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Challenge

LlamaIndex Aeration Telemetry Diagnostic Agent with Gemini 2.5 Flash

Wastewater aeration retrofits often generate unstructured alarm logs and OPC UA telemetry spikes that overwhelm operators. Build an intelligent telemetry diagnostic agent using LlamaIndex and Gemini 2.5 Flash to automatically parse telemetry streams and detect physical failure states. Success is measured by achieving over 90% diagnostic accuracy on benchmarked aeration fault logs.

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Challenge brief

What you are building

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

Implement a LlamaIndex RAG agent integrated with Gemini 2.5 Flash to parse real-time OPC UA aeration telemetry and identify physical failure states.

Delivery guide

How work is evaluated

Evaluation

Evaluates diagnostic accuracy and root-cause identification precision for aeration telemetry anomaly logs.

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

valid_json_structure

Ensures output includes valid fault_code, root_cause, and confidence_score.

Binary check

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

Dimension 2

diagnostic_accuracy

Percentage of correctly identified fault codes. • 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

  • Ingest and index OPC UA time-series telemetry and system logs using LlamaIndex.

  • Integrate Gemini 2.5 Flash for high-speed anomaly diagnosis and root cause analysis.

  • Build a structured query agent capable of correlating blower pressure drops with dissolved oxygen anomalies.

  • Evaluate diagnostic accuracy against known fault ground-truth data.

Resources and assets

Reference links and supporting material

Dataset notes

Time-series OPC UA telemetry stream from a municipal wastewater aeration retrofit, featuring pressure sensor, dissolved oxygen, and alarm event logs.

How this agent runs

Evaluates diagnostic accuracy and root-cause identification precision for aeration telemetry anomaly logs.

Challenge input

JSON object with telemetry_window array and alarm_events

LlamaIndex

Framework required for RAG and structured indexing over time-series logs.

Gemini 2.5 Flash

High speed and large context window suited for raw sensor log analysis.

Evaluated output

JSON object with fault_code, root_cause, and confidence_score

Checks for
  • Ensures output includes valid fault_code, root_cause, and confidence_score.
  • Percentage of correctly identified fault codes. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: IndustrialTelemetryEval-2026
  • Diagnostic Accuracy target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • LlamaIndex · Optional
Policy Serving
  • Gemini 2.5 Flash · Optional

Evaluation contract

  • valid_json_structure · Weight 1
  • diagnostic_accuracy · Weight 1

Recipe state

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

Frequently Asked Questions about LlamaIndex Aeration Telemetry Diagnostic Agent with Gemini 2.5 Flash