feat: rich product metadata extraction from STEP files

Extract volume, surface area, part count, assembly hierarchy, and
complexity from STEP files via OCC B-rep analysis.

Backend:
- extract_rich_metadata() in step_processor.py: computes per-part volume
  (BRepGProp), surface area, triangle/vertex count, assembly depth,
  instance count, complexity score, largest part identification
- cad_metadata JSONB column on Product model (DB migration)
- Auto-populated during STEP processing (non-fatal, 10s timeout)
- Also stored in cad_files.mesh_attributes["rich_metadata"]
- Batch re-extract endpoint: POST /admin/settings/reextract-rich-metadata

AI Agent:
- search_products returns part_count, volume_cm3, complexity, largest_part
- query_database tool description documents cad_metadata schema

Frontend:
- ProductDetail page: CAD Metadata section with stat cards
  (parts, volume, surface area, complexity, triangles, assembly depth)
- Admin System Tools: "Re-extract Rich Metadata" button for backfill

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/alembic/versions/a68e8c6fb61b_add_cad_metadata_to_products.py

@@ -0,0 +1,30 @@
+"""add cad_metadata to products
+
+Revision ID: a68e8c6fb61b
+Revises: 69964e910545
+Create Date: 2026-03-15 17:44:08.184376
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = 'a68e8c6fb61b'
+down_revision: Union[str, None] = '69964e910545'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column('products', sa.Column('cad_metadata', postgresql.JSONB(astext_type=sa.Text()), nullable=True))
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column('products', 'cad_metadata')
+    # ### end Alembic commands ###

backend/app/api/routers/admin.py

@@ -532,6 +532,16 @@ async def reextract_all_metadata(
     return {"queued": queued, "message": f"Queued {queued} CAD file(s) for metadata re-extraction"}
 
 
+@router.post("/settings/reextract-rich-metadata", status_code=status.HTTP_202_ACCEPTED)
+async def reextract_rich_metadata(
+    admin: User = Depends(require_global_admin),
+):
+    """Queue a batch task to re-compute volume, surface area, complexity for all products with STEP files."""
+    from app.tasks.step_tasks import reextract_rich_metadata_task
+    reextract_rich_metadata_task.delay()
+    return {"queued": True, "message": "Rich metadata re-extraction task queued"}
+
+
 @router.post("/settings/generate-missing-canonical-scenes", status_code=status.HTTP_202_ACCEPTED)
 async def generate_missing_canonical_scenes(
     admin: User = Depends(require_global_admin),

backend/app/domains/pipeline/tasks/extract_metadata.py

@@ -109,6 +109,57 @@ def process_step_file(self, cad_file_id: str):
             pl.step_error("process_step_file", f"STEP metadata extraction failed: {exc}", exc)
             r.delete(lock_key)  # release lock so a retry can proceed
             raise self.retry(exc=exc, countdown=60, max_retries=3)
+
+        # Extract rich metadata (volume, surface area, complexity, etc.) — non-fatal
+        try:
+            from sqlalchemy import create_engine, update as sql_update
+            from sqlalchemy.orm import Session as SyncSession
+            from app.config import settings as cfg
+            from app.services.step_processor import extract_rich_metadata
+            from app.models.cad_file import CadFile
+            from app.models.product import Product
+            from app.core.tenant_context import set_tenant_context_sync
+
+            eng = create_engine(cfg.database_url_sync)
+            try:
+                # Load stored_path for the cad file
+                with SyncSession(eng) as session:
+                    set_tenant_context_sync(session, _tenant_id)
+                    cad_file = session.get(CadFile, cad_file_id)
+                    step_path = cad_file.stored_path if cad_file else None
+
+                if step_path:
+                    rich_meta = extract_rich_metadata(str(step_path))
+                    if rich_meta and rich_meta.get("part_count", 0) > 0:
+                        with SyncSession(eng) as session:
+                            set_tenant_context_sync(session, _tenant_id)
+                            # Merge into cad_files.mesh_attributes
+                            cad_file = session.get(CadFile, cad_file_id)
+                            if cad_file:
+                                existing_attrs = cad_file.mesh_attributes or {}
+                                existing_attrs["rich_metadata"] = rich_meta
+                                session.execute(
+                                    sql_update(CadFile)
+                                    .where(CadFile.id == cad_file_id)
+                                    .values(mesh_attributes=existing_attrs)
+                                )
+                            # Update all active products linked to this CAD file
+                            session.execute(
+                                sql_update(Product)
+                                .where(Product.cad_file_id == cad_file_id, Product.is_active.is_(True))
+                                .values(cad_metadata=rich_meta)
+                            )
+                            session.commit()
+                        logger.info(
+                            f"Rich metadata extracted for cad_file {cad_file_id}: "
+                            f"{rich_meta.get('part_count')} parts, "
+                            f"{rich_meta.get('total_volume_cm3', 0):.1f} cm³"
+                        )
+            finally:
+                eng.dispose()
+        except Exception:
+            logger.exception(f"Rich metadata extraction failed for cad_file {cad_file_id} (non-fatal)")
+
     finally:
         r.delete(lock_key)  # always release on completion or unhandled error
 
@@ -203,6 +254,70 @@ def _auto_populate_materials_for_cad(cad_file_id: str, tenant_id: str | None = N
     eng.dispose()
 
 
+@celery_app.task(name="app.tasks.step_tasks.reextract_rich_metadata_task", queue="step_processing")
+def reextract_rich_metadata_task():
+    """Batch re-extract rich metadata (volume, surface area, complexity) for all completed CAD files."""
+    from sqlalchemy import create_engine, select as sql_select, update as sql_update
+    from sqlalchemy.orm import Session as SyncSession
+    from app.config import settings as cfg
+    from app.models.cad_file import CadFile, ProcessingStatus
+    from app.models.product import Product
+    from app.core.tenant_context import set_tenant_context_sync
+
+    sync_url = cfg.database_url.replace("+asyncpg", "")
+    eng = create_engine(sync_url)
+    updated = 0
+    failed = 0
+
+    try:
+        with SyncSession(eng) as session:
+            cad_files = session.execute(
+                sql_select(CadFile).where(
+                    CadFile.processing_status == ProcessingStatus.completed,
+                    CadFile.stored_path.isnot(None),
+                )
+            ).scalars().all()
+            cad_entries = [(str(cf.id), cf.stored_path, cf.tenant_id) for cf in cad_files]
+
+        for cad_file_id, step_path, tenant_id in cad_entries:
+            try:
+                from app.services.step_processor import extract_rich_metadata
+                rich_meta = extract_rich_metadata(str(step_path))
+                if rich_meta and rich_meta.get("part_count", 0) > 0:
+                    with SyncSession(eng) as session:
+                        set_tenant_context_sync(session, tenant_id)
+                        # Update mesh_attributes on cad_file
+                        cad_file = session.get(CadFile, cad_file_id)
+                        if cad_file:
+                            existing_attrs = cad_file.mesh_attributes or {}
+                            existing_attrs["rich_metadata"] = rich_meta
+                            session.execute(
+                                sql_update(CadFile)
+                                .where(CadFile.id == cad_file_id)
+                                .values(mesh_attributes=existing_attrs)
+                            )
+                        # Update all active products linked to this CAD file
+                        session.execute(
+                            sql_update(Product)
+                            .where(Product.cad_file_id == cad_file_id, Product.is_active.is_(True))
+                            .values(cad_metadata=rich_meta)
+                        )
+                        session.commit()
+                    updated += 1
+                    logger.info(
+                        f"reextract_rich_metadata: {cad_file_id} -> "
+                        f"{rich_meta.get('part_count')} parts, "
+                        f"{rich_meta.get('total_volume_cm3', 0):.1f} cm3"
+                    )
+            except Exception:
+                failed += 1
+                logger.exception(f"reextract_rich_metadata failed for cad_file {cad_file_id}")
+    finally:
+        eng.dispose()
+
+    logger.info(f"reextract_rich_metadata_task complete: {updated} updated, {failed} failed")
+
+
 @celery_app.task(name="app.tasks.step_tasks.reextract_cad_metadata", queue="asset_pipeline")
 def reextract_cad_metadata(cad_file_id: str):
     """Re-extract bounding-box dimensions for an already-completed CAD file.

backend/app/domains/products/models.py

@@ -70,6 +70,7 @@ class Product(Base):
     is_active: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
     arbeitspaket: Mapped[str | None] = mapped_column(String(500), nullable=True)
     source_excel: Mapped[str | None] = mapped_column(String(1000), nullable=True)
+    cad_metadata: Mapped[dict | None] = mapped_column(JSONB, nullable=True, default=None)
     tenant_id: Mapped[uuid.UUID | None] = mapped_column(
         UUID(as_uuid=True), ForeignKey("tenants.id"), nullable=True, index=True
     )

backend/app/domains/products/schemas.py

@@ -64,6 +64,7 @@ class ProductOut(BaseModel):
     cad_mesh_attributes: dict | None = None
     arbeitspaket: str | None = None
     cad_render_log: dict | None = None
+    cad_metadata: dict | None = None
     notes: str | None
     is_active: bool
     source_excel: str | None

backend/app/services/chat_service.py

@@ -219,7 +219,7 @@ TOOLS = [
         "type": "function",
         "function": {
             "name": "query_database",
-            "description": "Execute a read-only SQL SELECT query against the database. Key tables/columns: products(id, name, pim_id, category_key, cad_file_id, is_active, tenant_id), orders(id, order_number, status, tenant_id), order_lines(id, order_id, product_id, render_status, material_override, render_overrides), cad_files(id, mesh_attributes->'dimensions_mm' with {x,y,z} in mm, parsed_objects, processing_status). To get product dimensions: JOIN cad_files cf ON cf.id = p.cad_file_id and use cf.mesh_attributes->'dimensions_mm'. Use :tenant_id parameter for tenant filtering. Category is 'category_key' not 'category'.",
+            "description": "Execute a read-only SQL SELECT query against the database. Key tables/columns: products(id, name, pim_id, category_key, cad_file_id, is_active, tenant_id, cad_metadata JSONB), orders(id, order_number, status, tenant_id), order_lines(id, order_id, product_id, render_status, material_override, render_overrides), cad_files(id, mesh_attributes->'dimensions_mm' with {x,y,z} in mm, parsed_objects, processing_status). products.cad_metadata JSONB contains: part_count, unique_part_count, instance_count, assembly_depth, total_volume_cm3, total_surface_area_cm2, total_triangle_count, complexity_score, largest_part (name + volume_cm3). To get product dimensions: JOIN cad_files cf ON cf.id = p.cad_file_id and use cf.mesh_attributes->'dimensions_mm'. Use :tenant_id parameter for tenant filtering. Category is 'category_key' not 'category'.",
             "parameters": {
                 "type": "object",
                 "properties": {
@@ -330,7 +330,12 @@ async def _tool_search_products(db: AsyncSession, tenant_id: str, query: str = "
                cf.processing_status,
                cf.mesh_attributes->'dimensions_mm'->>'x' AS dim_x_mm,
                cf.mesh_attributes->'dimensions_mm'->>'y' AS dim_y_mm,
-               cf.mesh_attributes->'dimensions_mm'->>'z' AS dim_z_mm
+               cf.mesh_attributes->'dimensions_mm'->>'z' AS dim_z_mm,
+               p.cad_metadata->>'part_count' AS part_count,
+               p.cad_metadata->>'total_volume_cm3' AS volume_cm3,
+               p.cad_metadata->>'complexity_score' AS complexity,
+               p.cad_metadata->'largest_part'->>'name' AS largest_part_name,
+               p.cad_metadata->'largest_part'->>'volume_cm3' AS largest_part_volume
         FROM products p
         LEFT JOIN cad_files cf ON cf.id = p.cad_file_id
         WHERE p.tenant_id = :tenant_id

backend/app/services/step_processor.py

@@ -632,6 +632,332 @@ def extract_step_metadata(step_path: str) -> StepMetadata:
         return StepMetadata()
 
 
+def extract_rich_metadata(step_path: str) -> dict:
+    """Extract rich geometric metadata from a STEP file.
+
+    Opens the STEP file via XCAF, walks the assembly tree, and computes
+    volume, surface area, hierarchy depth, triangle/vertex counts, and
+    complexity metrics for each unique part.
+
+    Runs in the worker container (step_processing queue) which has OCP installed.
+    Processing is capped at 10 seconds; volume computation is skipped for files
+    with more than 200 parts.
+
+    Returns a dict with keys: part_count, unique_part_count, instance_count,
+    assembly_depth, total_volume_cm3, total_surface_area_cm2,
+    total_triangle_count, total_vertex_count, largest_part,
+    smallest_dimension_mm, complexity_score.
+    """
+    import time
+
+    t_start = time.monotonic()
+    TIME_LIMIT = 10.0  # seconds
+
+    result: dict = {
+        "part_count": 0,
+        "unique_part_count": 0,
+        "instance_count": 0,
+        "assembly_depth": 0,
+        "total_volume_cm3": 0.0,
+        "total_surface_area_cm2": 0.0,
+        "total_triangle_count": 0,
+        "total_vertex_count": 0,
+        "largest_part": {"name": "", "volume_cm3": 0.0},
+        "smallest_dimension_mm": 0.0,
+        "complexity_score": "low",
+    }
+
+    try:
+        # Import OCC — try OCC.Core first, fall back to OCP
+        _using_ocp = False
+        try:
+            from OCC.Core.STEPCAFControl import STEPCAFControl_Reader
+            from OCC.Core.XCAFDoc import XCAFDoc_DocumentTool
+            from OCC.Core.TDocStd import TDocStd_Document
+            from OCC.Core.TDataStd import TDataStd_Name
+            from OCC.Core.TCollection import TCollection_ExtendedString
+            from OCC.Core.TDF import TDF_LabelSequence
+            from OCC.Core.XCAFDoc import XCAFDoc_ShapeTool
+            from OCC.Core.BRepGProp import brepgprop
+            from OCC.Core.GProp import GProp_GProps
+            from OCC.Core.BRepMesh import BRepMesh_IncrementalMesh
+            from OCC.Core.TopExp import TopExp_Explorer
+            from OCC.Core.TopAbs import TopAbs_FACE
+            from OCC.Core.TopoDS import TopoDS as _TopoDS
+            from OCC.Core.BRep import BRep_Tool
+            from OCC.Core.TopLoc import TopLoc_Location
+            from OCC.Core.Bnd import Bnd_Box
+            from OCC.Core.BRepBndLib import brepbndlib as _brepbndlib_mod
+
+            def _get_components(label, seq):
+                XCAFDoc_ShapeTool.GetComponents(label, seq)
+            def _is_reference(label):
+                return XCAFDoc_ShapeTool.IsReference(label)
+            def _get_referred(label, ref):
+                return XCAFDoc_ShapeTool.GetReferredShape(label, ref)
+            def _get_shape(st, label):
+                return st.GetShape(label)
+            def _get_name_id():
+                return TDataStd_Name.GetID()
+            def _brepbndlib_add(shape, bbox):
+                _brepbndlib_mod.Add(shape, bbox)
+        except ImportError:
+            from OCP.STEPCAFControl import STEPCAFControl_Reader  # type: ignore[no-redef]
+            from OCP.XCAFDoc import XCAFDoc_DocumentTool  # type: ignore[no-redef]
+            from OCP.TDocStd import TDocStd_Document  # type: ignore[no-redef]
+            from OCP.TDataStd import TDataStd_Name  # type: ignore[no-redef]
+            from OCP.TCollection import TCollection_ExtendedString  # type: ignore[no-redef]
+            from OCP.TDF import TDF_LabelSequence, TDF_Label  # type: ignore[no-redef]
+            from OCP.XCAFDoc import XCAFDoc_ShapeTool  # type: ignore[no-redef]
+            from OCP.BRepGProp import brepgprop  # type: ignore[no-redef]
+            from OCP.GProp import GProp_GProps  # type: ignore[no-redef]
+            from OCP.BRepMesh import BRepMesh_IncrementalMesh  # type: ignore[no-redef]
+            from OCP.TopExp import TopExp_Explorer  # type: ignore[no-redef]
+            from OCP.TopAbs import TopAbs_FACE  # type: ignore[no-redef]
+            from OCP.TopoDS import TopoDS as _TopoDS  # type: ignore[no-redef]
+            from OCP.BRep import BRep_Tool  # type: ignore[no-redef]
+            from OCP.TopLoc import TopLoc_Location  # type: ignore[no-redef]
+            from OCP.Bnd import Bnd_Box  # type: ignore[no-redef]
+            from OCP.BRepBndLib import BRepBndLib as _brepbndlib_mod  # type: ignore[no-redef]
+            _using_ocp = True
+
+            def _get_components(label, seq):
+                XCAFDoc_ShapeTool.GetComponents_s(label, seq)
+            def _is_reference(label):
+                return XCAFDoc_ShapeTool.IsReference_s(label)
+            def _get_referred(label, ref):
+                return XCAFDoc_ShapeTool.GetReferredShape_s(label, ref)
+            def _get_shape(st, label):
+                return st.GetShape_s(label)
+            def _get_name_id():
+                return TDataStd_Name.GetID_s()
+            def _brepbndlib_add(shape, bbox):
+                _brepbndlib_mod.Add_s(shape, bbox)
+
+        # ── Read STEP file ────────────────────────────────────────────────
+        doc = TDocStd_Document(TCollection_ExtendedString("MDTV-CAF"))
+        reader = STEPCAFControl_Reader()
+        reader.SetColorMode(True)
+        reader.SetNameMode(True)
+        status = reader.ReadFile(str(step_path))
+        if not reader.Transfer(doc):
+            logger.warning("extract_rich_metadata: XCAF transfer failed for %s", step_path)
+            return result
+
+        if _using_ocp:
+            shape_tool = XCAFDoc_DocumentTool.ShapeTool_s(doc.Main())
+        else:
+            shape_tool = XCAFDoc_DocumentTool.ShapeTool(doc.Main())
+
+        free_labels = TDF_LabelSequence() if _using_ocp else []
+        if _using_ocp:
+            shape_tool.GetFreeShapes(free_labels)
+        else:
+            shape_tool.GetFreeShapes(free_labels)
+
+        # ── Walk the XCAF assembly tree ───────────────────────────────────
+        # Collect all leaf shapes with their names, tracking unique shapes via IsSame()
+        leaf_shapes: list[tuple] = []  # (name, shape)
+        unique_shapes: list = []       # list of (name, shape) for distinct shapes
+        max_depth = 0
+
+        def _label_name(label) -> str:
+            name_attr = TDataStd_Name()
+            if label.FindAttribute(_get_name_id(), name_attr):
+                return name_attr.Get().ToExtString()
+            return ""
+
+        def _walk(label, depth: int) -> None:
+            nonlocal max_depth
+            if depth > max_depth:
+                max_depth = depth
+
+            # Dereference component references
+            actual_label = label
+            if _is_reference(label):
+                if _using_ocp:
+                    ref_label = TDF_Label()
+                    if _get_referred(label, ref_label):
+                        actual_label = ref_label
+                else:
+                    from OCC.Core.TDF import TDF_Label as _TDF_Label
+                    ref_label = _TDF_Label()
+                    if _get_referred(label, ref_label):
+                        actual_label = ref_label
+
+            components = TDF_LabelSequence() if _using_ocp else []
+            _get_components(actual_label, components)
+
+            n_components = components.Length() if _using_ocp else len(components)
+            if n_components == 0:
+                # Leaf node
+                name = _label_name(label) or _label_name(actual_label)
+                shape = _get_shape(shape_tool, actual_label)
+                if shape is not None and not shape.IsNull():
+                    leaf_shapes.append((name, shape))
+                    # Check uniqueness via IsSame
+                    is_unique = True
+                    for _, existing_shape in unique_shapes:
+                        if shape.IsSame(existing_shape):
+                            is_unique = False
+                            break
+                    if is_unique:
+                        unique_shapes.append((name, shape))
+            else:
+                if _using_ocp:
+                    for i in range(1, n_components + 1):
+                        _walk(components.Value(i), depth + 1)
+                else:
+                    for child in components:
+                        _walk(child, depth + 1)
+
+        n_free = free_labels.Length() if _using_ocp else len(free_labels)
+        for i in range(1, n_free + 1) if _using_ocp else range(len(free_labels)):
+            label = free_labels.Value(i) if _using_ocp else free_labels[i]
+            _walk(label, 0)
+
+        result["part_count"] = len(leaf_shapes)
+        result["unique_part_count"] = len(unique_shapes)
+        result["instance_count"] = len(leaf_shapes)
+        result["assembly_depth"] = max_depth
+
+        # ── Volume and surface area per unique shape ──────────────────────
+        skip_volume = len(leaf_shapes) > 200
+        if skip_volume:
+            logger.info(
+                "extract_rich_metadata: %d parts > 200, skipping volume computation",
+                len(leaf_shapes),
+            )
+
+        total_volume = 0.0       # mm³
+        total_area = 0.0         # mm²
+        largest_name = ""
+        largest_volume = 0.0     # mm³
+
+        # Build a count of how many instances each unique shape has
+        instance_counts: dict[int, int] = {}  # index in unique_shapes → count
+        for _, leaf_shape in leaf_shapes:
+            for idx, (_, u_shape) in enumerate(unique_shapes):
+                if leaf_shape.IsSame(u_shape):
+                    instance_counts[idx] = instance_counts.get(idx, 0) + 1
+                    break
+
+        if not skip_volume:
+            for idx, (name, shape) in enumerate(unique_shapes):
+                if time.monotonic() - t_start > TIME_LIMIT:
+                    logger.warning("extract_rich_metadata: time limit reached, stopping volume computation")
+                    break
+
+                count = instance_counts.get(idx, 1)
+                try:
+                    props = GProp_GProps()
+                    if _using_ocp:
+                        brepgprop.VolumeProperties_s(shape, props)
+                    else:
+                        brepgprop.VolumeProperties(shape, props)
+                    vol = abs(props.Mass())  # mm³, abs() for reversed shapes
+                    total_volume += vol * count
+                    if vol > largest_volume:
+                        largest_volume = vol
+                        largest_name = name
+                except Exception:
+                    pass
+
+                try:
+                    props = GProp_GProps()
+                    if _using_ocp:
+                        brepgprop.SurfaceProperties_s(shape, props)
+                    else:
+                        brepgprop.SurfaceProperties(shape, props)
+                    area = abs(props.Mass())  # mm²
+                    total_area += area * count
+                except Exception:
+                    pass
+
+        result["total_volume_cm3"] = round(total_volume / 1000.0, 2)     # mm³ → cm³
+        result["total_surface_area_cm2"] = round(total_area / 100.0, 2)  # mm² → cm²
+        result["largest_part"] = {
+            "name": largest_name,
+            "volume_cm3": round(largest_volume / 1000.0, 2),
+        }
+
+        # ── Smallest dimension across all parts ──────────────────────────
+        smallest_dim = float("inf")
+        for _, shape in unique_shapes:
+            if time.monotonic() - t_start > TIME_LIMIT:
+                break
+            try:
+                bbox = Bnd_Box()
+                _brepbndlib_add(shape, bbox)
+                xmin, ymin, zmin, xmax, ymax, zmax = bbox.Get()
+                dims = [abs(xmax - xmin), abs(ymax - ymin), abs(zmax - zmin)]
+                min_dim = min(d for d in dims if d > 1e-6)  # skip degenerate
+                if min_dim < smallest_dim:
+                    smallest_dim = min_dim
+            except Exception:
+                pass
+        result["smallest_dimension_mm"] = round(smallest_dim, 2) if smallest_dim < float("inf") else 0.0
+
+        # ── Triangle and vertex counts from tessellation ──────────────────
+        # Tessellate all root shapes first (coarse, for counting only)
+        total_triangles = 0
+        total_vertices = 0
+        for i in range(1, n_free + 1) if _using_ocp else range(len(free_labels)):
+            label = free_labels.Value(i) if _using_ocp else free_labels[i]
+            shape = _get_shape(shape_tool, label)
+            if shape is not None and not shape.IsNull():
+                BRepMesh_IncrementalMesh(shape, 0.5, False, 0.5)
+
+                # Walk faces and sum Poly_Triangulation data
+                explorer = TopExp_Explorer(shape, TopAbs_FACE)
+                while explorer.More():
+                    face = _TopoDS.Face_s(explorer.Current()) if _using_ocp \
+                        else _TopoDS.Face(explorer.Current())
+                    try:
+                        loc = TopLoc_Location()
+                        if _using_ocp:
+                            tri = BRep_Tool.Triangulation_s(face, loc)
+                        else:
+                            tri = BRep_Tool.Triangulation(face, loc)
+                        if tri is not None:
+                            total_triangles += tri.NbTriangles()
+                            total_vertices += tri.NbNodes()
+                    except Exception:
+                        pass
+                    explorer.Next()
+
+        result["total_triangle_count"] = total_triangles
+        result["total_vertex_count"] = total_vertices
+
+        # ── Complexity score ──────────────────────────────────────────────
+        if total_triangles < 5000:
+            result["complexity_score"] = "low"
+        elif total_triangles <= 50000:
+            result["complexity_score"] = "medium"
+        else:
+            result["complexity_score"] = "high"
+
+        elapsed = time.monotonic() - t_start
+        logger.info(
+            "extract_rich_metadata: %d parts (%d unique), %.1f cm³, %d tris, "
+            "complexity=%s, %.2fs",
+            result["part_count"],
+            result["unique_part_count"],
+            result["total_volume_cm3"],
+            result["total_triangle_count"],
+            result["complexity_score"],
+            elapsed,
+        )
+        return result
+
+    except ImportError:
+        logger.warning("OCC not available for extract_rich_metadata")
+        return result
+    except Exception as exc:
+        logger.warning("extract_rich_metadata failed: %s", exc)
+        return result
+
+
 def _extract_step_objects(step_path: Path) -> list[str]:
     """Extract part names from STEP file using pythonocc."""
     try:

backend/app/tasks/step_tasks.py

@@ -8,6 +8,7 @@ Celery discovers tasks by import path, so these re-exports are required.
 from app.domains.pipeline.tasks.extract_metadata import (  # noqa: F401
     process_step_file,
     reextract_cad_metadata,
+    reextract_rich_metadata_task,
 )
 from app.domains.pipeline.tasks.render_thumbnail import (  # noqa: F401
     render_step_thumbnail,

frontend/src/api/products.ts

@@ -67,6 +67,7 @@ export interface Product {
   } | null
   arbeitspaket: string | null
   cad_render_log?: RenderLog | null
+  cad_metadata: Record<string, unknown> | null
   notes: string | null
   is_active: boolean
   source_excel: string | null

frontend/src/pages/Admin.tsx

@@ -179,6 +179,14 @@ export default function AdminPage() {
     onError: (e: any) => toast.error(e.response?.data?.detail || 'Failed'),
   })
 
+  const reextractRichMetadataMut = useMutation({
+    mutationFn: () => api.post('/admin/settings/reextract-rich-metadata'),
+    onSuccess: (res) => {
+      toast.success(res.data.message || 'Rich metadata re-extraction queued')
+    },
+    onError: (e: any) => toast.error(e.response?.data?.detail || 'Failed'),
+  })
+
   const cleanupOrphanedCadMut = useMutation({
     mutationFn: () => api.post('/admin/settings/cleanup-orphaned-cad-files'),
     onSuccess: (res) => {
@@ -1228,6 +1236,21 @@ export default function AdminPage() {
                 </button>
               </div>
             </div>
+
+            <div className="card p-5">
+              <h3 className="text-sm font-semibold text-content mb-1">Re-extract Rich Metadata</h3>
+              <p className="text-xs text-content-muted mb-3">Re-compute volume, surface area, complexity for all products with STEP files.</p>
+              <div className="space-y-2">
+                <button
+                  onClick={() => reextractRichMetadataMut.mutate()}
+                  disabled={reextractRichMetadataMut.isPending}
+                  className="btn-secondary text-sm w-full justify-start"
+                >
+                  <RefreshCw size={14} className={reextractRichMetadataMut.isPending ? 'animate-spin' : ''} />
+                  {reextractRichMetadataMut.isPending ? 'Queueing...' : 'Re-extract Rich Metadata'}
+                </button>
+              </div>
+            </div>
           </div>
         </div>
 

frontend/src/pages/ProductDetail.tsx

@@ -626,6 +626,54 @@ export default function ProductDetailPage() {
               </p>
             </div>
           )}
+          {product.cad_metadata && (() => {
+            const meta = product.cad_metadata as any
+            return (
+              <div className="col-span-2 mt-2 pt-3 border-t border-border-light">
+                <label className="block text-xs text-content-muted mb-2 flex items-center gap-1">
+                  <Box size={11} /> CAD Metadata
+                </label>
+                <div className="grid grid-cols-3 gap-2">
+                  {meta.part_count != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{meta.part_count}</p>
+                      <p className="text-xs text-content-muted">Parts</p>
+                    </div>
+                  )}
+                  {meta.total_volume_cm3 != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{Number(meta.total_volume_cm3).toFixed(1)}</p>
+                      <p className="text-xs text-content-muted">Volume (cm³)</p>
+                    </div>
+                  )}
+                  {meta.total_surface_area_cm2 != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{Number(meta.total_surface_area_cm2).toFixed(1)}</p>
+                      <p className="text-xs text-content-muted">Surface (cm²)</p>
+                    </div>
+                  )}
+                  {meta.complexity_score != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{Number(meta.complexity_score).toFixed(2)}</p>
+                      <p className="text-xs text-content-muted">Complexity</p>
+                    </div>
+                  )}
+                  {meta.total_triangle_count != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{Number(meta.total_triangle_count).toLocaleString()}</p>
+                      <p className="text-xs text-content-muted">Triangles</p>
+                    </div>
+                  )}
+                  {meta.assembly_depth != null && (
+                    <div className="text-center p-2 rounded-lg" style={{ backgroundColor: 'var(--color-bg-muted)' }}>
+                      <p className="text-lg font-semibold text-content">{meta.assembly_depth}</p>
+                      <p className="text-xs text-content-muted">Assembly Depth</p>
+                    </div>
+                  )}
+                </div>
+              </div>
+            )
+          })()}
         </div>
 
         {editMode && isPrivileged && (

plan.md

@@ -1,208 +1,122 @@
-# Plan: Tenant AI Chat Agent (Actionable)
+# Plan: Rich Product Metadata Extraction from STEP Files
 
 ## Context
 
-Each tenant has Azure OpenAI credentials stored in `tenant_config` JSONB. The goal is an **actionable AI agent** where users can type natural language commands to control the render pipeline — create orders, dispatch renders, check status, set overrides — scoped to their tenant.
+The AI chat agent was asked "What is the biggest product from my order?" and couldn't answer because dimensional data wasn't available in tool results. While `cad_files.mesh_attributes` already stores bounding box dimensions, much more metadata is extractable from STEP files via OCC that would make the AI agent and the product library significantly more useful.
 
-Example interactions:
+**Currently extracted**: part names, bounding box (xyz), sharp edges, smooth angle
-- "Render all Kugellager products as WebP at 1024x1024"
+**Available but not extracted**: per-part volume, surface area, assembly hierarchy, instance counts, embedded colors, triangle counts, geometric complexity
-- "What's the status of my last order?"
-- "Set material override to Steel-Bare on order SA-2026-00160"
-- "How many renders failed this week?"
 
-The agent uses **function calling** (Azure OpenAI tool use) — the LLM decides which API action to execute, the backend executes it, and returns the result. Tenants are fully isolated — each uses their own Azure API key and only sees their own data.
+**Goal**: Expand the STEP metadata extraction to compute richer product characteristics and store them in a structured `cad_metadata` JSONB field, accessible to the AI agent, product search, and frontend.
-
-**What exists:**
-- Per-tenant Azure OpenAI credentials in `tenant_config` JSONB
-- WebSocket system scoped by tenant for real-time events
-- `ai_validation` Celery queue (concurrency=8)
-- Azure OpenAI integration boilerplate in `azure_ai.py`
 
 ## Affected Files
 
 | File | Change |
 |------|--------|
-| `backend/app/models/chat.py` | **NEW** — ChatMessage model |
+| `backend/app/services/step_processor.py` | Expand `extract_step_metadata()` with volume, surface area, hierarchy, complexity |
-| `backend/app/models/__init__.py` | Import ChatMessage |
+| `backend/app/domains/products/models.py` | Add `cad_metadata` JSONB column to Product |
-| `backend/app/api/routers/chat.py` | **NEW** — Chat API endpoints |
+| `backend/alembic/versions/XXX_add_cad_metadata.py` | Migration |
-| `backend/app/services/chat_service.py` | **NEW** — Azure OpenAI chat + DB context |
+| `backend/app/domains/pipeline/tasks/extract_metadata.py` | Populate `cad_metadata` after STEP processing |
-| `backend/app/main.py` | Register chat router |
+| `backend/app/domains/products/schemas.py` | Expose `cad_metadata` in ProductOut |
-| `backend/alembic/versions/XXX_add_chat_messages.py` | Migration |
+| `backend/app/services/chat_service.py` | Include metadata in search_products and system prompt |
-| `frontend/src/api/chat.ts` | **NEW** — Chat API types + functions |
+| `frontend/src/pages/ProductDetail.tsx` | Display rich metadata (volume, part count, complexity) |
-| `frontend/src/components/chat/ChatPanel.tsx` | **NEW** — Chat UI component |
-| `frontend/src/components/layout/Layout.tsx` | Add chat toggle button |
 
 ## Tasks (in order)
 
-### [ ] Task 1: ChatMessage model + migration
+### [ ] Task 1: Expand STEP metadata extraction
 
-- **File**: `backend/app/models/chat.py` (new)
+- **File**: `backend/app/services/step_processor.py`
-- **What**: Create a ChatMessage model:
+- **What**: Expand `extract_step_metadata()` to compute additional properties after the existing bbox/edge extraction. Add a new function `extract_rich_metadata(doc, shape_tool)` that returns:
   ```python
-  class ChatMessage(Base):
+  {
-      __tablename__ = "chat_messages"
+      "part_count": 42,                    # Number of leaf parts
-      id: UUID PK
+      "assembly_depth": 3,                 # Max nesting depth
-      tenant_id: UUID FK → tenants.id (nullable, indexed)
+      "total_volume_cm3": 1250.4,          # Sum of all part volumes (cm³)
-      user_id: UUID FK → users.id (nullable)
+      "total_surface_area_cm2": 3400.2,    # Sum of all surface areas (cm²)
-      session_id: UUID (groups messages in a conversation, indexed)
+      "total_triangle_count": 45000,       # After tessellation
-      role: String(20) — "user", "assistant", "system"
+      "total_vertex_count": 23000,         # After tessellation
-      content: Text
+      "largest_part": {                    # Part with largest volume
-      context_type: String(50) nullable — "order", "product", "general"
+          "name": "OuterRing",
-      context_id: UUID nullable — order_id or product_id
+          "volume_cm3": 450.2,
-      token_count: Integer nullable — track usage
+      },
-      created_at: DateTime
+      "smallest_dimension_mm": 0.5,        # Smallest bbox dimension across all parts
+      "instance_count": 36,                # Total instances (parts may repeat)
+      "unique_part_count": 12,             # Distinct shapes
+      "complexity_score": "high",          # low/medium/high based on triangle count
+  }
   ```
-- **Also**: Import in `backend/app/models/__init__.py`
+  Use OCC:
-- **Migration**: `alembic revision --autogenerate -m "add chat_messages table"`
+  - `GProp_GProps` + `BRepGProp.VolumeProperties()` for volume
-- **Acceptance gate**: Table exists in DB; model importable
+  - `BRepGProp.SurfaceProperties()` for surface area
+  - `Poly_Triangulation` for triangle/vertex counts (after tessellation)
+  - Assembly tree walk (already done in `_collect_part_key_map`) for hierarchy depth + instance count
+- **Acceptance gate**: `extract_rich_metadata()` returns all fields for a test STEP file
 - **Dependencies**: None
 
-### [ ] Task 2: Chat service — Azure OpenAI with function calling
+### [ ] Task 2: Add cad_metadata column to Product model
 
-- **File**: `backend/app/services/chat_service.py` (new)
+- **File**: `backend/app/domains/products/models.py`
-- **What**: Service with Azure OpenAI **tool use / function calling**:
+- **What**: Add `cad_metadata: Mapped[dict | None] = mapped_column(JSONB, nullable=True, default=None)` to the Product model. This stores the rich metadata at the product level (not cad_file) because products are the user-facing entity.
-  1. Takes a user message + session_id + tenant_id + user_id
+- **Migration**: `alembic revision --autogenerate -m "add cad_metadata to products"`
-  2. Loads tenant Azure credentials from `tenant_config`
+- **Also**: Add to ProductOut schema in `backend/app/domains/products/schemas.py`
-  3. Defines **tools** the LLM can call (JSON schema for each):
+- **Acceptance gate**: Column exists, schema includes it
-     - `list_orders(status, limit)` — list tenant's orders
+- **Dependencies**: None
-     - `search_products(query, category, limit)` — search products
-     - `create_order(product_ids, output_type_name, render_overrides, material_override)` — create & submit
-     - `dispatch_renders(order_id)` — dispatch renders for an order
-     - `get_order_status(order_id)` — check render progress
-     - `set_material_override(order_id, material_name)` — batch material override
-     - `set_render_overrides(order_id, overrides)` — batch render overrides
-     - `get_render_stats()` — throughput stats
-     - `check_materials(order_id)` — unmapped materials check
-     - `query_database(sql)` — read-only SQL (SELECT only, tenant-scoped)
-  4. Calls Azure OpenAI with `tools` parameter — the LLM decides which tool to call
-  5. Executes the tool call internally (same functions as MCP server but tenant-scoped)
-  6. Returns tool result to LLM for a natural language response
-  7. Stores conversation in ChatMessage table
 
-  **Tenant isolation**: All DB queries filter by `tenant_id`. The `query_database` tool auto-appends `WHERE tenant_id = '{tenant_id}'` or validates tenant scope.
+### [ ] Task 3: Populate cad_metadata during STEP processing
 
-  **Tool execution**: Uses the existing backend API functions directly (not HTTP calls) — import from the routers/services.
+- **File**: `backend/app/domains/pipeline/tasks/extract_metadata.py`
+- **What**: After `process_step_file` extracts objects and queues thumbnail, call `extract_rich_metadata()` and store the result on the Product's `cad_metadata` field. Also store it on `cad_files.mesh_attributes` (merge with existing data).
+- **Also**: Add a "reextract metadata" admin action that re-runs this for all existing products
+- **Acceptance gate**: After STEP processing, product.cad_metadata is populated with volume, part_count, etc.
+- **Dependencies**: Tasks 1, 2
 
-  ```python
+### [ ] Task 4: Expose metadata in AI agent tools
-  tools = [
-      {
-          "type": "function",
-          "function": {
-              "name": "search_products",
-              "description": "Search products by name, PIM-ID, or category",
-              "parameters": {
-                  "type": "object",
-                  "properties": {
-                      "query": {"type": "string"},
-                      "category": {"type": "string"},
-                  }
-              }
-          }
-      },
-      # ... more tools
-  ]
-  response = client.chat.completions.create(
-      model=deployment,
-      messages=messages,
-      tools=tools,
-      tool_choice="auto",
-  )
-  # Handle tool_calls in response, execute, return result
-  ```
-- **Acceptance gate**: User can say "show my last 5 orders" and get real data back via function calling
-- **Dependencies**: Task 1
 
-### [ ] Task 3: Chat API endpoints
+- **File**: `backend/app/services/chat_service.py`
-
+- **What**:
-- **File**: `backend/app/api/routers/chat.py` (new)
+  1. Update `_tool_search_products()` to include `cad_metadata` fields (part_count, total_volume_cm3, complexity_score) in results
-- **What**: FastAPI router with endpoints:
+  2. Update `query_database` tool description to mention `products.cad_metadata` JSONB field
-  - `POST /api/chat/messages` — send a message, get AI response
+  3. Update system prompt to mention available metadata
-    - Body: `{ message: str, session_id: str | None, context_type: str | None, context_id: str | None }`
+- **Acceptance gate**: AI agent can answer "What is the biggest product?" using volume data
-    - Creates session_id if not provided
-    - Returns: `{ session_id: str, message: ChatMessageOut, response: ChatMessageOut }`
-    - Auth: `get_current_user` — uses user's tenant AI config
-  - `GET /api/chat/sessions` — list user's chat sessions
-    - Returns: `[{ session_id, last_message, message_count, created_at }]`
-  - `GET /api/chat/sessions/{session_id}/messages` — get conversation history
-    - Returns: `[{ id, role, content, created_at }]`
-  - `DELETE /api/chat/sessions/{session_id}` — delete a conversation
-- **Also**: Register router in `backend/app/main.py`
-- **Acceptance gate**: POST /api/chat/messages returns an AI response using tenant credentials
-- **Dependencies**: Task 2
-
-### [ ] Task 4: Frontend — Chat API types
-
-- **File**: `frontend/src/api/chat.ts` (new)
-- **What**: TypeScript interfaces and API functions:
-  ```typescript
-  interface ChatMessage { id: string; role: 'user' | 'assistant' | 'system'; content: string; created_at: string }
-  interface ChatSession { session_id: string; last_message: string; message_count: number; created_at: string }
-  interface ChatResponse { session_id: string; message: ChatMessage; response: ChatMessage }
-
-  function sendMessage(message: string, sessionId?: string, contextType?: string, contextId?: string): Promise<ChatResponse>
-  function getSessions(): Promise<ChatSession[]>
-  function getSessionMessages(sessionId: string): Promise<ChatMessage[]>
-  function deleteSession(sessionId: string): Promise<void>
-  ```
-- **Acceptance gate**: Types compile; functions callable
 - **Dependencies**: Task 3
 
-### [ ] Task 5: Frontend — ChatPanel component
+### [ ] Task 5: Display rich metadata on ProductDetail page
 
-- **File**: `frontend/src/components/chat/ChatPanel.tsx` (new)
+- **File**: `frontend/src/pages/ProductDetail.tsx`
-- **What**: Slide-out chat panel (right side, similar to notification panels in modern apps):
+- **What**: Add a "CAD Metadata" section on the product detail page showing:
-  1. **Header**: "AI Assistant" title + close button + session selector
+  - Part count + unique parts + instances
-  2. **Message list**: Scrollable area with role-based styling:
+  - Total volume (cm³) + surface area (cm²)
-     - User messages: right-aligned, accent background
+  - Largest part name + volume
-     - Assistant messages: left-aligned, surface background, markdown support
+  - Complexity score badge (low/medium/high)
-     - Timestamps below each message
+  - Triangle/vertex count
-  3. **Input area**: Text input + send button (Enter to send)
+  - Assembly depth
-  4. **Loading state**: Typing indicator while waiting for AI response
+- **Acceptance gate**: Metadata displayed on product page; empty gracefully when not available
-  5. **Session management**: "New conversation" button, session history dropdown
+- **Dependencies**: Task 2
-  6. **Context awareness**: When opened from an order/product page, auto-includes context
 
-  **Styling**:
+### [ ] Task 6: Batch re-extract metadata for existing products
-  - Fixed right panel (w-96, full height)
-  - Backdrop overlay on mobile
-  - Smooth slide-in animation
-  - Use existing CSS variables (surface, content, accent)
-  - lucide-react icons (MessageSquare, Send, Loader2, X, Plus)
-- **Acceptance gate**: Panel opens/closes, messages send and display, AI responds
-- **Dependencies**: Task 4
 
-### [ ] Task 6: Frontend — Chat toggle in Layout
+- **File**: `backend/app/api/routers/admin.py`
-
+- **What**: Add a "Re-extract Rich Metadata" button in System Tools that queues a Celery task to re-process all completed STEP files and populate `cad_metadata` for all products.
-- **File**: `frontend/src/components/layout/Layout.tsx`
+- **Acceptance gate**: Button triggers batch job; existing products get metadata populated
-- **What**: Add a chat toggle button:
+- **Dependencies**: Tasks 1, 3
-  1. Floating button in bottom-right corner (or in the sidebar)
-  2. Icon: `MessageSquare` from lucide-react
-  3. Badge with unread count (optional, for future)
-  4. Click toggles ChatPanel visibility
-  5. Only show when tenant has `ai_enabled = true`
-- **Acceptance gate**: Button visible for users with AI-enabled tenant; clicking opens/closes ChatPanel
-- **Dependencies**: Task 5
 
 ## Migration Check
 
-**Yes** — one new table `chat_messages` with UUID PK, FK to tenants and users.
+**Yes** — one new JSONB column on `products` table.
 
 ## Order Recommendation
 
-1. Backend model + migration (Task 1)
+1. Task 1 (extraction logic) + Task 2 (model + migration) — parallel
-2. Backend service (Task 2)
+2. Task 3 (wire up in pipeline)
-3. Backend API (Task 3)
+3. Task 4 (AI agent) + Task 5 (frontend) — parallel
-4. Frontend types (Task 4)
+4. Task 6 (batch re-extract)
-5. Frontend chat UI (Task 5)
-6. Frontend layout integration (Task 6)
 
 ## Risks / Open Questions
 
-1. **Azure OpenAI availability**: If tenant hasn't configured AI credentials, the chat should show a helpful message ("AI not configured — ask your admin to set up Azure OpenAI in Tenant Settings")
+1. **Volume calculation accuracy**: OCC `BRepGProp` computes exact B-rep volume, not mesh-based. This is accurate but can be slow for very complex shapes. Cap at 5s per file.
 
-2. **Token costs**: Each message uses Azure OpenAI tokens. Consider adding token counting and a configurable monthly limit per tenant.
+2. **Performance**: Rich metadata extraction adds ~100-500ms per STEP file. This is acceptable since STEP processing already takes 1-5s.
 
-3. **Context enrichment**: The system prompt could include live data (order counts, render status). This makes the AI more helpful but costs more tokens. Start simple, enhance later.
+3. **Existing products**: ~45 products with STEP files need backfill. Task 6 handles this.
 
-4. **Streaming responses**: Azure OpenAI supports streaming. V1 uses a simple request/response. V2 could stream via WebSocket for real-time typing effect.
+4. **Triangle count varies**: Depends on tessellation settings (deflection angles). Store the count at the current tessellation quality for reference, with a note that it's approximate.
-
-5. **openai package**: The `openai` Python package must be installed in the backend container. Check if it's already a dependency (it may be via `azure_ai.py`).