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Challenge

Office Conversion Feasibility RAG Pipeline with LlamaIndex & Fiddler AI

Structural realities hinder office-to-residential conversions due to deep floorplates and core layout constraints. Build a LlamaIndex RAG agent integrated with Fiddler AI monitoring to parse architectural specs, calculate floorplate feasibility, and extract municipal constraints with 90% precision.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an architectural document parsing and structural assessment workflow using LlamaIndex sentence-window retrieval and Fiddler AI performance monitoring.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of extracted structural parameters and feasibility classification against ground truth engineering specs.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

exact_parameter_match

Extracted dimensions match ground truth within 5% tolerance

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

extraction_accuracy

Percentage of correctly extracted structural fields across test suite • target: 0.9 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Extract structural parameters and MEP constraints from PDF architectural blueprints.

  • Implement a LlamaIndex document index using custom node parsers and window retrieval.

  • Integrate Fiddler AI to monitor model drift and extraction accuracy over time.

  • Calculate floorplate depth feasibility ratios against residential daylighting requirements.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 50 office building architectural specifications, structural reports, and MEP municipal filings.

How this agent runs

Evaluates accuracy of extracted structural parameters and feasibility classification against ground truth engineering specs.

Challenge input

JSON containing document_id and blueprint_text

LlamaIndex

Framework assignment for indexing and structured query processing.

Fiddler AI

Required tool for tracking model accuracy and drift in real estate analytics.

Evaluated output

JSON with core_to_window_dist_ft, mechanical_shaft_capacity, conversion_viable boolean

Checks for
  • Extracted dimensions match ground truth within 5% tolerance
  • Percentage of correctly extracted structural fields across test suite • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: RAG-Eval-RE
  • Extraction Accuracy target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Fiddler AI · Required
  • Llama Index · Optional
  • Synthflow · Optional

Evaluation contract

  • exact_parameter_match · Weight 1
  • extraction_accuracy · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Office Conversion Feasibility RAG Pipeline with LlamaIndex & Fiddler AI