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Challenge

Build a Multi-Site Battery Storage Portfolio Manager with LlamaIndex

Managing performance across three BESS deals worth $867m requires unified multi-site telemetry aggregation. Build an agentic RAG solution using LlamaIndex workflows to track battery degradation, query maintenance manuals, and optimize warranty coverage.

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Challenge brief

What you are building

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

Utilize LlamaIndex Workflows and agentic tools to monitor multi-site BESS telemetry and match fault logs against warranty terms.

Delivery guide

How work is evaluated

Evaluation

Evaluates fault code diagnosis accuracy and warranty claim generation correctness.

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

warranty_coverage_check

Verify warranty status is correctly identified as covered

Binary check

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

Dimension 2

diagnosis_accuracy

Accuracy of fault code classification against manuals • target: 0.98 • range: 0.9-1

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

  • Construct LlamaIndex Event-driven Workflows for multi-site telemetry ingestion

  • Index technical warranty manuals and cell degradation lookup tables

  • Automate fault diagnosis when battery cell voltage variance exceeds tolerance limits

  • Generate automated warranty claim submissions for regional BESS suppliers

Resources and assets

Reference links and supporting material

Dataset notes

SCADA fault logs, battery cell SOH degradation curves, and manufacturer warranty contracts across 3 Saudi BESS sites.

Challenge setup

Evaluates fault code diagnosis accuracy and warranty claim generation correctness.

Challenge input

JSON BMS telemetry log

LlamaIndex

Offers event-driven Workflow architecture for multi-document RAG

Evaluated output

JSON warranty assessment

Evaluation checks
  • Verify warranty status is correctly identified as covered
  • Accuracy of fault code classification against manuals • target: 0.98 • range: 0.9-1
Configured evaluation evidence
  • Benchmark: BMSFaultBench
  • Diagnosis Accuracy target: 0.98
  • 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
  • Ray Tune · Optional
  • Hume AI · Optional

Evaluation contract

  • warranty_coverage_check · Weight 1
  • diagnosis_accuracy · Weight 1

Metrics

  • diagnosis_accuracy · Target 0.98

Benchmarks

  • BMSFaultBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON BMS telemetry log

    Output: JSON warranty assessment

    Execution harness: Python

Evaluation tools

  • LlamaIndex · RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

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