Riyadh Expo Substation RAG Assistant with LlamaIndex & ElevenLabs
Electrical engineering teams on gigaproject sites require immediate access to substation engineering schemas and vendor specifications. Build a hybrid search RAG agent using LlamaIndex that answers technical queries and delivers audio briefings via ElevenLabs.
What you are building
The core problem, expected build, and operating context for this challenge.
Combine LlamaIndex document stores with voice synthesis to query high-voltage substation engineering documentation.
How work is evaluated
Measures response context recall and correctness for substation technical questions.
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.
fact_accuracy_check
Checks if '63kA' and '380kV' exist in generated answer text.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
retrieval_relevance
Cosine similarity of retrieved context nodes against golden reference. • 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
Build a structured RAG pipeline with LlamaIndex over substation technical specifications
Implement dynamic metadata filtering for engineering drawings and voltage ratings
Integrate ElevenLabs text-to-speech API for field audio briefing generation
Optimize retrieval accuracy using hybrid dense-sparse index structures
Reference links and supporting material
Substation project documentation including technical manuals, single-line diagrams descriptions, and vendor specs for high-voltage installations.
How this agent runs
Measures response context recall and correctness for substation technical questions.
Challenge input
JSON with query string and project_code
LlamaIndex
Framework used for indexing and document retrieval.
ElevenLabs
Generates clear field audio updates from text answers.
Evaluated output
JSON with answer text and audio_stream_url
- Checks if '63kA' and '380kV' exist in generated answer text.
- Cosine similarity of retrieved context nodes against golden reference. • target: 0.9 • range: 0-1
- Benchmark: RAG-Engineering-Eval
- Retrieval Relevance target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- ElevenLabs · Required
- Llama Index · Optional
- LangWatch · Optional
Evaluation contract
- fact_accuracy_check · Weight 1
- retrieval_relevance · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.