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Offshore EPC Tender Intelligence System with LlamaIndex

Qatar's massive offshore oil field expansion tenders feature thousands of technical specifications, HSE addendums, and commercial terms. Build an advanced RAG and agentic evaluation framework using LlamaIndex with hybrid vector/BM25 retrieval. Achieve a retrieval Mean Reciprocal Rank (MRR) above 0.85 on engineering query evaluations.

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What you are building

Construct an agentic RAG index using LlamaIndex with hybrid retrieval and reranking to answer technical queries on Qatar offshore EPC tender documents.

How work is evaluated

Evaluation

Measures retrieval MRR and answer correctness on complex offshore EPC tender queries.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

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

source_citation_presence

Checks that synthesized response includes explicit document page citations

Binary check

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

Dimension 2

retrieval_mrr

Mean Reciprocal Rank of relevant chunks in top-5 retrieval results • target: 0.85 • 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

  • Implement hierarchical document indexing and hybrid vector + BM25 search in LlamaIndex

  • Incorporate reranking models (e.g., Cohere or sentence-transformers) for context optimization

  • Build dynamic Query Engine Tools integrated into a LlamaIndex agent workflow

  • Evaluate retrieval quality using TrueLens or LlamaIndex RAG evaluation metrics

Resources

Dataset notes

Dataset of 50 synthetic offshore engineering specification documents covering jackets, topsides, and subsea pipelines.

Challenge setup

Measures retrieval MRR and answer correctness on complex offshore EPC tender queries.

Challenge input

JSON object with tender question string and target document collection ID

LlamaIndex

Leading framework for structured data indexing and retrieval synthesis.

Evaluated output

JSON containing synthesized answer, cited source document IDs, and confidence score

Evaluation checks
  • Checks that synthesized response includes explicit document page citations
  • Mean Reciprocal Rank of relevant chunks in top-5 retrieval results • target: 0.85 • range: 0-1
Configured evaluation evidence
  • Benchmark: RAGBench-Industrial
  • Retrieval Mrr target: 0.85
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Llama Index · Required
  • Traceloop · Optional
  • Deepgram · Optional

Evaluation contract

  • source_citation_presence · Weight 1
  • retrieval_mrr · Weight 1

Metrics

  • retrieval_mrr · Target 0.85

Benchmarks

  • RAGBench-Industrial
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with tender question string and target document collection ID

    Output: JSON containing synthesized answer, cited source document IDs, and confidence score

    Execution harness: Python

Evaluation tools

  • LlamaIndex · RAG Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Offshore EPC Tender Intelligence System with LlamaIndex