LangChain & Dagster Structural Office Conversion Analyzer
Commercial office adaptive reuse projects face severe structural and plumbing constraints during conversion. Build an automated Dagster orchestrator utilizing LangChain pipelines to extract floorplate geometry, run structural compliance checks, and yield structured feasibility scores with 90% audit pass rates.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement an automated pipeline using LangChain and Dagster to analyze office floorplates for residential conversion feasibility.
How work is evaluated
Evaluates the accuracy of floorplate structural scoring and Dagster pipeline execution output.
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.
schema_and_range_check
Validates schema adherence and accurate scoring calculation.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
feasibility_score_accuracy
Mean absolute deviation from ground-truth conversion feasibility ratings. • 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
Extract structural dimensions and core-to-window depth from architectural CAD/PDF files
Orchestrate multi-stage compliance pipelines in Dagster with strict data assets
Implement LangChain agents with custom CAD extraction tools
Validate structural feasibility against municipal residential zoning standards
Reference links and supporting material
Synthetic architectural floorplate measurements and CAD geometry descriptors for 50 commercial office towers.
Challenge setup
Evaluates the accuracy of floorplate structural scoring and Dagster pipeline execution output.
Challenge input
JSON containing CAD floorplate specs and core depth metrics
LangChain
Provides tool abstractions and agent logic for parsing CAD text reports.
Dagster
Manages software-defined assets and execution graph for multi-property evaluation.
Evaluated output
JSON with feasibility score, structural pass/fail flag, and violation array
- Validates schema adherence and accurate scoring calculation.
- Mean absolute deviation from ground-truth conversion feasibility ratings. • target: 0.95 • range: 0-1
- Benchmark: RealEstateFeasibilityBench
- Feasibility Score Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python 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.
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Configured tools
- Dagster · Required
- LangChain · Optional
- LiveKit · Optional
- LangChain · Optional
Evaluation contract
- schema_and_range_check · Weight 1
- feasibility_score_accuracy · Weight 1
Metrics
- feasibility_score_accuracy · Target 0.95
Benchmarks
- RealEstateFeasibilityBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing CAD floorplate specs and core depth metrics
Output: JSON with feasibility score, structural pass/fail flag, and violation array
Execution harness: Python
Evaluation tools
- LangChain · Agent Framework
- Dagster · Data Orchestration
Recipe freeze marker
No recipe freeze time is recorded.