Learning from Unreal Engine Blueprints
I’ve always admired Unreal Engine’s Blueprints - particularly its subtle yet powerful connection suggestion feature. When you drag a wire out from a pin, the system intuitively suggests compatible nodes based on the pin’s data type.
This kind of context-awareness accelerates creation and keeps workflow fluid by making things just work - no guesswork needed.
The Challenge: Missing Type Labels in C# Instance Methods
Divooka, built atop C#, posed a snag: instance methods don’t include the object’s type in their signature. So if a method like Rotate() belongs to a Transform object, the type isn’t explicitly declared in the method signature - making automatic suggestion tricky.
To match Unreal’s intuitiveness, I needed a way to tell Divooka - not just "Rotate()", but Rotate() on Transform.
Explicit Type Filter in Search Syntax
The first step is to allow users to filter the toolbox explicitly in their search query.
For example: [Pixel Image] Save would filter for methods in the Pixel Image toolbox (category). The symmetry of this syntax remains intuitive even in localized languages like Chinese - because only the type would be translated, not the syntax design itself.
Building the Pipeline: Filtering and Highlighting
Next, I leveled up the search experience by crafting a multi-stage pipeline:
- Category names (like
[AI Service], [Pixel Image]) now at the front, so users can scope their search by purpose.
- Then we optionally parse
<TypeName> syntax, which is used to filter input parameters.
- Finally, full-text search for other keywords.
So a query might look like:
[Pixel Image] <String> Save
In which case we will search only for functions containing the word "Save" and expects a String parameter. Visually, I emphasized that first category tag with syntax highlighting to help the eye quickly scan.
The Result: It Just Works (Mostly)
Here’s how it currently looks:
- Type
[Pixel Image] Save.
- The search autocomplete suggests all instance methods named
Save...() defined in Pixel Image toolbox (mostly operating on the PixelImage object type) (e.g., PixelImage.Save, PixelImage.Load, etc.).
- Everything lights up in palette instantly - users can find what they need fast.
What’s Missing: Auto-Connecting Suggestions
This solves the searching half of the problem beautifully - but it doesn’t yet auto-connect. In Unreal, dragging from a Vector pin might instantly suggest Normalize() - and connect it for you if you click that suggestion.
Right now, Divooka still requires a manual drag-and-click - our intention was only searching and filtering, not full drag-and-drop automation.
Next Steps: Toward Full Contextual Suggestions
I’m plotting the future:
- Pin-aware suggestion: When dragging a wire from a pin, immediately filter down to matching methods—and optionally even pre-wire the node when hitting “Enter” or double-clicking.
- Type inference: Avoid requiring
<Type> if the context is obvious - since the system already knows the pin’s type.
- Better localization: Make sure category tags and type filters localize cleanly, without mangling grammar in languages like Chinese, Japanese, etc.
- Enhanced UX hints: Perhaps grey-out non-matching nodes live, or provide quick tooltips (e.g. “only shows methods on Transform”, like Unreal Blueprint's node search interface).
Summary
Previously, instance-based methods don’t expose their object type; this hampers auto-suggestion. We solved this by introducing explicit <Type> filters and category tags in our search syntax. The result is now the users can quickly locate relevant methods with filtered search - instant clarity. Up next, we want to add full context-aware, auto-connecting suggestion - move from “search” to “smart connection.”
Conclusion
Unreal Blueprints’ delightful node suggestions inspired us to refine Divooka’s search. By introducing syntax like [Category] <Type> Keywords..., Divooka now lets you filter instance-based methods explicitly - greatly improving discoverability. Coming up: auto-connection and smarter, context-aware wiring.
Let me know if you are interesting in learning more specific implementation details - like the regex matching, indexing logic, or localization strategy!
Happy visual coding!
