Knowledge & Data · Software component
Graph Retriever
Software componentKnowledge & DataKnowledge & Dataarc:GraphRetriever
A retriever that executes pattern-matching traversal queries over a knowledge graph and returns structured results.
Responsibility. Executes graph pattern-matching traversals and returns structured results.
Also known as: Graph Query Executor, Graph Traversal Retriever
Variant of Retriever abstract
When to choose. Choose when queries require following typed relationships across multiple hops (e.g., conflict-of-interest chains, investor overlap).
Relationships
invokes dependency
is cached by dependency
is invoked by dependency
reads dependency
- Archive Graph Store Ch1.7B
- Knowledge Graph Store abstract Ch1.7A Ch5.7 +2
sends data to dynamic
- Answer Synthesizer abstract Ch1.7A
is guarded by control
is evaluated by assurance
alternative to variability
- Hybrid Retriever abstract Ch1.7A Ch5.7 +1
- Vector Retriever abstract Ch1.7A Ch5.7 +1
Design guidance
- MUST use parameterized queries to prevent Cypher injection from user-supplied entity names.
- SHOULD express relational questions declaratively as graph patterns rather than as LLM reasoning tasks.
- SHOULD specify relationship types and apply LIMIT clauses, and profile queries (EXPLAIN/PROFILE) to confirm index seeks instead of label scans.
- SHOULD query archived graph partitions only when explicitly needed.
Quantitative guidance
As stated by the sources; verify before use.
- Knowledge graph only: medium semantic coverage, high relational accuracy, p95 ~80ms, medium complexity (Ch1.7B).
Classification
- Patterns
- Multi-hop traversalCyclic pattern match (four-hop conflict detection)
- Technologies
- Neo4jCypher
- Quality attributes
- Functional suitability: correctness and validity (ISO/IEC 25010 | NIST AI RMF: valid)Security (ISO/IEC 25010 | NIST AI RMF: secure and resilient)
- Risks mitigated
- Cypher injectionLLM errors in maintaining multi-hop logical chains
Sources
- Ch1.7A: T. Nguyen, "Relational Reasoning with Knowledge Graphs - The Fundamentals, Integration, and Extraction," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.7A. ISBN: 9798244538229.
- Ch1.7B: T. Nguyen, "Relational Reasoning with Knowledge Graphs - Hybrid RAG+KG Integration," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 1.7B. ISBN: 9798244538229.
- Ch3.6: T. Nguyen, "Trace Analysis and Execution Debugging," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 3.6. ISBN: 9798244538229.
- Ch5.7: T. Nguyen, "Episodic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.7. ISBN: 9798244538229.
- Ch5.8: T. Nguyen, "Semantic Memory," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.8. ISBN: 9798244538229.
- Ch5.13: T. Nguyen, "Hybrid Decision Systems Fundamentals," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 5.13. ISBN: 9798244538229.