Office-to-Residential Structural Conversion Assessment Pipeline with AI SDK & Haystack
Commercial conversion projects encounter severe budget overruns when structural floorplate limitations are identified late in planning. Build a web application with Vercel AI SDK and Haystack to extract core depths and window distances from architectural floor plans. Achieve 95% metric extraction precision on structural blueprints.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AI SDK dynamic TypeScript tool with Haystack document pipeline integration to grade office conversion suitability from architectural PDF data.
How work is evaluated
Evaluates accuracy of extracted floorplate dimensions and conversion feasibility grading.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
feasibility_boolean_check
Checks if feasibility flag accurately matches expected grade based on core depth threshold
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
extraction_precision
Precision of structural numeric parameters extracted • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Use Vercel AI SDK (`ai` and `@ai-sdk/openai`) to generate structured output for architectural metric extraction
Leverage Haystack pipelines for document indexing, chunking, and semantic searching across building engineering specs
Implement client-side interactive UI with React and streaming output for feasibility scoring
Validate extracted core-to-window depth against municipal residential daylighting requirements
Reference links and supporting material
Dataset of technical building specification text excerpts describing office floorplates, core shafts, and column spacings.
Challenge setup
Evaluates accuracy of extracted floorplate dimensions and conversion feasibility grading.
Challenge input
JSON containing spec string, depth dimensions, and window counts
AI SDK
TypeScript AI SDK for building streaming AI interfaces.
Haystack
Document pipeline framework for searching architectural engineering specs.
Evaluated output
JSON containing core_to_window_distance_ft, conversion_feasibility, yield_grade
- Checks if feasibility flag accurately matches expected grade based on core depth threshold
- Precision of structural numeric parameters extracted • target: 0.95 • range: 0-1
- Benchmark: Architectural Document AI Benchmark
- Extraction Precision target: 0.95
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Haystack · Required
- Haystack · Optional
- Dagster · Optional
Evaluation contract
- feasibility_boolean_check · Weight 1
- extraction_precision · Weight 1
Metrics
- extraction_precision · Target 0.95
Benchmarks
- Architectural Document AI Benchmark
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing spec string, depth dimensions, and window counts
Output: JSON containing core_to_window_distance_ft, conversion_feasibility, yield_grade
Execution harness: JavaScript
Evaluation tools
- AI SDK · agent_framework
- Haystack · rag_framework
Recipe freeze marker
No recipe freeze time is recorded.