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#4114 Knowledge Graph Cypher Queries & LLM Prompts

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#4114 Knowledge Graph Cypher Queries & LLM Prompts #4114  Given """Knowledge graph 'Artemis III Integrated Hazard Reports' schema: Entities: Program, Mission, Hazard_Report, Subsystem Hazard_Cause, Hazard_Cause_Control, Hazard_Cause_Control_Verif Relationships: (Program)-[:HAS_MISSION]->(Mission) (Mission)-[:HAS_SUBSYSTEM]->(Subsystem) (Mission)-[:HAS_HR]->(Hazard_Report) (Hazard_Report)-[:REFS_SUBSYSTEM]->(Subsystem) (Hazard_Report)-[:HAS_CAUSE]->(Hazard_Cause) (Hazard_Cause)-[:HAS_CONTROL]->(Hazard_Cause_Control) (Hazard_Cause_Control)-[:HAS_VERIF]->(Hazard_Cause_Control_Verif) (Mission)-[:HAS_DOC]->(Document) (Hazard_Report)-[:HAS_DOC]->(Document) (Hazard_Cause)-[:HAS_DOC]->(Document) (Hazard_Cause_Control)-[:HAS_DOC]->(Document) (Hazard_Cause_Control_Verif)-[:HAS_DOC]->(Document)""" List and describe ten useful queries for first-time reviewer of 30 hazard reports represented in the graph Stepping into a r...
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#4113 Step-by-Step KG Queries

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#4113 Step-by-Step KG Queries #4113  Describe step-by-step how to query a Neo4j knowledge graph containing nodes, edges and embedded documents from Python Querying a Neo4j knowledge graph—especially one that mixes structured graph data (nodes/edges) with unstructured data (embedded documents)—requires a blend of the Neo4j Python Driver and, typically, a Vector Index for the embeddings. Here is the step-by-step workflow to get your Python environment talking to your graph. 1. Set Up Your Environment First, install the official driver. If you are handling embeddings, you’ll likely need a library like openai or sentence-transformers to process your query text into a vector. pip install neo4j 2. Establish the Connection You need to create a GraphDatabase driver instance. It’s best practice to manage this connection via a class or a context manager to ensure the session closes properly. from neo4j import GraphDatabase class KnowledgeGraphQuery : def __init__ ( self, uri,...
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