feat: Implement BM25 hybrid search with native Qdrant RRF fusion

Replace custom keyword/fuzzy search algorithms with industry-standard BM25
sparse vectors, combined with dense semantic vectors using Qdrant's native
Reciprocal Rank Fusion (RRF). This consolidates search architecture and
improves relevance for both semantic and keyword queries.

Key changes:
- Add fastembed dependency for BM25 sparse vector generation
- Update Qdrant collection schema to support named vectors (dense + sparse)
- Create BM25SparseEmbeddingProvider using FastEmbed's Qdrant/bm25 model
- Implement BM25HybridSearchAlgorithm with native Qdrant RRF prefetch
- Update document processor to generate both dense and sparse embeddings
- Simplify nc_semantic_search() tool to use BM25 hybrid only
- Remove legacy keyword.py, fuzzy.py, and custom hybrid.py (736 lines)
- Update ADR-014 with implementation notes and test results

Benefits:
- Consolidated architecture (single Qdrant database)
- Native database-level RRF fusion (more efficient)
- Industry-standard BM25 (replaces brittle custom keyword search)
- Better relevance across semantic and keyword queries
- Simplified codebase (-285 net lines)

Tests: All 125 tests passing (118 unit, 7 integration)

Implements ADR-014: Replace Custom Keyword Search with BM25 Hybrid Search

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

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

nextcloud_mcp_server/embedding/service.py

@@ -4,6 +4,7 @@ import logging
 import os
 
 from .base import EmbeddingProvider
+from .bm25_provider import BM25SparseEmbeddingProvider
 from .ollama_provider import OllamaEmbeddingProvider
 from .simple_provider import SimpleEmbeddingProvider
 
@@ -109,3 +110,20 @@ def get_embedding_service() -> EmbeddingService:
     if _embedding_service is None:
         _embedding_service = EmbeddingService()
     return _embedding_service
+
+
+# BM25 sparse embedding singleton
+_bm25_service: BM25SparseEmbeddingProvider | None = None
+
+
+def get_bm25_service() -> BM25SparseEmbeddingProvider:
+    """
+    Get singleton BM25 sparse embedding service instance.
+
+    Returns:
+        Global BM25SparseEmbeddingProvider instance
+    """
+    global _bm25_service
+    if _bm25_service is None:
+        _bm25_service = BM25SparseEmbeddingProvider()
+    return _bm25_service