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LlamaIndex Purdue Model OT/IT RAG Pipeline with Together AI

Integrating Level 0-3 operational telemetry with cloud data lakes risks breaking network segmentation protocols. Build a secure RAG metadata indexing agent using LlamaIndex and Together AI to parse Purdue Model architectures and validate data flow compliance. The system must index 1,000 node schemas and flag boundary violations in under 3 seconds.

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What you are building

Construct an OT/IT architecture indexing engine using LlamaIndex and Together AI inference to query enterprise Purdue Model metadata without breaching firewalls.

How work is evaluated

Evaluation

Evaluates RAG retrieval precision, compliance violation detection, and query execution latency.

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

boundary_detection

Verifies illegal data flows crossing DMZ directly are rejected

Binary check

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

Dimension 2

search_time_ms

Query execution time in milliseconds • target: 500 • range: 0-3000

Binary check

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

Learning goals

  • Implement a LlamaIndex vector store for Purdue Model Level 0 to Level 4 system schemas

  • Configure Together AI inference endpoint for ultra-fast RAG embedding and generation

  • Enforce OT network safety policy checks on query pathways across DMZ boundaries

  • Benchmark retrieval precision and context grounding for industrial asset queries

Resources

Dataset notes

Purdue Model architecture layout schema defining 1,000 factory asset nodes, assigned Purdue levels, and network connectivity permissions.

Challenge setup

Evaluates RAG retrieval precision, compliance violation detection, and query execution latency.

Challenge input

JSON query specifying source_level, target_level, and proposed protocol

LlamaIndex

Assigned framework for data indexing and enterprise RAG.

Together AI

Assigned inference provider for high-throughput LLM and embedding calls.

Evaluated output

JSON with allowed (bool), boundary_violations (list), context_retrieved (string), search_time_ms (int)

Evaluation checks
  • Verifies illegal data flows crossing DMZ directly are rejected
  • Query execution time in milliseconds • target: 500 • range: 0-3000
Configured evaluation evidence
  • Benchmark: Industrial RAG Security Benchmark
  • Search Time Ms target: 500
  • 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
  • LlamaIndex · Optional
Policy Serving
  • Together AI · Optional
Training Infra
  • Together AI · Optional

Evaluation contract

  • boundary_detection · Weight 1
  • search_time_ms · Weight 1

Metrics

  • search_time_ms · Target 500

Benchmarks

  • Industrial RAG Security Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON query specifying source_level, target_level, and proposed protocol

    Output: JSON with allowed (bool), boundary_violations (list), context_retrieved (string), search_time_ms (int)

    Execution harness: Python

Evaluation tools

  • LlamaIndex · Data Framework
  • Together AI · Inference Engine

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about LlamaIndex Purdue Model OT/IT RAG Pipeline with Together AI