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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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What you are building

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

How work is evaluated

Evaluation

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

Datasets

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

How submissions are scored

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

  • 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

Dataset notes

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

Challenge setup

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

Evaluation checks
  • 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
Configured evaluation evidence
  • Benchmark: RAGBench-Audio
  • Retrieval Recall@3 target: 0.9
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

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

Metrics

  • Retrieval Recall@3 · Target 0.9

Benchmarks

  • RAGBench-Audio
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Audio file path or base64 WAV stream containing technical question

    Output: JSON object with transcript, document_chunk_id, and final answer

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Data Orchestration / RAG
  • Deepgram · Audio Speech Recognition

Recipe freeze marker

No recipe freeze time is recorded.

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