perf(sync): batch file indexing in core

src/basic_memory/cli/commands/db.py

@@ -1,5 +1,6 @@
 """Database management commands."""
 
+from dataclasses import dataclass
 from pathlib import Path
 
 import typer
@@ -12,13 +13,47 @@
 from basic_memory.cli.app import app
 from basic_memory.cli.commands.command_utils import run_with_cleanup
 from basic_memory.config import ConfigManager, ProjectMode
+from basic_memory.indexing import IndexProgress
 from basic_memory.repository import ProjectRepository
 from basic_memory.services.initialization import reconcile_projects_with_config
 from basic_memory.sync.sync_service import get_sync_service
 
 console = Console()
 
 
+@dataclass(slots=True)
+class EmbeddingProgress:
+    """Typed CLI progress payload for embedding backfills."""
+
+    entity_id: int
+    index: int
+    total: int
+
+
+def _format_eta(seconds: float | None) -> str:
+    """Render a compact ETA string for CLI progress descriptions."""
+    if seconds is None:
+        return "--:--"
+
+    whole_seconds = max(int(seconds), 0)
+    minutes, remaining_seconds = divmod(whole_seconds, 60)
+    hours, remaining_minutes = divmod(minutes, 60)
+    if hours:
+        return f"{hours:d}:{remaining_minutes:02d}:{remaining_seconds:02d}"
+    return f"{remaining_minutes:02d}:{remaining_seconds:02d}"
+
+
+def _format_index_progress(progress: IndexProgress) -> str:
+    """Render typed index progress as a compact Rich task description."""
+    files_per_minute = int(progress.files_per_minute) if progress.files_per_minute else 0
+    return (
+        "  Indexing files... "
+        f"{progress.files_processed}/{progress.files_total} files | "
+        f"{progress.batches_completed}/{progress.batches_total} batches | "
+        f"{files_per_minute}/min | ETA {_format_eta(progress.eta_seconds)}"
+    )
+
+
 async def _reindex_projects(app_config):
     """Reindex all projects in a single async context.
 
@@ -185,10 +220,34 @@ async def _reindex(app_config, search: bool, embeddings: bool, project: str | No
             console.print(f"\n[bold]Project: [cyan]{proj.name}[/cyan][/bold]")
 
             if search:
-                console.print("  Rebuilding full-text search index...")
                 sync_service = await get_sync_service(proj)
                 sync_dir = Path(proj.path)
-                await sync_service.sync(sync_dir, project_name=proj.name)
+                with Progress(
+                    SpinnerColumn(),
+                    TextColumn("[progress.description]{task.description}"),
+                    BarColumn(),
+                    TaskProgressColumn(),
+                    console=console,
+                ) as progress:
+                    task = progress.add_task("  Indexing files... scanning changes", total=1)
+
+                    async def on_index_progress(update: IndexProgress) -> None:
+                        total = update.files_total or 1
+                        completed = update.files_processed if update.files_total else 1
+                        progress.update(
+                            task,
+                            description=_format_index_progress(update),
+                            total=total,
+                            completed=min(completed, total),
+                        )
+
+                    await sync_service.sync(
+                        sync_dir,
+                        project_name=proj.name,
+                        progress_callback=on_index_progress,
+                    )
+                    progress.update(task, completed=progress.tasks[task].total or 1)
+
                 console.print("  [green]✓[/green] Full-text search index rebuilt")
 
             if embeddings:
@@ -213,7 +272,16 @@ async def _reindex(app_config, search: bool, embeddings: bool, project: str | No
                     task = progress.add_task("  Embedding entities...", total=None)
 
                     def on_progress(entity_id, index, total):
-                        progress.update(task, total=total, completed=index)
+                        embedding_progress = EmbeddingProgress(
+                            entity_id=entity_id,
+                            index=index,
+                            total=total,
+                        )
+                        progress.update(
+                            task,
+                            total=embedding_progress.total,
+                            completed=embedding_progress.index,
+                        )
 
                     stats = await search_service.reindex_vectors(progress_callback=on_progress)
                     progress.update(task, completed=stats["total_entities"])

src/basic_memory/config.py

@@ -193,6 +193,11 @@ class BasicMemoryConfig(BaseSettings):
         description="Batch size for embedding generation.",
         gt=0,
     )
+    semantic_embedding_request_concurrency: int = Field(
+        default=4,
+        description="Maximum number of concurrent provider requests for batched embedding generation when the active provider supports request-level concurrency.",
+        gt=0,
+    )
     semantic_embedding_sync_batch_size: int = Field(
         default=64,
         description="Batch size for vector sync orchestration flushes.",
@@ -286,6 +291,31 @@ class BasicMemoryConfig(BaseSettings):
         description="Maximum number of files to process concurrently during sync. Limits memory usage on large projects (2000+ files). Lower values reduce memory consumption.",
         gt=0,
     )
+    index_batch_size: int = Field(
+        default=32,
+        description="Maximum number of changed files to load into one indexing batch.",
+        gt=0,
+    )
+    index_batch_max_bytes: int = Field(
+        default=8 * 1024 * 1024,
+        description="Maximum total bytes to load into one indexing batch. Large files still run as single-file batches.",
+        gt=0,
+    )
+    index_parse_max_concurrent: int = Field(
+        default=8,
+        description="Maximum number of markdown parse tasks to run concurrently inside one indexing batch.",
+        gt=0,
+    )
+    index_entity_max_concurrent: int = Field(
+        default=4,
+        description="Maximum number of entity create/update tasks to run concurrently inside one indexing batch.",
+        gt=0,
+    )
+    index_metadata_update_max_concurrent: int = Field(
+        default=4,
+        description="Maximum number of metadata/search refresh tasks to run concurrently inside one indexing batch.",
+        gt=0,
+    )
 
     kebab_filenames: bool = Field(
         default=False,

src/basic_memory/db.py

@@ -44,101 +44,6 @@
 _session_maker: Optional[async_sessionmaker[AsyncSession]] = None
 
 
-async def _needs_semantic_embedding_backfill(
-    app_config: BasicMemoryConfig,
-    session_maker: async_sessionmaker[AsyncSession],
-) -> bool:
-    """Check if entities exist but vector embeddings are empty.
-
-    This is the reliable way to detect that embeddings need to be generated,
-    regardless of how migrations were applied (fresh DB, upgrade, reset, etc.).
-    """
-    if not app_config.semantic_search_enabled:
-        return False
-
-    try:
-        async with scoped_session(session_maker) as session:
-            entity_count = (
-                await session.execute(text("SELECT COUNT(*) FROM entity"))
-            ).scalar() or 0
-            if entity_count == 0:
-                return False
-
-            # Check if vector chunks table exists and is empty
-            embedding_count = (
-                await session.execute(text("SELECT COUNT(*) FROM search_vector_chunks"))
-            ).scalar() or 0
-
-            return embedding_count == 0
-    except Exception as exc:
-        # Table might not exist yet (pre-migration)
-        logger.debug(f"Could not check embedding status: {exc}")
-        return False
-
-
-async def _run_semantic_embedding_backfill(
-    app_config: BasicMemoryConfig,
-    session_maker: async_sessionmaker[AsyncSession],
-) -> None:
-    """Backfill semantic embeddings for all active projects/entities."""
-    if not app_config.semantic_search_enabled:
-        logger.info("Skipping automatic semantic embedding backfill: semantic search is disabled.")
-        return
-
-    async with scoped_session(session_maker) as session:
-        project_result = await session.execute(
-            text("SELECT id, name FROM project WHERE is_active = :is_active ORDER BY id"),
-            {"is_active": True},
-        )
-        projects = [(int(row[0]), str(row[1])) for row in project_result.fetchall()]
-
-    if not projects:
-        logger.info("Skipping automatic semantic embedding backfill: no active projects found.")
-        return
-
-    repository_class = (
-        PostgresSearchRepository
-        if app_config.database_backend == DatabaseBackend.POSTGRES
-        else SQLiteSearchRepository
-    )
-
-    total_entities = 0
-    for project_id, project_name in projects:
-        async with scoped_session(session_maker) as session:
-            entity_result = await session.execute(
-                text("SELECT id FROM entity WHERE project_id = :project_id ORDER BY id"),
-                {"project_id": project_id},
-            )
-            entity_ids = [int(row[0]) for row in entity_result.fetchall()]
-
-        if not entity_ids:
-            continue
-
-        total_entities += len(entity_ids)
-        logger.info(
-            "Automatic semantic embedding backfill: "
-            f"project={project_name}, entities={len(entity_ids)}"
-        )
-
-        search_repository = repository_class(
-            session_maker,
-            project_id=project_id,
-            app_config=app_config,
-        )
-        batch_result = await search_repository.sync_entity_vectors_batch(entity_ids)
-        if batch_result.entities_failed > 0:
-            logger.warning(
-                "Automatic semantic embedding backfill encountered entity failures: "
-                f"project={project_name}, failed={batch_result.entities_failed}, "
-                f"failed_entity_ids={batch_result.failed_entity_ids}"
-            )
-
-    logger.info(
-        "Automatic semantic embedding backfill complete: "
-        f"projects={len(projects)}, entities={total_entities}"
-    )
-
-
 class DatabaseType(Enum):
     """Types of supported databases."""
 
@@ -521,14 +426,6 @@ async def run_migrations(
         else:
             await SQLiteSearchRepository(session_maker, 1).init_search_index()
 
-        # Check if backfill is needed — actual backfill runs in background
-        # from the MCP server lifespan to avoid blocking startup.
-        if await _needs_semantic_embedding_backfill(app_config, session_maker):
-            logger.info(
-                "Semantic embeddings missing — backfill will run in background after startup"
-            )
-        else:
-            logger.info("Semantic embeddings: up to date")
     except Exception as e:  # pragma: no cover
         logger.error(f"Error running migrations: {e}")
         raise

src/basic_memory/indexing/__init__.py

@@ -0,0 +1,29 @@
+"""Reusable indexing primitives shared by local sync and future remote callers."""
+
+from basic_memory.indexing.batch_indexer import BatchIndexer
+from basic_memory.indexing.batching import build_index_batches
+from basic_memory.indexing.models import (
+    IndexedEntity,
+    IndexBatch,
+    IndexFileMetadata,
+    IndexFileWriter,
+    IndexFrontmatterUpdate,
+    IndexFrontmatterWriteResult,
+    IndexingBatchResult,
+    IndexInputFile,
+    IndexProgress,
+)
+
+__all__ = [
+    "BatchIndexer",
+    "IndexedEntity",
+    "IndexBatch",
+    "IndexFileMetadata",
+    "IndexFileWriter",
+    "IndexFrontmatterUpdate",
+    "IndexFrontmatterWriteResult",
+    "IndexingBatchResult",
+    "IndexInputFile",
+    "IndexProgress",
+    "build_index_batches",
+]