feat: add maintenance system with index reconciliation

Complete maintenance subsystem for index health and cleanup:

Endpoints:
- GET /maintenance/health - lightweight (or detailed) health check
- POST /maintenance/cleanup/all - full orphan cleanup
- POST /maintenance/cleanup/vectors - purge orphan vector chunks
- POST /maintenance/cleanup/graph - purge orphan graph nodes
- POST /maintenance/reconcile-index - cleanup + reindex missing pages

Bidirectional orphan detection:
- find_documents_without_vectors() in GraphService
- find_chunks_without_graph_nodes() in VectorService

Redis integration:
- Tracks last_cleanup timestamp for scheduler visibility

Config additions:
- Document store, volatile cache, and maintenance settings
- VectorServiceDep and GraphServiceDep type aliases

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

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

src/services/vector_service.py

@@ -356,3 +356,189 @@ class VectorService:
                 collections=[],
                 total=0
             )
+
+    # ========== Cleanup Methods ==========
+
+    async def delete_document_chunks(
+        self,
+        document_id: str,
+        user: str
+    ) -> int:
+        """
+        Delete all chunks for a document (Document Store).
+
+        Args:
+            document_id: Document UUID
+            user: User identifier
+
+        Returns:
+            Number of chunks deleted
+        """
+        collection_name = get_qdrant_collection_name(user)
+
+        try:
+            deleted_count = await self.qdrant.delete_by_filter(
+                collection_name=collection_name,
+                filter_conditions={"document_id": document_id}
+            )
+
+            logger.info(f"Deleted chunks for document {document_id}")
+            return deleted_count
+
+        except Exception as e:
+            logger.error(f"Failed to delete chunks for document {document_id}: {e}", exc_info=True)
+            return 0
+
+    async def delete_collection_chunks(
+        self,
+        collection_id: str,
+        user: str
+    ) -> int:
+        """
+        Delete all chunks for a document collection.
+
+        Args:
+            collection_id: Collection UUID
+            user: User identifier
+
+        Returns:
+            Number of chunks deleted
+        """
+        collection_name = get_qdrant_collection_name(user)
+
+        try:
+            deleted_count = await self.qdrant.delete_by_filter(
+                collection_name=collection_name,
+                filter_conditions={"collection_id": collection_id}
+            )
+
+            logger.info(f"Deleted chunks for collection {collection_id}")
+            return deleted_count
+
+        except Exception as e:
+            logger.error(f"Failed to delete chunks for collection {collection_id}: {e}", exc_info=True)
+            return 0
+
+    async def get_all_chunk_references(
+        self,
+        user: str
+    ) -> List[Dict[str, Any]]:
+        """
+        Get all chunk references for orphan detection.
+
+        Returns list of {id, page_id, document_id} for all chunks.
+
+        Args:
+            user: User identifier
+
+        Returns:
+            List of chunk references
+        """
+        collection_name = get_qdrant_collection_name(user)
+
+        try:
+            # Check if collection exists
+            exists = await self.qdrant.collection_exists(collection_name)
+            if not exists:
+                return []
+
+            all_points = await self.qdrant.scroll_all_points(
+                collection_name=collection_name,
+                batch_size=100,
+                with_payload=True
+            )
+
+            references = []
+            for point in all_points:
+                payload = point.get("payload", {})
+                references.append({
+                    "chunk_id": point["id"],
+                    "page_id": payload.get("page_id"),
+                    "document_id": payload.get("document_id"),
+                    "collection_id": payload.get("collection_id"),
+                    "doc_type": payload.get("doc_type", "wiki")
+                })
+
+            logger.info(f"Found {len(references)} chunks for user {user}")
+            return references
+
+        except Exception as e:
+            logger.error(f"Failed to get chunk references: {e}", exc_info=True)
+            return []
+
+    async def purge_chunks_by_ids(
+        self,
+        user: str,
+        chunk_ids: List[str]
+    ) -> int:
+        """
+        Delete specific chunks by their IDs.
+
+        Args:
+            user: User identifier
+            chunk_ids: List of chunk IDs to delete
+
+        Returns:
+            Number of chunks deleted
+        """
+        if not chunk_ids:
+            return 0
+
+        collection_name = get_qdrant_collection_name(user)
+
+        try:
+            deleted_count = await self.qdrant.delete_by_ids(
+                collection_name=collection_name,
+                point_ids=chunk_ids
+            )
+
+            logger.info(f"Purged {deleted_count} orphan chunks for user {user}")
+            return deleted_count
+
+        except Exception as e:
+            logger.error(f"Failed to purge chunks: {e}", exc_info=True)
+            return 0
+
+    def find_chunks_without_graph_nodes(
+        self,
+        chunk_references: List[Dict[str, Any]],
+        graph_references: List[Dict[str, Any]]
+    ) -> List[str]:
+        """
+        Find vector chunks that have no corresponding graph Document node.
+
+        Used for bidirectional orphan detection - vectors without graph representation.
+
+        Args:
+            chunk_references: List from get_all_chunk_references()
+            graph_references: List from GraphService.get_all_document_references()
+
+        Returns:
+            List of orphan chunk IDs
+        """
+        # Build sets of IDs that have graph nodes
+        graph_page_ids = {
+            ref.get("page_id") for ref in graph_references
+            if ref.get("doc_type") == "wiki" and ref.get("page_id")
+        }
+        graph_doc_ids = {
+            ref.get("document_id") for ref in graph_references
+            if ref.get("doc_type") != "wiki" and ref.get("document_id")
+        }
+
+        # Find chunks with no graph node
+        orphan_ids = []
+        for chunk in chunk_references:
+            doc_type = chunk.get("doc_type", "wiki")
+
+            if doc_type == "wiki":
+                page_id = chunk.get("page_id")
+                if page_id and page_id not in graph_page_ids:
+                    orphan_ids.append(chunk["chunk_id"])
+            else:
+                document_id = chunk.get("document_id")
+                if document_id and document_id not in graph_doc_ids:
+                    orphan_ids.append(chunk["chunk_id"])
+
+        logger.info(f"Found {len(orphan_ids)} vector chunks without graph nodes")
+        return orphan_ids