Merge pull request 'docs(solver): update drag protocol docs to reflect implemented caching' (#330) from docs/solver-drag-cache into main

Reviewed-on: #330

docs/src/solver/assembly-integration.md

@@ -98,53 +98,107 @@ if hasattr(FreeCADGui, "ActiveDocument"):
 
 ## Interactive drag protocol
 
-The drag protocol provides real-time constraint solving during viewport part dragging. It is a three-phase protocol:
+The drag protocol provides real-time constraint solving during viewport part dragging. It is a three-phase protocol with a caching layer that avoids rebuilding the constraint system on every mouse move.
 
 ### pre_drag(ctx, drag_parts)
 
-Called when the user begins dragging. Stores the context and dragged part IDs, then runs a full solve to establish the starting state.
+Called when the user begins dragging. Builds the constraint system once, runs the substitution pre-pass, constructs the symbolic Jacobian, compiles the evaluator, performs an initial solve, and caches everything in a `_DragCache` for reuse across subsequent `drag_step()` calls.
 
 ```python
 def pre_drag(self, ctx, drag_parts):
     self._drag_ctx = ctx
     self._drag_parts = set(drag_parts)
-    return self.solve(ctx)
+
+    system = _build_system(ctx)
+
+    half_spaces = compute_half_spaces(...)
+    weight_vec = build_weight_vector(system.params)
+
+    residuals = substitution_pass(system.all_residuals, system.params)
+    # single_equation_pass is intentionally skipped — it bakes variable
+    # values as constants that become stale when dragged parts move.
+
+    jac_exprs = [[r.diff(name).simplify() for name in free] for r in residuals]
+    compiled_eval = try_compile_system(residuals, jac_exprs, ...)
+
+    # Initial solve (Newton-Raphson + BFGS fallback)
+    newton_solve(residuals, system.params, ...)
+
+    # Cache for drag_step() reuse
+    cache = _DragCache()
+    cache.system = system
+    cache.residuals = residuals
+    cache.jac_exprs = jac_exprs
+    cache.compiled_eval = compiled_eval
+    cache.half_spaces = half_spaces
+    cache.weight_vec = weight_vec
+    ...
+    return result
 ```
 
+**Important:** `single_equation_pass` is not used in the drag path. It analytically solves single-variable equations and bakes the results as `Const()` nodes into downstream expressions. During drag, those baked values become stale when part positions change, causing constraints to silently stop being enforced. Only `substitution_pass` (which replaces genuinely grounded parameters) is safe to cache.
+
 ### drag_step(drag_placements)
 
-Called on each mouse move. Updates the dragged parts' placements in the stored context, then re-solves. Since the parts moved only slightly from the previous position, Newton-Raphson converges in 1-2 iterations.
+Called on each mouse move. Updates only the dragged part's 7 parameter values in the cached `ParamTable`, then re-solves using the cached residuals, Jacobian, and compiled evaluator. No system rebuild occurs.
 
 ```python
 def drag_step(self, drag_placements):
-    ctx = self._drag_ctx
+    cache = self._drag_cache
+    params = cache.system.params
+
+    # Update only the dragged part's parameters
     for pr in drag_placements:
-        for part in ctx.parts:
+        pfx = pr.id + "/"
-            if part.id == pr.id:
+        params.set_value(pfx + "tx", pr.placement.position[0])
-                part.placement = pr.placement
+        params.set_value(pfx + "ty", pr.placement.position[1])
-                break
+        params.set_value(pfx + "tz", pr.placement.position[2])
-    return self.solve(ctx)
+        params.set_value(pfx + "qw", pr.placement.quaternion[0])
+        params.set_value(pfx + "qx", pr.placement.quaternion[1])
+        params.set_value(pfx + "qy", pr.placement.quaternion[2])
+        params.set_value(pfx + "qz", pr.placement.quaternion[3])
+
+    # Solve with cached artifacts — no rebuild
+    newton_solve(cache.residuals, params, ...,
+                 jac_exprs=cache.jac_exprs,
+                 compiled_eval=cache.compiled_eval)
+
+    return result
 ```
 
 ### post_drag()
 
-Called when the drag ends. Clears the stored state.
+Called when the drag ends. Clears the cached state.
 
 ```python
 def post_drag(self):
     self._drag_ctx = None
     self._drag_parts = None
+    self._drag_cache = None
 ```
 
-### Performance notes
+### _DragCache
 
-The current implementation re-solves from scratch on each drag step, using the updated placements as the initial guess. This is correct and simple. For assemblies with fewer than ~50 parts, interactive frame rates are maintained because:
+The cache holds all artifacts built in `pre_drag()` that are invariant across drag steps (constraint topology doesn't change during a drag):
+
+| Field | Contents |
+|-------|----------|
+| `system` | `_System` -- owns `ParamTable` and `Expr` trees |
+| `residuals` | `list[Expr]` -- after substitution pass |
+| `jac_exprs` | `list[list[Expr]]` -- symbolic Jacobian |
+| `compiled_eval` | `Callable` or `None` -- native compiled evaluator |
+| `half_spaces` | `list[HalfSpace]` -- branch trackers |
+| `weight_vec` | `ndarray` or `None` -- minimum-movement weights |
+| `post_step_fn` | `Callable` or `None` -- half-space correction callback |
+
+### Performance
+
+The caching layer eliminates the expensive per-frame overhead (~150 ms for system build + Jacobian construction + compilation). Each `drag_step()` only evaluates the cached expressions at updated parameter values:
 
 - Newton-Raphson converges in 1-2 iterations from a nearby initial guess
-- Pre-passes eliminate fixed parameters before the iterative loop
+- The compiled evaluator (`codegen.py`) uses native Python `exec` for flat evaluation, avoiding the recursive tree-walk overhead
-- The symbolic Jacobian is recomputed each step (no caching yet)
+- The substitution pass compiles grounded-body parameters to constants, reducing the effective system size
-
+- DOF counting is skipped during drag for speed (`result.dof = -1`)
-For larger assemblies, cached incremental solving (reusing the decomposition and Jacobian structure across drag steps) is planned as a future optimization.
 
 ## Diagnostics integration