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Challenge

Build a Phosphate Rail Route Optimization Agent with LlamaIndex

Doubling Saudi Arabia's $1bn phosphate rail track requires continuous alignment and terrain route intelligence. Build an advanced RAG agent using LlamaIndex with property graph indexing to query geotechnical surveys and route alignment specs efficiently.

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Challenge brief

What you are building

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

Utilize LlamaIndex Property Graphs and structured data connectors to query Saudi Railway Company (SAR) expansion plans and terrain constraints.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of track route query results and safety load compliance validation.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

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

axle_load_check

Verify extracted load matches target specification

Binary check

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

Dimension 2

graph_retrieval_faithfulness

Faithfulness of answer derived from Knowledge Graph nodes • target: 0.97 • range: 0.88-1

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

  • Implement LlamaIndex PropertyGraphIndex for connected railway domain entities

  • Parse geospatial terrain reports and track-doubling engineering constraints

  • Build dynamic sub-question query engines to analyze cross-country rail corridor risks

  • Generate automated compliance reports for railway safety and axle-load specifications

Resources and assets

Reference links and supporting material

Dataset notes

Geotechnical reports, heavy-haul railway track standards, and corridor alignment specs for Saudi SAR network.

Challenge setup

Evaluates accuracy of track route query results and safety load compliance validation.

Challenge input

JSON query string

LlamaIndex

Enterprise standard for complex graph indexing and structured document parsing

Evaluated output

JSON spec response

Evaluation checks
  • Verify extracted load matches target specification
  • Faithfulness of answer derived from Knowledge Graph nodes • target: 0.97 • range: 0.88-1
Configured evaluation evidence
  • Benchmark: GraphRAGBench
  • Graph Retrieval Faithfulness target: 0.97
  • 1 public reference case
Configured runtime
  • 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.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • RAI · Optional
Policy Serving
  • Vercel AI Gateway · Optional
Orchestration
  • Vercel AI Gateway · Optional

Evaluation contract

  • axle_load_check · Weight 1
  • graph_retrieval_faithfulness · Weight 1

Metrics

  • graph_retrieval_faithfulness · Target 0.97

Benchmarks

  • GraphRAGBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON query string

    Output: JSON spec response

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Data & RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

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