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Challenge

Build an LNG Project Knowledge Retrieval System with CrewAI & Milvus

Managing technical specifications for complex LNG projects requires multi-agent document analysis. Build a collaborative multi-agent system using CrewAI and Milvus vector database to index FEED documents, extract environmental specs, and verify engineering compliance.

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Challenge brief

What you are building

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

Deploy specialized CrewAI agent roles backed by Milvus vector index to analyze LNG terminal engineering specifications.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent CrewAI retrieval and agreement accuracy on LNG technical specs.

Datasets

Challenge datasets

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

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

meets_standard_check

Verify compliance result is accurate based on spec standard

Binary check

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

Dimension 2

retrieval_recall

Recall metric of relevant technical document chunks from Milvus • target: 0.96 • range: 0.85-1

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

  • Orchestrate CrewAI agents with dedicated roles: Engineering Compliance Analyst and Procurement Lead

  • Implement high-performance vector search using Milvus for technical FEED documentation

  • Verify LNG cryo-tank insulation and environmental emission compliance standards

  • Produce consolidated multi-agent audit summary reports

Resources and assets

Reference links and supporting material

Dataset notes

Engineering specification documents, FEED studies, and regulatory standards for UAE LNG terminals.

Challenge setup

Evaluates multi-agent CrewAI retrieval and agreement accuracy on LNG technical specs.

Challenge input

JSON containing spec query and target standards

CrewAI

Multi-agent coordination for specialized role playback

Milvus

Scalable search over large technical PDF collections

Evaluated output

JSON evaluation report

Evaluation checks
  • Verify compliance result is accurate based on spec standard
  • Recall metric of relevant technical document chunks from Milvus • target: 0.96 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: RAGBench-Engineering
  • Retrieval Recall target: 0.96
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Milvus · Optional

Evaluation contract

  • meets_standard_check · Weight 1
  • retrieval_recall · Weight 1

Metrics

  • retrieval_recall · Target 0.96

Benchmarks

  • RAGBench-Engineering
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing spec query and target standards

    Output: JSON evaluation report

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework
  • Milvus · Vector DB

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build an LNG Project Knowledge Retrieval System with CrewAI & Milvus