Build a Clearinghouse Real-Time Risk Exposure RAG Engine with LlamaIndex
Securities clearinghouses handling billions in daily trade volume need continuous vector-indexed risk policy enforcement. Build an advanced RAG query and tool-calling agent using LlamaIndex to query margin liquidity rules and assess broker-dealer clearing exposure. Achieve sub-second retrieval with 96% context relevance across complex clearinghouse handbooks.
What you are building
Construct an advanced LlamaIndex RAG agent to evaluate broker-dealer clearing collateral exposure against clearinghouse rulebooks.
How work is evaluated
Evaluate retrieval precision and margin deficit calculation accuracy.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Correct Deficit Calculation
Ensure margin_call_issued triggers when required margin exceeds cash collateral.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Context Precision
Precision of retrieved regulatory text context. • target: 0.96 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Build a hierarchical vector index using LlamaIndex over complex clearinghouse rulebooks
Implement LlamaIndex QueryEngine tools to calculate dynamic margin maintenance calls
Validate broker-dealer clearing account capital adequacy under high-volatility stress scenarios
Ensure context retrieval accuracy with zero hallucinated policy citations
Resources
Synthetic dataset of 300 clearinghouse risk manual sections paired with 1,000 broker capital telemetry frames.
Challenge setup
Evaluate retrieval precision and margin deficit calculation accuracy.
Challenge input
JSON containing broker_id, cash_collateral, equity_exposure, and market_volatility_index.
LlamaIndex
Offers specialized indexing and retrieval primitives for complex structured documents.
Evaluated output
JSON containing margin_required, margin_call_issued (boolean), and cited_rule_section.
- Ensure margin_call_issued triggers when required margin exceeds cash collateral.
- Precision of retrieved regulatory text context. • target: 0.96 • range: 0-1
- Benchmark: FinRAGBench
- Context Precision target: 0.96
- 1 public reference case
- 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
- Llama Index · Required
- LlamaIndex · Optional
- Llama 3.2 · Optional
Evaluation contract
- Correct Deficit Calculation · Weight 1
- Context Precision · Weight 1
Metrics
- Context Precision · Target 0.96
Benchmarks
- FinRAGBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing broker_id, cash_collateral, equity_exposure, and market_volatility_index.
Output: JSON containing margin_required, margin_call_issued (boolean), and cited_rule_section.
Execution harness: Python
Evaluation tools
- LlamaIndex · Data & RAG Framework
Recipe freeze marker
No recipe freeze time is recorded.