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Challenge

Build a LlamaIndex AYUSH DPI Traditional Medicine RAG Knowledge Engine

Integrating traditional medicine into digital public health stacks requires verifiable retrieval over classical Ayurvedic texts and modern clinical trials. Build a LlamaIndex hybrid RAG pipeline with dynamic metadata filtering to answer clinical queries with 95% source attribution accuracy.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop a LlamaIndex RAG system over classical AYUSH medical texts featuring metadata filters and chunk-level citation tracking.

Delivery guide

How work is evaluated

Evaluation

Evaluates query retrieval recall and exact citation match precision.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

attribution_check

Verifies source attribution score meets threshold

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

attribution_score

Accuracy ratio of cited source documents to generated facts • target: 0.96 • 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

What you should walk away with

  • Construct LlamaIndex vector and keyword hybrid indices over classical medical texts

  • Implement dynamic metadata filtering by herb taxonomy and formulation type

  • Enforce precise source attribution and verbatim chunk reference extraction

  • Deploy sub-node query engine pipelines for clinical decision support

Resources and assets

Reference links and supporting material

Dataset notes

Corpus of digitized WHO and AYUSH traditional medicine research reports and clinical trial metadata.

How this agent runs

Evaluates query retrieval recall and exact citation match precision.

Challenge input

JSON query string regarding botanical formulation

LlamaIndex

RAG index creation and retrieval orchestration

Evaluated output

JSON answer string with citation_ids array

Checks for
  • Verifies source attribution score meets threshold
  • Accuracy ratio of cited source documents to generated facts • target: 0.96 • range: 0-1
Proof of success
  • Benchmark: Medical RAG Benchmark
  • Attribution Score target: 0.96
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • Exa · Optional
Observation
  • Langfuse · Optional
Reward / Eval
  • Langfuse · Optional

Evaluation contract

  • attribution_check · Weight 1
  • attribution_score · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Build a LlamaIndex AYUSH DPI Traditional Medicine RAG Knowledge Engine