Build a LlamaIndex Pediatric Genomics Decision Agent on Amazon Bedrock
Pediatric rapid Whole-Genome Sequencing (rWGS) produces vast variant calls that require rapid mapping against Human Phenotype Ontology (HPO) terms. Construct a LlamaIndex decision support agent deployed on Amazon Bedrock that indexes variant annotations and provides phenotype-driven diagnostic candidates.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a LlamaIndex RAG and decision agent on Amazon Bedrock to match pediatric rWGS genomic variants with HPO clinical terms.
How work is evaluated
Evaluates candidate disease ranking accuracy given patient HPO phenotype terms and variant call inputs.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
top_candidate_match
Verifies correct disease is ranked in top 3 positions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mean_reciprocal_rank
MRR score of true diagnosis across rare disease benchmark test cases. • target: 0.88 • 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 LlamaIndex vector and graph index over HPO and genomic transcript databases
Integrate Amazon Bedrock Claude foundation models for high-throughput clinical reasoning
Design query engines capable of phenotype-to-genotype candidate disease ranking
Implement audit logging for reproducible clinical evidence chains
Reference links and supporting material
Cohort of 40 pediatric clinical cases containing HPO phenotype codes and associated pathogenic variant calls with verified diagnoses.
Challenge setup
Evaluates candidate disease ranking accuracy given patient HPO phenotype terms and variant call inputs.
Challenge input
JSON containing hpo_terms (list of strings) and detected_variants (list of strings).
LlamaIndex
Structures indices and retrieval tools.
Amazon Bedrock
Provides enterprise-grade LLM inference.
Evaluated output
JSON with ranked_diseases (list of objects with disease_id and score).
- Verifies correct disease is ranked in top 3 positions.
- MRR score of true diagnosis across rare disease benchmark test cases. • target: 0.88 • range: 0-1
- Benchmark: HPO Rare Disease Retrieval Benchmark
- Mean Reciprocal Rank target: 0.88
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Amazon Bedrock · Required
- Llama Index · Optional
- MutableAI · Optional
Evaluation contract
- top_candidate_match · Weight 1
- mean_reciprocal_rank · Weight 1
Metrics
- mean_reciprocal_rank · Target 0.88
Benchmarks
- HPO Rare Disease Retrieval Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing hpo_terms (list of strings) and detected_variants (list of strings).
Output: JSON with ranked_diseases (list of objects with disease_id and score).
Execution harness: Python
Evaluation tools
- LlamaIndex · RAG Framework
- Amazon Bedrock · Cloud Model Platform
Recipe freeze marker
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