Salalah AI & Hydrogen Knowledge RAG Assistant using LlamaIndex
Middle East power demand is projected to double by 2050, requiring rapid access to energy transition research. Build an enterprise RAG query engine using LlamaIndex to index technical papers from the Salalah Energy Forum and provide grounded answers on hydrogen integration.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an advanced vector RAG index using LlamaIndex to query power demand forecasts and green hydrogen research from the Salalah Energy Forum.
How work is evaluated
Evaluates accuracy of query answer retrieval and faithfulness against Salalah energy paper ground truth.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Target Year Match
Verifies that extracted projection target year is 2050.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Retrieval Accuracy
Percentage of accurate facts retrieved. • target: 1 • 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 energy research publications using LlamaIndex data connectors
Configure vector store indexing with localized metadata filtering
Build a query engine with source citation enforcement for power doubling projections
Evaluate retrieval faithfulness and answer relevance scores
Reference links and supporting material
Text corpus of Salalah Forum energy system papers and Rystad Middle East 2050 power demand reports.
How this agent runs
Evaluates accuracy of query answer retrieval and faithfulness against Salalah energy paper ground truth.
Challenge input
JSON object with user query string.
LlamaIndex
Enterprise RAG indexing and document retrieval
Evaluated output
JSON object with retrieved_answer, cited_year, and projected_demand_growth.
- Verifies that extracted projection target year is 2050.
- Percentage of accurate facts retrieved. • target: 1 • range: 0-1
- Benchmark: GCC Energy RAG QA Benchmark
- Retrieval Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Llama Index · Required
- LangWatch · Optional
- Cartesia · Optional
Evaluation contract
- Target Year Match · Weight 1
- Retrieval Accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.