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.
What you are building
The core problem, expected build, and operating context for this challenge.
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
Evaluates RAG retrieval precision, compliance violation detection, and query execution latency.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
boundary_detection
Verifies illegal data flows crossing DMZ directly are rejected
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
search_time_ms
Query execution time in milliseconds • target: 500 • range: 0-3000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
Purdue Model architecture layout schema defining 1,000 factory asset nodes, assigned Purdue levels, and network connectivity permissions.
How this agent runs
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)
- Verifies illegal data flows crossing DMZ directly are rejected
- Query execution time in milliseconds • target: 500 • range: 0-3000
- Benchmark: Industrial RAG Security Benchmark
- Search Time Ms target: 500
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Llama Index · Required
- LlamaIndex · Optional
- Together AI · Optional
- Together AI · Optional
Evaluation contract
- boundary_detection · Weight 1
- search_time_ms · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.