feat(vector): Add configurable chunk size and overlap for document embedding

Enable users to tune document chunking parameters to match their embedding
model and content type by adding DOCUMENT_CHUNK_SIZE and DOCUMENT_CHUNK_OVERLAP
environment variables.

- **config.py**: Added `document_chunk_size` (default: 512) and
  `document_chunk_overlap` (default: 50) configuration fields with validation:
  - Ensures overlap < chunk_size
  - Warns if chunk_size < 100 words
  - Prevents negative overlap values

- **processor.py**: Updated DocumentChunker instantiation to use config
  settings instead of hardcoded values (line 174-177)

- **tests/unit/test_config.py**: Added TestChunkConfigValidation class with
  9 tests covering:
  - Default values
  - Valid configurations
  - Validation errors (overlap >= chunk_size, negative overlap)
  - Warning for small chunk sizes
  - Environment variable loading

- **docs/configuration.md**: Added comprehensive "Document Chunking
  Configuration" section with:
  - Chunk size selection guidance (256-384 vs 512 vs 768-1024 words)
  - Overlap recommendations (10-20% of chunk size)
  - Configuration examples for different use cases
  - Added env vars to reference table

- **docs/semantic-search-architecture.md**: Added "Document Chunking Strategy"
  section with:
  - Chunking process explanation
  - Example showing sliding window behavior
  - Search behavior with chunks
  - Tuning recommendations

- **env.sample**: Added complete "Semantic Search & Vector Sync Configuration"
  section with:
  - Vector sync settings
  - Qdrant configuration (3 modes)
  - Ollama embedding service
  - Document chunking configuration

- **docker-compose.yml**: Added commented examples for DOCUMENT_CHUNK_SIZE and
  DOCUMENT_CHUNK_OVERLAP with usage notes

\`\`\`bash
DOCUMENT_CHUNK_SIZE=512

DOCUMENT_CHUNK_OVERLAP=50
\`\`\`

1. \`overlap\` must be less than \`chunk_size\`
2. \`overlap\` cannot be negative
3. Warning issued if \`chunk_size\` < 100 words

**Precise matching** (small notes, specific queries):
\`\`\`bash
DOCUMENT_CHUNK_SIZE=256
DOCUMENT_CHUNK_OVERLAP=25
\`\`\`

**Balanced** (default, general purpose):
\`\`\`bash
DOCUMENT_CHUNK_SIZE=512
DOCUMENT_CHUNK_OVERLAP=50
\`\`\`

**Contextual** (long documents, broader topics):
\`\`\`bash
DOCUMENT_CHUNK_SIZE=1024
DOCUMENT_CHUNK_OVERLAP=100
\`\`\`

✅ **User control** - Tune chunking to match embedding model capabilities
✅ **Experimentation** - Test different chunk sizes for optimal results
✅ **Model alignment** - Match chunk size to embedding context window
✅ **Backward compatible** - Defaults maintain existing behavior
✅ **Well validated** - Comprehensive tests prevent misconfiguration

All 22 config validation tests pass (9 new tests for chunking):
- Default values work correctly
- Validation prevents invalid configurations
- Environment variables load properly
- Warning system works as expected

With configurable chunk sizes, users can now experiment with different Ollama
embedding models and tune chunk parameters for optimal semantic search quality.

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

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

env.sample

@@ -124,3 +124,75 @@ ENABLE_CUSTOM_PROCESSOR=false
 
 # Comma-separated MIME types your processor supports
 #CUSTOM_PROCESSOR_TYPES=application/pdf,image/jpeg,image/png
+
+# ============================================
+# Semantic Search & Vector Sync Configuration
+# ============================================
+# EXPERIMENTAL: Semantic search for Notes app (multi-app support planned)
+# Requires: Qdrant vector database + Ollama embedding service
+# Disabled by default
+
+# Enable background vector indexing
+VECTOR_SYNC_ENABLED=false
+
+# Document scan interval in seconds (default: 300 = 5 minutes)
+# How often to check for new/updated documents
+#VECTOR_SYNC_SCAN_INTERVAL=300
+
+# Concurrent indexing workers (default: 3)
+# Number of parallel workers for embedding generation
+#VECTOR_SYNC_PROCESSOR_WORKERS=3
+
+# Max queued documents (default: 10000)
+# Maximum documents waiting to be processed
+#VECTOR_SYNC_QUEUE_MAX_SIZE=10000
+
+# ============================================
+# Qdrant Vector Database Configuration
+# ============================================
+# Choose ONE of three modes:
+# 1. In-memory mode (default): Set neither QDRANT_URL nor QDRANT_LOCATION
+# 2. Persistent local: Set QDRANT_LOCATION=/path/to/data
+# 3. Network mode: Set QDRANT_URL=http://qdrant:6333
+
+# Network mode: URL to Qdrant service
+#QDRANT_URL=http://qdrant:6333
+
+# Local mode: Path to store vectors (use :memory: for in-memory)
+#QDRANT_LOCATION=:memory:
+
+# API key for network mode (optional)
+#QDRANT_API_KEY=
+
+# Collection name (optional - auto-generated if not set)
+# Auto-generation format: {deployment-id}-{model-name}
+# Allows safe model switching and multi-server deployments
+#QDRANT_COLLECTION=nextcloud_content
+
+# ============================================
+# Ollama Embedding Service Configuration
+# ============================================
+# Ollama endpoint for embeddings (if not set, uses SimpleEmbeddingProvider fallback)
+#OLLAMA_BASE_URL=http://ollama:11434
+
+# Embedding model to use (default: nomic-embed-text, 768 dimensions)
+# Changing this creates a new collection (requires re-embedding all documents)
+#OLLAMA_EMBEDDING_MODEL=nomic-embed-text
+
+# Verify SSL certificates (default: true)
+#OLLAMA_VERIFY_SSL=true
+
+# ============================================
+# Document Chunking Configuration
+# ============================================
+# Configure how documents are split before embedding
+
+# Words per chunk (default: 512)
+# Smaller chunks (256-384): More precise, less context, more storage
+# Larger chunks (768-1024): More context, less precise, less storage
+#DOCUMENT_CHUNK_SIZE=512
+
+# Overlapping words between chunks (default: 50)
+# Recommended: 10-20% of chunk size
+# Preserves context across chunk boundaries
+#DOCUMENT_CHUNK_OVERLAP=50