Build a Voice-Enabled Substation Procurement RAG Agent with LlamaIndex and LiveKit
High-voltage substation EPC bidding involves complex technical specifications and rapid query responses for site engineers. Build an interactive voice and document query agent using LlamaIndex and LiveKit that synthesizes technical schematics and procurement constraints with under 1.5 seconds latency.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a voice-driven technical document RAG agent using LlamaIndex indexers and LiveKit real-time voice sessions.
How work is evaluated
Evaluates accuracy of answers extracted from substation contract PDFs and latency over LiveKit voice channel.
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.
latency_test
Ensures query response time is under 1500ms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
retrieval_precision
Cosine similarity of generated response with technical reference specification • 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 high-performance vector indices for engineering specifications using LlamaIndex
Stream natural voice queries and synthesized answers using LiveKit WebRTC SDK
Implement dynamic vector routing for Riyadh Expo 380kV substation technical codes
Measure and optimize end-to-end voice query latency below 1500ms
Reference links and supporting material
Indexed specification documents covering 380kV electrical substation engineering standards, procurement BOMs, and Expo site layout specs.
How this agent runs
Evaluates accuracy of answers extracted from substation contract PDFs and latency over LiveKit voice channel.
Challenge input
Audio chunk or audio transcript string query
LlamaIndex
Vector indexing and document retrieval
LiveKit
Real-time audio session management
Evaluated output
JSON containing string response, source_node_id, latency_ms
- Ensures query response time is under 1500ms
- Cosine similarity of generated response with technical reference specification • target: 0.9 • range: 0-1
- Benchmark: RAG QA Standard Bench
- Retrieval Precision target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- LiveKit · Required
- Llama Index · Optional
- Exa · Optional
Evaluation contract
- latency_test · Weight 1
- retrieval_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.