                <div id="viz-plot-container">
-                    <div x-show="loading" class="viz-loading-overlay" x-transition.opacity.duration.200ms>
-                        Executing search and computing PCA projection...
-                    </div>
-                    <div id="viz-plot" x-show="!loading" x-transition.opacity.duration.200ms></div>
-                </div>
-            </div>
-
-            <div class="viz-card">
-                <h3>Search Results (<span x-text="loading ? '...' : results.length"></span>)</h3>
-
-                <div x-show="loading" class="viz-loading" x-transition.opacity.duration.200ms>
-                    Loading results...
-                </div>
-
-                <div x-show="!loading && results.length === 0" class="viz-no-results" x-transition.opacity.duration.200ms>
-                    No results found. Try a different query or adjust your search parameters.
-                </div>
-
-                <template x-if="!loading && results.length > 0">
-                    <div x-transition.opacity.duration.200ms>
-                        <template x-for="result in results" :key="result.id">
-                            <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: 12px; color: #999; margin-top: 4px;">
-                                    Score: <span x-text="result.score.toFixed(3)"></span> |
-                                    Type: <span x-text="result.doc_type"></span>
-                                </div>
-                            </div>
-                        </template>
-                    </div>
-                </template>
-            </div>
-        </div>
-    """
-
+    # Load and render template
+    template = _jinja_env.get_template("vector_viz.html")
+    html_content = template.render(username=username)
     return HTMLResponse(content=html_content)
 
 
@@ -352,6 +115,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
     algorithm = request.query_params.get("algorithm", "bm25_hybrid")
     limit = int(request.query_params.get("limit", "50"))
     score_threshold = float(request.query_params.get("score_threshold", "0.0"))
+    fusion = request.query_params.get("fusion", "rrf")  # Default to RRF
 
     # Parse doc_types (comma-separated list, None = all types)
     doc_types_param = request.query_params.get("doc_types", "")
@@ -359,7 +123,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
 
     logger.info(
         f"Viz search: user={username}, query='{query}', "
-        f"algorithm={algorithm}, limit={limit}, doc_types={doc_types}"
+        f"algorithm={algorithm}, fusion={fusion}, limit={limit}, doc_types={doc_types}"
     )
 
     try:
@@ -377,7 +141,9 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             if algorithm == "semantic":
                 search_algo = SemanticSearchAlgorithm(score_threshold=score_threshold)
             elif algorithm == "bm25_hybrid":
-                search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
+                search_algo = BM25HybridSearchAlgorithm(
+                    score_threshold=score_threshold, fusion=fusion
+                )
             else:
                 return JSONResponse(
                     {"success": False, "error": f"Unknown algorithm: {algorithm}"},
@@ -418,7 +184,7 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             search_results = all_results[:limit]
             search_duration = time.perf_counter() - search_start
 
-        # Normalize scores relative to this result set for better visualization
+        # Store original scores and normalize for visualization
         # (best result = 1.0, worst result = 0.0 within THIS result set)
         # This makes visual encoding meaningful regardless of RRF normalization
         if search_results:
@@ -431,8 +197,11 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 f"→ [0.0, 1.0]"
             )
 
-            # Rescale each result's score to 0-1 within this result set
+            # Store original score and rescale to 0-1 for visualization
             for r in search_results:
+                # Store original score before normalization
+                r.original_score = r.score
+                # Rescale for visual encoding
                 r.score = (r.score - min_score) / score_range
 
         if not search_results:
@@ -551,7 +320,12 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
                 "doc_type": r.doc_type,
                 "title": r.title,
                 "excerpt": r.excerpt,
-                "score": r.score,
+                "score": r.score,  # Normalized score for visual encoding (0-1)
+                "original_score": getattr(
+                    r, "original_score", r.score
+                ),  # Raw score from algorithm
+                "chunk_start_offset": r.chunk_start_offset,
+                "chunk_end_offset": r.chunk_end_offset,
             }
             for r in search_results
         ]
@@ -594,3 +368,125 @@ async def vector_visualization_search(request: Request) -> JSONResponse:
             {"success": False, "error": str(e)},
             status_code=500,
         )
+
+
+@requires("authenticated", redirect="oauth_login")
+async def chunk_context_endpoint(request: Request) -> JSONResponse:
+    """Fetch chunk text with surrounding context for visualization.
+
+    This endpoint retrieves the matched chunk along with surrounding text
+    to provide context for the search result. Used by the viz pane to
+    display chunks inline.
+
+    Query parameters:
+        doc_type: Document type (e.g., "note")
+        doc_id: Document ID
+        start: Chunk start offset (character position)
+        end: Chunk end offset (character position)
+        context: Characters of context before/after (default: 500)
+
+    Returns:
+        JSON with chunk_text, before_context, after_context, and flags
+    """
+    try:
+        # Get query parameters
+        doc_type = request.query_params.get("doc_type")
+        doc_id = request.query_params.get("doc_id")
+        start_str = request.query_params.get("start")
+        end_str = request.query_params.get("end")
+        context_chars = int(request.query_params.get("context", "500"))
+
+        # Validate required parameters
+        if not all([doc_type, doc_id, start_str, end_str]):
+            return JSONResponse(
+                {
+                    "success": False,
+                    "error": "Missing required parameters: doc_type, doc_id, start, end",
+                },
+                status_code=400,
+            )
+
+        start = int(start_str)
+        end = int(end_str)
+
+        # 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
+        from nextcloud_mcp_server.auth.userinfo_routes import (
+            _get_authenticated_client_for_userinfo,
+        )
+        from nextcloud_mcp_server.client.notes import NotesClient
+
+        # Get username from request auth
+        username = (
+            request.user.display_name
+            if hasattr(request.user, "display_name")
+            else "unknown"
+        )
+
+        # 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:
+            return JSONResponse(
+                {
+                    "success": False,
+                    "error": f"Invalid offsets: start={start}, end={end}, content_length={len(full_content)}",
+                },
+                status_code=400,
+            )
+
+        # 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)}"
+        )
+
+        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,
+            }
+        )
+
+    except ValueError as e:
+        logger.error(f"Invalid parameter format: {e}")
+        return JSONResponse(
+            {"success": False, "error": f"Invalid parameter format: {e}"},
+            status_code=400,
+        )
+    except Exception as e:
+        logger.error(f"Chunk context error: {e}", exc_info=True)
+        return JSONResponse(
+            {"success": False, "error": str(e)},
+            status_code=500,
+        )

nextcloud_mcp_server/config.py

@@ -181,8 +181,8 @@ class Settings:
     ollama_verify_ssl: bool = True
 
     # Document chunking settings (for vector embeddings)
-    document_chunk_size: int = 512  # Words per chunk
-    document_chunk_overlap: int = 50  # Overlapping words between chunks
+    document_chunk_size: int = 2048  # Characters per chunk
+    document_chunk_overlap: int = 200  # Overlapping characters between chunks
 
     # Observability settings
     metrics_enabled: bool = True
@@ -227,10 +227,10 @@ class Settings:
                 f"Overlap should be 10-20% of chunk size for optimal results."
             )
 
-        if self.document_chunk_size < 100:
+        if self.document_chunk_size < 512:
             logger.warning(
-                f"DOCUMENT_CHUNK_SIZE is set to {self.document_chunk_size} words, which is quite small. "
-                f"Smaller chunks may lose context. Consider using at least 256 words."
+                f"DOCUMENT_CHUNK_SIZE is set to {self.document_chunk_size} characters, which is quite small. "
+                f"Smaller chunks may lose context. Consider using at least 1024 characters."
             )
 
         if self.document_chunk_overlap < 0:
@@ -335,8 +335,8 @@ def get_settings() -> Settings:
         ollama_embedding_model=os.getenv("OLLAMA_EMBEDDING_MODEL", "nomic-embed-text"),
         ollama_verify_ssl=os.getenv("OLLAMA_VERIFY_SSL", "true").lower() == "true",
         # Document chunking settings
-        document_chunk_size=int(os.getenv("DOCUMENT_CHUNK_SIZE", "512")),
-        document_chunk_overlap=int(os.getenv("DOCUMENT_CHUNK_OVERLAP", "50")),
+        document_chunk_size=int(os.getenv("DOCUMENT_CHUNK_SIZE", "2048")),
+        document_chunk_overlap=int(os.getenv("DOCUMENT_CHUNK_OVERLAP", "200")),
         # Observability settings
         metrics_enabled=os.getenv("METRICS_ENABLED", "true").lower() == "true",
         metrics_port=int(os.getenv("METRICS_PORT", "9090")),

nextcloud_mcp_server/models/semantic.py

@@ -19,9 +19,22 @@ class SemanticSearchResult(BaseModel):
         default="", description="Document category (notes) or location (calendar)"
     )
     excerpt: str = Field(description="Excerpt from matching chunk")
-    score: float = Field(description="Semantic similarity score (0-1)")
+    score: float = Field(
+        description=(
+            "Relevance score (≥ 0.0, higher is better). "
+            "Score range depends on fusion method: "
+            "RRF produces scores in [0.0, 1.0], "
+            "DBSF can exceed 1.0 (sum of normalized scores from multiple systems)"
+        )
+    )
     chunk_index: int = Field(description="Index of matching chunk in document")
     total_chunks: int = Field(description="Total number of chunks in document")
+    chunk_start_offset: Optional[int] = Field(
+        default=None, description="Character position where chunk starts in document"
+    )
+    chunk_end_offset: Optional[int] = Field(
+        default=None, description="Character position where chunk ends in document"
+    )
 
 
 class SemanticSearchResponse(BaseResponse):

nextcloud_mcp_server/search/algorithms.py

@@ -127,8 +127,12 @@ class SearchResult:
         doc_type: Document type (note, file, calendar, contact, etc.)
         title: Document title
         excerpt: Content excerpt showing match context
-        score: Relevance score (0.0-1.0, higher is better)
+        score: Relevance score (≥ 0.0, higher is better)
+            - RRF fusion: scores in [0.0, 1.0]
+            - DBSF fusion: scores can exceed 1.0 (sum of normalized scores)
         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)
     """
 
     id: int
@@ -137,11 +141,20 @@ class SearchResult:
     excerpt: str
     score: float
     metadata: dict[str, Any] | None = None
+    chunk_start_offset: int | None = None
+    chunk_end_offset: int | None = None
 
     def __post_init__(self):
-        """Validate score is in valid range."""
-        if not 0.0 <= self.score <= 1.0:
-            raise ValueError(f"Score must be between 0.0 and 1.0, got {self.score}")
+        """Validate score is non-negative.
+
+        Note: Different fusion methods produce different score ranges:
+        - RRF (Reciprocal Rank Fusion): Bounded to [0.0, 1.0]
+        - DBSF (Distribution-Based Score Fusion): Unbounded (can exceed 1.0)
+          DBSF sums normalized scores from multiple systems, so scores can be
+          1.5, 2.0, etc. when multiple systems agree a document is highly relevant.
+        """
+        if self.score < 0.0:
+            raise ValueError(f"Score must be non-negative, got {self.score}")
 
 
 class SearchAlgorithm(ABC):

nextcloud_mcp_server/search/bm25_hybrid.py

@@ -28,15 +28,27 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
     eliminating the need for application-layer result merging.
     """
 
-    def __init__(self, score_threshold: float = 0.0):
+    def __init__(self, score_threshold: float = 0.0, fusion: str = "rrf"):
         """
         Initialize BM25 hybrid search algorithm.
 
         Args:
-            score_threshold: Minimum RRF score (0-1, default: 0.0 to allow RRF scoring)
-                           Note: RRF produces normalized scores, so threshold is typically lower
+            score_threshold: Minimum fusion score (0-1, default: 0.0 to allow fusion scoring)
+                           Note: Both RRF and DBSF produce normalized scores
+            fusion: Fusion algorithm to use: "rrf" (Reciprocal Rank Fusion, default)
+                   or "dbsf" (Distribution-Based Score Fusion)
+
+        Raises:
+            ValueError: If fusion is not "rrf" or "dbsf"
         """
+        if fusion not in ("rrf", "dbsf"):
+            raise ValueError(
+                f"Invalid fusion algorithm '{fusion}'. Must be 'rrf' or 'dbsf'"
+            )
+
         self.score_threshold = score_threshold
+        self.fusion = models.Fusion.RRF if fusion == "rrf" else models.Fusion.DBSF
+        self.fusion_name = fusion
 
     @property
     def name(self) -> str:
@@ -78,7 +90,8 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
 
         logger.info(
             f"BM25 hybrid search: query='{query}', user={user_id}, "
-            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}"
+            f"limit={limit}, score_threshold={score_threshold}, doc_type={doc_type}, "
+            f"fusion={self.fusion_name}"
         )
 
         # Generate dense embedding for semantic search
@@ -139,8 +152,8 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                         filter=query_filter,
                     ),
                 ],
-                # RRF fusion query (no additional query needed, just fusion)
-                query=models.FusionQuery(fusion=models.Fusion.RRF),
+                # Fusion query (RRF or DBSF based on initialization)
+                query=models.FusionQuery(fusion=self.fusion),
                 limit=limit * 2,  # Get extra for deduplication
                 score_threshold=score_threshold,
                 with_payload=True,
@@ -152,14 +165,16 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
             raise
 
         logger.info(
-            f"Qdrant RRF fusion returned {len(search_response.points)} results "
+            f"Qdrant {self.fusion_name.upper()} fusion returned {len(search_response.points)} results "
             f"(before deduplication)"
         )
 
         if search_response.points:
-            # Log top 3 RRF scores to help with threshold tuning
+            # Log top 3 fusion scores to help with threshold tuning
             top_scores = [p.score for p in search_response.points[:3]]
-            logger.debug(f"Top 3 RRF fusion scores: {top_scores}")
+            logger.debug(
+                f"Top 3 {self.fusion_name.upper()} fusion scores: {top_scores}"
+            )
 
         # Deduplicate by (doc_id, doc_type) - multiple chunks per document
         seen_docs = set()
@@ -183,12 +198,14 @@ class BM25HybridSearchAlgorithm(SearchAlgorithm):
                     doc_type=doc_type,
                     title=result.payload.get("title", "Untitled"),
                     excerpt=result.payload.get("excerpt", ""),
-                    score=result.score,  # RRF fusion score
+                    score=result.score,  # Fusion score (RRF or DBSF)
                     metadata={
                         "chunk_index": result.payload.get("chunk_index"),
                         "total_chunks": result.payload.get("total_chunks"),
-                        "search_method": "bm25_hybrid_rrf",
+                        "search_method": f"bm25_hybrid_{self.fusion_name}",
                     },
+                    chunk_start_offset=result.payload.get("chunk_start_offset"),
+                    chunk_end_offset=result.payload.get("chunk_end_offset"),
                 )
             )
 

nextcloud_mcp_server/search/semantic.py

@@ -150,6 +150,8 @@ class SemanticSearchAlgorithm(SearchAlgorithm):
                         "chunk_index": result.payload.get("chunk_index"),
                         "total_chunks": result.payload.get("total_chunks"),
                     },
+                    chunk_start_offset=result.payload.get("chunk_start_offset"),
+                    chunk_end_offset=result.payload.get("chunk_end_offset"),
                 )
             )
 

nextcloud_mcp_server/server/semantic.py

@@ -42,6 +42,7 @@ def configure_semantic_tools(mcp: FastMCP):
         limit: int = 10,
         doc_types: list[str] | None = None,
         score_threshold: float = 0.0,
+        fusion: str = "rrf",
     ) -> SemanticSearchResponse:
         """
         Search Nextcloud content using BM25 hybrid search with cross-app support.
@@ -50,7 +51,7 @@ def configure_semantic_tools(mcp: FastMCP):
         - Dense semantic vectors: For conceptual similarity and natural language queries
         - BM25 sparse vectors: For precise keyword matching, acronyms, and specific terms
 
-        Results are automatically fused using Reciprocal Rank Fusion (RRF) in the
+        Results are automatically fused using the selected fusion algorithm in the
         database for optimal relevance. This provides the best of both semantic
         understanding and keyword precision.
 
@@ -61,10 +62,13 @@ def configure_semantic_tools(mcp: FastMCP):
             query: Natural language or keyword search query
             limit: Maximum number of results to return (default: 10)
             doc_types: Document types to search (e.g., ["note", "file"]). None = search all indexed types (default)
-            score_threshold: Minimum RRF fusion score (0-1, default: 0.0 for RRF scoring)
+            score_threshold: Minimum fusion score (0-1, default: 0.0)
+            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
 
         Returns:
-            SemanticSearchResponse with matching documents ranked by RRF fusion scores
+            SemanticSearchResponse with matching documents ranked by fusion scores
         """
         from nextcloud_mcp_server.config import get_settings
 
@@ -74,7 +78,7 @@ def configure_semantic_tools(mcp: FastMCP):
 
         logger.info(
             f"BM25 hybrid search: query='{query}', user={username}, "
-            f"limit={limit}, score_threshold={score_threshold}"
+            f"limit={limit}, score_threshold={score_threshold}, fusion={fusion}"
         )
 
         # Check that vector sync is enabled
@@ -87,8 +91,10 @@ def configure_semantic_tools(mcp: FastMCP):
             )
 
         try:
-            # Create BM25 hybrid search algorithm
-            search_algo = BM25HybridSearchAlgorithm(score_threshold=score_threshold)
+            # Create BM25 hybrid search algorithm with specified fusion
+            search_algo = BM25HybridSearchAlgorithm(
+                score_threshold=score_threshold, fusion=fusion
+            )
 
             # Execute search across requested document types
             # If doc_types is None, search all indexed types (cross-app search)
@@ -152,6 +158,8 @@ def configure_semantic_tools(mcp: FastMCP):
                         total_chunks=r.metadata.get("total_chunks", 1)
                         if r.metadata
                         else 1,
+                        chunk_start_offset=r.chunk_start_offset,
+                        chunk_end_offset=r.chunk_end_offset,
                     )
                 )
 
@@ -161,7 +169,7 @@ def configure_semantic_tools(mcp: FastMCP):
                 results=results,
                 query=query,
                 total_found=len(results),
-                search_method="bm25_hybrid",
+                search_method=f"bm25_hybrid_{fusion}",
             )
 
         except ValueError as e:
@@ -193,6 +201,7 @@ def configure_semantic_tools(mcp: FastMCP):
         limit: int = 5,
         score_threshold: float = 0.7,
         max_answer_tokens: int = 500,
+        fusion: str = "rrf",
     ) -> SamplingSearchResponse:
         """
         Semantic search with LLM-generated answer using MCP sampling.
@@ -217,6 +226,7 @@ def configure_semantic_tools(mcp: FastMCP):
             limit: Maximum number of documents to retrieve (default: 5)
             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)
 
         Returns:
             SamplingSearchResponse containing:
@@ -256,6 +266,7 @@ def configure_semantic_tools(mcp: FastMCP):
             ctx=ctx,
             limit=limit,
             score_threshold=score_threshold,
+            fusion=fusion,
         )
 
         # 2. Handle no results case - don't waste a sampling call

nextcloud_mcp_server/vector/document_chunker.py

@@ -1,51 +1,91 @@
-"""Document chunking for large texts."""
+"""Document chunking for large texts using LangChain text splitters."""
 
 import logging
+from dataclasses import dataclass
+
+from langchain_text_splitters import MarkdownTextSplitter
 
 logger = logging.getLogger(__name__)
 
 
-class DocumentChunker:
-    """Chunk large documents for optimal embedding."""
+@dataclass
+class ChunkWithPosition:
+    """A text chunk with its character position in the original document."""
 
-    def __init__(self, chunk_size: int = 512, overlap: int = 50):
+    text: str
+    start_offset: int  # Character position where chunk starts
+    end_offset: int  # Character position where chunk ends (exclusive)
+
+
+class DocumentChunker:
+    """Chunk large documents for optimal embedding using LangChain text splitters.
+
+    Uses MarkdownTextSplitter which is optimized for Markdown content like
+    Nextcloud Notes. Respects markdown structure (headers, code blocks, lists)
+    while maintaining semantic boundaries.
+    """
+
+    def __init__(self, chunk_size: int = 2048, overlap: int = 200):
         """
         Initialize document chunker.
 
         Args:
-            chunk_size: Number of words per chunk (default: 512)
-            overlap: Number of overlapping words between chunks (default: 50)
+            chunk_size: Number of characters per chunk (default: 2048)
+            overlap: Number of overlapping characters between chunks (default: 200)
         """
         self.chunk_size = chunk_size
         self.overlap = overlap
 
-    def chunk_text(self, content: str) -> list[str]:
-        """
-        Split text into overlapping chunks.
+        # Initialize LangChain MarkdownTextSplitter
+        # Optimized for Markdown content with special handling for:
+        # - Headers (# ## ###)
+        # - Code blocks (``` ```)
+        # - Lists (- * 1.)
+        # - Horizontal rules (---)
+        # - Paragraphs and sentences
+        # This preserves both markdown structure and semantic boundaries
+        self.splitter = MarkdownTextSplitter(
+            chunk_size=chunk_size,
+            chunk_overlap=overlap,
+            add_start_index=True,  # Enable position tracking
+            strip_whitespace=True,
+        )
 
-        Uses simple word-based chunking with configurable overlap to preserve
-        context across chunk boundaries.
+    def chunk_text(self, content: str) -> list[ChunkWithPosition]:
+        """
+        Split text into overlapping chunks with position tracking.
+
+        Uses LangChain's MarkdownTextSplitter to create chunks that respect
+        both markdown structure and semantic boundaries. Optimized for Nextcloud
+        Notes content with special handling for headers, code blocks, lists, etc.
+        Preserves character positions for each chunk to enable precise document
+        retrieval.
 
         Args:
-            content: Text content to chunk
+            content: Markdown text content to chunk
 
         Returns:
-            List of text chunks (may be single item if content is small)
+            List of chunks with their character positions in the original content
         """
-        # Simple word-based chunking
-        words = content.split()
+        # Handle empty content - return single empty chunk for backward compatibility
+        if not content:
+            return [ChunkWithPosition(text="", start_offset=0, end_offset=0)]
 
-        if len(words) <= self.chunk_size:
-            return [content]
+        # Use LangChain to create documents with position tracking
+        docs = self.splitter.create_documents([content])
 
-        chunks = []
-        start = 0
+        # Convert LangChain Documents to ChunkWithPosition objects
+        chunks = [
+            ChunkWithPosition(
+                text=doc.page_content,
+                start_offset=doc.metadata.get("start_index", 0),
+                end_offset=doc.metadata.get("start_index", 0) + len(doc.page_content),
+            )
+            for doc in docs
+        ]
 
-        while start < len(words):
-            end = start + self.chunk_size
-            chunk_words = words[start:end]
-            chunks.append(" ".join(chunk_words))
-            start = end - self.overlap
-
-        logger.debug(f"Chunked document into {len(chunks)} chunks ({len(words)} words)")
+        logger.debug(
+            f"Chunked document into {len(chunks)} chunks "
+            f"(chunk_size={self.chunk_size}, overlap={self.overlap})"
+        )
         return chunks

nextcloud_mcp_server/vector/processor.py

@@ -233,13 +233,16 @@ async def _index_document(
     )
     chunks = chunker.chunk_text(content)
 
+    # 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(chunks)
+    dense_embeddings = await embedding_service.embed_batch(chunk_texts)
 
     # Generate sparse embeddings (BM25 for keyword matching)
     bm25_service = get_bm25_service()
-    sparse_embeddings = bm25_service.encode_batch(chunks)
+    sparse_embeddings = bm25_service.encode_batch(chunk_texts)
 
     # Prepare Qdrant points
     indexed_at = int(time.time())
@@ -265,12 +268,15 @@ async def _index_document(
                     "doc_id": doc_task.doc_id,
                     "doc_type": doc_task.doc_type,
                     "title": title,
-                    "excerpt": chunk[:200],
+                    "excerpt": chunk.text[:200],
                     "indexed_at": indexed_at,
                     "modified_at": doc_task.modified_at,
                     "etag": etag,
                     "chunk_index": i,
                     "total_chunks": len(chunks),
+                    "chunk_start_offset": chunk.start_offset,
+                    "chunk_end_offset": chunk.end_offset,
+                    "metadata_version": 2,  # v2 includes position metadata
                 },
             )
         )

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "nextcloud-mcp-server"
-version = "0.39.0"
+version = "0.42.0"
 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"}
@@ -12,7 +12,7 @@ keywords = ["nextcloud", "mcp", "model-context-protocol", "llm", "ai", "claude",
 dependencies = [
     "mcp[cli] (>=1.21,<1.22)",
     "httpx (>=0.28.1,<0.29.0)",
-    "pillow (>=10.3.0,<12.0.0)",  # Compatible with fastembed
+    "pillow (>=10.3.0,<12.0.0)", # Compatible with fastembed
     "icalendar (>=6.0.0,<7.0.0)",
     "pythonvcard4>=0.2.0",
     "pydantic>=2.11.4",
@@ -22,7 +22,9 @@ dependencies = [
     "aiosqlite>=0.20.0", # Async SQLite for refresh token storage
     "authlib>=1.6.5",
     "qdrant-client>=1.7.0",
-    "fastembed>=0.4.2",  # BM25 sparse vector embeddings for hybrid search
+    "fastembed>=0.7.3", # BM25 sparse vector embeddings for hybrid search
+    "anthropic>=0.42.0", # For RAG evaluation with Anthropic LLMs
+    "boto3>=1.35.0", # For Amazon Bedrock provider (optional)
     # Observability dependencies
     "prometheus-client>=0.21.0", # Prometheus metrics
     "opentelemetry-api>=1.28.2", # OpenTelemetry API
@@ -32,6 +34,8 @@ dependencies = [
     "opentelemetry-instrumentation-logging>=0.49b2", # Logging integration
     "opentelemetry-exporter-otlp-proto-grpc>=1.28.2", # OTLP gRPC exporter
     "python-json-logger>=3.2.0", # Structured JSON logging
+    "jinja2>=3.1.6",
+    "langchain-text-splitters>=1.0.0",
 ]
 classifiers = [
     "Development Status :: 4 - Beta",
@@ -103,10 +107,8 @@ module-root = ""
 
 [dependency-groups]
 dev = [
-    "anthropic>=0.42.0",  # For RAG evaluation with Anthropic LLMs
-    "boto3>=1.35.0",  # For Amazon Bedrock provider (optional)
     "commitizen>=4.8.2",
-    "datasets>=3.3.0",  # For BeIR nfcorpus dataset loading
+    "datasets>=3.3.0", # For BeIR nfcorpus dataset loading
     "ipython>=9.2.0",
     "playwright>=1.49.1",
     "pytest>=8.3.5",

tests/unit/search/__init__.py

@@ -0,0 +1 @@
+"""Unit tests for search algorithms."""

tests/unit/search/test_bm25_hybrid.py

@@ -0,0 +1,54 @@
+"""Unit tests for BM25 hybrid search algorithm."""
+
+import pytest
+from qdrant_client import models
+
+from nextcloud_mcp_server.search.bm25_hybrid import BM25HybridSearchAlgorithm
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_default():
+    """Test BM25HybridSearchAlgorithm initializes with default RRF fusion."""
+    algo = BM25HybridSearchAlgorithm()
+
+    assert algo.score_threshold == 0.0
+    assert algo.fusion == models.Fusion.RRF
+    assert algo.fusion_name == "rrf"
+    assert algo.name == "bm25_hybrid"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_with_rrf():
+    """Test BM25HybridSearchAlgorithm initializes with explicit RRF fusion."""
+    algo = BM25HybridSearchAlgorithm(score_threshold=0.5, fusion="rrf")
+
+    assert algo.score_threshold == 0.5
+    assert algo.fusion == models.Fusion.RRF
+    assert algo.fusion_name == "rrf"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_initialization_with_dbsf():
+    """Test BM25HybridSearchAlgorithm initializes with DBSF fusion."""
+    algo = BM25HybridSearchAlgorithm(score_threshold=0.7, fusion="dbsf")
+
+    assert algo.score_threshold == 0.7
+    assert algo.fusion == models.Fusion.DBSF
+    assert algo.fusion_name == "dbsf"
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_invalid_fusion_raises_error():
+    """Test BM25HybridSearchAlgorithm raises ValueError for invalid fusion."""
+    with pytest.raises(ValueError) as exc_info:
+        BM25HybridSearchAlgorithm(fusion="invalid")
+
+    assert "Invalid fusion algorithm 'invalid'" in str(exc_info.value)
+    assert "Must be 'rrf' or 'dbsf'" in str(exc_info.value)
+
+
+@pytest.mark.unit
+def test_bm25_hybrid_requires_vector_db():
+    """Test BM25HybridSearchAlgorithm reports it requires vector database."""
+    algo = BM25HybridSearchAlgorithm()
+    assert algo.requires_vector_db is True

tests/unit/search/test_search_result.py

@@ -0,0 +1,135 @@
+"""Unit tests for SearchResult validation."""
+
+import pytest
+
+from nextcloud_mcp_server.search.algorithms import SearchResult
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_in_range():
+    """Test SearchResult accepts RRF scores in [0.0, 1.0] range."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=0.85,
+    )
+
+    assert result.score == 0.85
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_at_lower_bound():
+    """Test SearchResult accepts RRF score at lower bound (0.0)."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=0.0,
+    )
+
+    assert result.score == 0.0
+
+
+@pytest.mark.unit
+def test_search_result_rrf_score_at_upper_bound():
+    """Test SearchResult accepts RRF score at upper bound (1.0)."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=1.0,
+    )
+
+    assert result.score == 1.0
+
+
+@pytest.mark.unit
+def test_search_result_dbsf_score_above_one():
+    """Test SearchResult accepts DBSF scores > 1.0.
+
+    DBSF (Distribution-Based Score Fusion) sums normalized scores from multiple
+    systems (dense semantic + sparse BM25), so scores can exceed 1.0 when both
+    systems strongly agree a document is relevant.
+    """
+    # Typical DBSF score when both systems agree
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Highly Relevant Note",
+        excerpt="Contains keywords and is semantically similar",
+        score=1.55,
+    )
+
+    assert result.score == 1.55
+
+
+@pytest.mark.unit
+def test_search_result_dbsf_score_edge_case():
+    """Test SearchResult accepts DBSF maximum theoretical score (2.0).
+
+    Maximum DBSF score with 2 systems: 1.0 (dense) + 1.0 (sparse) = 2.0
+    """
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Perfect Match",
+        excerpt="Perfect semantic and keyword match",
+        score=2.0,
+    )
+
+    assert result.score == 2.0
+
+
+@pytest.mark.unit
+def test_search_result_negative_score_raises_error():
+    """Test SearchResult rejects negative scores."""
+    with pytest.raises(ValueError) as exc_info:
+        SearchResult(
+            id=1,
+            doc_type="note",
+            title="Test Note",
+            excerpt="Test excerpt",
+            score=-0.1,
+        )
+
+    assert "Score must be non-negative" in str(exc_info.value)
+    assert "got -0.1" in str(exc_info.value)
+
+
+@pytest.mark.unit
+def test_search_result_with_metadata():
+    """Test SearchResult with optional metadata field."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="Test excerpt",
+        score=1.25,
+        metadata={"fusion_method": "dbsf", "dense_score": 0.8, "sparse_score": 0.45},
+    )
+
+    assert result.score == 1.25
+    assert result.metadata["fusion_method"] == "dbsf"
+    assert result.metadata["dense_score"] == 0.8
+    assert result.metadata["sparse_score"] == 0.45
+
+
+@pytest.mark.unit
+def test_search_result_with_chunk_offsets():
+    """Test SearchResult with chunk offset information."""
+    result = SearchResult(
+        id=1,
+        doc_type="note",
+        title="Test Note",
+        excerpt="matching chunk text",
+        score=0.9,
+        chunk_start_offset=100,
+        chunk_end_offset=500,
+    )
+
+    assert result.chunk_start_offset == 100
+    assert result.chunk_end_offset == 500

tests/unit/test_document_chunker.py

@@ -0,0 +1,288 @@
+"""Unit tests for DocumentChunker with LangChain text splitters."""
+
+from nextcloud_mcp_server.vector.document_chunker import (
+    ChunkWithPosition,
+    DocumentChunker,
+)
+
+
+class TestDocumentChunkerPositions:
+    """Test suite for DocumentChunker position tracking functionality."""
+
+    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)
+
+        assert len(chunks) == 1
+        assert isinstance(chunks[0], ChunkWithPosition)
+        assert chunks[0].text == content
+        assert chunks[0].start_offset == 0
+        assert chunks[0].end_offset == len(content)
+
+    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)
+        # Create content longer than chunk size
+        content = (
+            "This is the first sentence with some important content. "
+            "This is the second sentence with more details. "
+            "This is the third sentence continuing the discussion. "
+            "This is the fourth sentence adding more context."
+        )
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify we got multiple chunks
+        assert len(chunks) > 1
+
+        # Verify all chunks are ChunkWithPosition
+        for chunk in chunks:
+            assert isinstance(chunk, ChunkWithPosition)
+
+        # Verify first chunk starts at 0
+        assert chunks[0].start_offset == 0
+
+        # Verify last chunk ends at content length
+        assert chunks[-1].end_offset == len(content)
+
+        # Verify chunks are contiguous or overlap (minimal gaps allowed)
+        for i in range(len(chunks) - 1):
+            # Next chunk should start at or near current chunk end
+            # Allow small gaps (1-2 chars) for whitespace/punctuation at boundaries
+            gap = chunks[i + 1].start_offset - chunks[i].end_offset
+            assert gap <= 2, f"Gap too large between chunks: {gap} characters"
+
+        # Verify we can reconstruct the content using positions
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+    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)
+
+        # Verify positions correctly handle whitespace
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+            # LangChain strips whitespace by default
+            assert len(chunk.text.strip()) > 0
+
+    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)
+
+        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):
+        """Test that overlapping chunks have correct positions."""
+        chunker = DocumentChunker(chunk_size=50, overlap=15)
+        content = (
+            "This is sentence one with content. "
+            "This is sentence two with more. "
+            "This is sentence three continuing. "
+            "This is sentence four adding details."
+        )
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify overlap exists if we have multiple chunks
+        if len(chunks) > 1:
+            for i in range(len(chunks) - 1):
+                current_chunk = chunks[i]
+                next_chunk = chunks[i + 1]
+
+                # Verify positions are valid
+                assert next_chunk.start_offset >= 0
+                assert current_chunk.end_offset <= len(content)
+
+                # With overlap, next chunk may start before current ends
+                assert next_chunk.start_offset <= current_chunk.end_offset
+
+    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)
+
+        # Verify all chunks extract correctly
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+        # Verify full coverage
+        if len(chunks) == 1:
+            assert chunks[0].start_offset == 0
+            assert chunks[0].end_offset == len(content)
+
+    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
+
+This is a note about my project. It contains several paragraphs of text
+that should be chunked appropriately for embedding.
+
+## Key Points
+
+- First important point with some details
+- Second point that needs to be remembered
+- Third point for future reference
+
+The document continues with more content here. We want to make sure that
+the chunking preserves context across boundaries while maintaining proper
+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)
+
+        # Should have multiple chunks
+        assert len(chunks) > 1
+
+        # Verify all chunks
+        for chunk in chunks:
+            assert isinstance(chunk, ChunkWithPosition)
+            # Verify extraction
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+            # Verify positions are valid
+            assert chunk.start_offset >= 0
+            assert chunk.end_offset <= len(content)
+            assert chunk.start_offset < chunk.end_offset
+
+    def test_semantic_boundary_preservation(self):
+        """Test that LangChain creates semantically coherent chunks."""
+        chunker = DocumentChunker(chunk_size=100, overlap=20)
+        content = (
+            "First sentence is here. "
+            "Second sentence follows. "
+            "Third sentence continues. "
+            "Fourth sentence ends."
+        )
+
+        chunks = chunker.chunk_text(content)
+
+        # Verify all chunks are extractable using their positions
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+            # Verify chunk text is meaningful (not empty or just whitespace)
+            assert len(chunk.text.strip()) > 0
+
+            # Verify positions are valid
+            assert chunk.start_offset >= 0
+            assert chunk.end_offset <= len(content)
+            assert chunk.start_offset < chunk.end_offset
+
+    def test_paragraph_boundary_preservation(self):
+        """Test that LangChain preserves paragraph boundaries."""
+        chunker = DocumentChunker(chunk_size=80, overlap=15)
+        content = """First paragraph here.
+
+Second paragraph here.
+
+Third paragraph here.
+
+Fourth paragraph here."""
+
+        chunks = chunker.chunk_text(content)
+
+        # LangChain should prefer splitting at paragraph boundaries (\n\n)
+        # Verify we got multiple chunks
+        assert len(chunks) >= 1
+
+        # Verify all positions work correctly
+        for chunk in chunks:
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+    def test_default_parameters(self):
+        """Test that default parameters work correctly."""
+        chunker = DocumentChunker()  # Use defaults: 2048 chars, 200 overlap
+
+        # Create content that's smaller than default chunk size
+        content = (
+            "This is a short note with a few sentences. It should fit in one chunk."
+        )
+
+        chunks = 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):
+        """Test chunking of a large document."""
+        chunker = DocumentChunker(chunk_size=100, overlap=20)
+
+        # Create a large document with multiple paragraphs
+        paragraphs = [
+            f"This is paragraph {i} with some meaningful content about topic {i}. "
+            f"It contains multiple sentences to make it realistic. "
+            f"The content should be properly chunked."
+            for i in range(10)
+        ]
+        content = "\n\n".join(paragraphs)
+
+        chunks = chunker.chunk_text(content)
+
+        # Should create multiple chunks
+        assert len(chunks) > 1
+
+        # Verify all chunks are valid
+        for chunk in chunks:
+            assert isinstance(chunk, ChunkWithPosition)
+            assert len(chunk.text) > 0
+            # Verify extraction
+            extracted = content[chunk.start_offset : chunk.end_offset]
+            assert extracted == chunk.text
+
+        # Verify first and last positions
+        assert chunks[0].start_offset == 0
+        assert chunks[-1].end_offset == len(content)
+
+    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)
+
+        if len(chunks) > 1:
+            # Verify overlap creates correct positions
+            for i in range(len(chunks) - 1):
+                # Each chunk should be extractable
+                assert (
+                    content[chunks[i].start_offset : chunks[i].end_offset]
+                    == chunks[i].text
+                )
+
+                # Next chunk should overlap with current
+                # (start before current ends)
+                if chunks[i + 1].start_offset < chunks[i].end_offset:
+                    # There is overlap - verify content matches
+                    overlap_start = chunks[i + 1].start_offset
+                    overlap_end = chunks[i].end_offset
+                    overlap_text = content[overlap_start:overlap_end]
+                    assert overlap_text in chunks[i].text
+                    assert overlap_text in chunks[i + 1].text

uv.lock

@@ -2,7 +2,8 @@ version = 1
 revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.13'",
+    "python_full_version >= '3.14'",
+    "python_full_version == '3.13.*'",
     "python_full_version == '3.12.*'",
     "python_full_version < '3.12'",
 ]
@@ -1333,6 +1334,27 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/31/b4/b9b800c45527aadd64d5b442f9b932b00648617eb5d63d2c7a6587b7cafc/jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980", size = 20256, upload-time = "2022-06-17T18:00:10.251Z" },
 ]
 
+[[package]]
+name = "jsonpatch"
+version = "1.33"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jsonpointer" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/42/78/18813351fe5d63acad16aec57f94ec2b70a09e53ca98145589e185423873/jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c", size = 21699, upload-time = "2023-06-26T12:07:29.144Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/73/07/02e16ed01e04a374e644b575638ec7987ae846d25ad97bcc9945a3ee4b0e/jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade", size = 12898, upload-time = "2023-06-16T21:01:28.466Z" },
+]
+
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/0a/eebeb1fa92507ea94016a2a790b93c2ae41a7e18778f85471dc54475ed25/jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef", size = 9114, upload-time = "2024-06-10T19:24:42.462Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/71/92/5e77f98553e9e75130c78900d000368476aed74276eb8ae8796f65f00918/jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942", size = 7595, upload-time = "2024-06-10T19:24:40.698Z" },
+]
+
 [[package]]
 name = "jsonschema"
 version = "4.25.1"
@@ -1360,6 +1382,54 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
 ]
 
+[[package]]
+name = "langchain-core"
+version = "1.0.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jsonpatch" },
+    { name = "langsmith" },
+    { name = "packaging" },
+    { name = "pydantic" },
+    { name = "pyyaml" },
+    { name = "tenacity" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/d9/61/c356e19525a210baf960968dbfb03ee38a05e05ddb41efeb32abfcb4e360/langchain_core-1.0.5.tar.gz", hash = "sha256:7ecbad9a60dde626252733a9c18c7377f4468cfe00465ffa99f5e9c6cb9b82d2", size = 778259, upload-time = "2025-11-14T16:59:27.277Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6e/ee/aaf2343a35080154c82ceb110e03dd00f15459bc72e518df51724cbc41a9/langchain_core-1.0.5-py3-none-any.whl", hash = "sha256:d24c0cf12cfcd96dd4bd479aa91425f3a6652226cd824228ae422a195067b74e", size = 471506, upload-time = "2025-11-14T16:59:25.629Z" },
+]
+
+[[package]]
+name = "langchain-text-splitters"
+version = "1.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/fa/2e/c833dcc379c1c086453708ef5eef7d4d1f808559ca4458bd6569d5d83ad7/langchain_text_splitters-1.0.0.tar.gz", hash = "sha256:d8580a20ad7ed10b432feb273e5758b2cc0902d094919629cec0e1ad691a6744", size = 264257, upload-time = "2025-10-17T14:33:41.743Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1e/97/d362353ab04f865af6f81d4d46e7aa428734aa032de0017934b771fc34b7/langchain_text_splitters-1.0.0-py3-none-any.whl", hash = "sha256:f00c8219d3468f2c5bd951b708b6a7dd9bc3c62d0cfb83124c377f7170f33b2e", size = 33851, upload-time = "2025-10-17T14:33:40.46Z" },
+]
+
+[[package]]
+name = "langsmith"
+version = "0.4.43"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "httpx" },
+    { name = "orjson", marker = "platform_python_implementation != 'PyPy'" },
+    { name = "packaging" },
+    { name = "pydantic" },
+    { name = "requests" },
+    { name = "requests-toolbelt" },
+    { name = "zstandard" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ad/b4/073e3fd494f7853fd4e59f5ae56c49f672e081e65f17ef363224e60530ab/langsmith-0.4.43.tar.gz", hash = "sha256:75c2468ab740438adfb32af8595ad8837c3af2bd1cdaf057d534182c5a07407a", size = 984142, upload-time = "2025-11-15T00:32:12.454Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f1/5c/521a3d8295e2e7caea67032e65554866293b6dc8e934bd86be8cc1f7b955/langsmith-0.4.43-py3-none-any.whl", hash = "sha256:c97846a0b15061bc15844aac32fd1ce4a8e50983905f80a0d6079bb41b112ae3", size = 410232, upload-time = "2025-11-15T00:32:10.557Z" },
+]
+
 [[package]]
 name = "loguru"
 version = "0.7.3"
@@ -1857,16 +1927,20 @@ wheels = [
 
 [[package]]
 name = "nextcloud-mcp-server"
-version = "0.39.0"
+version = "0.42.0"
 source = { editable = "." }
 dependencies = [
     { name = "aiosqlite" },
+    { name = "anthropic" },
     { name = "authlib" },
+    { name = "boto3" },
     { name = "caldav" },
     { name = "click" },
     { name = "fastembed" },
     { name = "httpx" },
     { name = "icalendar" },
+    { name = "jinja2" },
+    { name = "langchain-text-splitters" },
     { name = "mcp", extra = ["cli"] },
     { name = "opentelemetry-api" },
     { name = "opentelemetry-exporter-otlp-proto-grpc" },
@@ -1885,8 +1959,6 @@ dependencies = [
 
 [package.dev-dependencies]
 dev = [
-    { name = "anthropic" },
-    { name = "boto3" },
     { name = "commitizen" },
     { name = "datasets" },
     { name = "ipython" },
@@ -1904,12 +1976,16 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "aiosqlite", specifier = ">=0.20.0" },
+    { name = "anthropic", specifier = ">=0.42.0" },
     { name = "authlib", specifier = ">=1.6.5" },
+    { name = "boto3", specifier = ">=1.35.0" },
     { name = "caldav", git = "https://github.com/cbcoutinho/caldav?branch=feature%2Fhttpx" },
     { name = "click", specifier = ">=8.1.8" },
-    { name = "fastembed", specifier = ">=0.4.2" },
+    { name = "fastembed", specifier = ">=0.7.3" },
     { name = "httpx", specifier = ">=0.28.1,<0.29.0" },
     { 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 = "opentelemetry-api", specifier = ">=1.28.2" },
     { name = "opentelemetry-exporter-otlp-proto-grpc", specifier = ">=1.28.2" },
@@ -1928,8 +2004,6 @@ requires-dist = [
 
 [package.metadata.requires-dev]
 dev = [
-    { name = "anthropic", specifier = ">=0.42.0" },
-    { name = "boto3", specifier = ">=1.35.0" },
     { name = "commitizen", specifier = ">=4.8.2" },
     { name = "datasets", specifier = ">=3.3.0" },
     { name = "ipython", specifier = ">=9.2.0" },
@@ -2208,6 +2282,74 @@ wheels = [
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 ]
 
+[[package]]
+name = "orjson"
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