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.
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.
How work is evaluated
Evaluates diagnostic accuracy and root-cause identification precision for aeration telemetry anomaly logs.
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.
valid_json_structure
Ensures output includes valid fault_code, root_cause, and confidence_score.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
diagnostic_accuracy
Percentage of correctly identified fault codes. • target: 0.9 • 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
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.
Reference links and supporting material
Time-series OPC UA telemetry stream from a municipal wastewater aeration retrofit, featuring pressure sensor, dissolved oxygen, and alarm event logs.
Challenge setup
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
- Ensures output includes valid fault_code, root_cause, and confidence_score.
- Percentage of correctly identified fault codes. • target: 0.9 • range: 0-1
- Benchmark: IndustrialTelemetryEval-2026
- Diagnostic Accuracy target: 0.9
- 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.
View technical recipe
Configured tools
- Llama Index · Required
- LlamaIndex · Optional
- Gemini 2.5 Flash · Optional
Evaluation contract
- valid_json_structure · Weight 1
- diagnostic_accuracy · Weight 1
Metrics
- diagnostic_accuracy · Target 0.9
Benchmarks
- IndustrialTelemetryEval-2026
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with telemetry_window array and alarm_events
Output: JSON object with fault_code, root_cause, and confidence_score
Execution harness: Python
Evaluation tools
- LlamaIndex · Agent Framework
- Gemini 2.5 Flash · LLM Inference
Recipe freeze marker
No recipe freeze time is recorded.