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Challenge

Oman Power Plant Telemetry Intelligence Engine with LlamaIndex & Deepgram

Managing a $683m CCGT power plant contract in Oman requires querying complex turbine manuals while processing voice updates from site engineers. Build a voice-enabled RAG pipeline using LlamaIndex for indexing plant engineering docs and Deepgram for real-time acoustic transcription of engineer status reports. Achieve an exact operational retrieval recall above 90%.

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Challenge brief

What you are building

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

Build an audio-driven power plant inspection RAG assistant using LlamaIndex indexing and Deepgram transcription.

Delivery guide

How work is evaluated

Evaluation

Evaluates audio-to-RAG accuracy on CCGT plant maintenance queries.

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

Transcription Accuracy

Ensures transcript contains key domain terms like 'casing B' and 'hot start'.

Binary check

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

Dimension 2

Retrieval Recall@3

Percentage of queries where correct manual section is in top 3 retrieved results. • 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

  • Transcribe site voice memos using Deepgram Speech-to-Text SDK

  • Index thermal power plant documentation with LlamaIndex vector indices

  • Combine audio queries with semantic search over engineering documents

  • Deliver actionable maintenance summaries to plant engineering managers

Resources and assets

Reference links and supporting material

Dataset notes

A technical manual dataset covering 1,000 pages of CCGT power plant specs and 50 recorded voice queries from Oman site engineers.

How this agent runs

Evaluates audio-to-RAG accuracy on CCGT plant maintenance queries.

Challenge input

Audio file path or base64 WAV stream containing technical question

LlamaIndex

Framework for enterprise document indexing.

Deepgram

Speech-to-text engine for site operator audio feeds.

Evaluated output

JSON object with transcript, document_chunk_id, and final answer

Checks for
  • Ensures transcript contains key domain terms like 'casing B' and 'hot start'.
  • Percentage of queries where correct manual section is in top 3 retrieved results. • target: 0.9 • range: 0-1
Proof of success
  • Benchmark: RAGBench-Audio
  • Retrieval Recall@3 target: 0.9
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Deepgram · Required
  • Llama Index · Optional
  • Exa · Optional

Evaluation contract

  • Transcription Accuracy · Weight 1
  • Retrieval Recall@3 · Weight 1

Recipe state

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

Frequently Asked Questions about Oman Power Plant Telemetry Intelligence Engine with LlamaIndex & Deepgram