Morphik Core's integrated knowledge graph engine adds a structured knowledge dimension to traditional RAG systems through automated entity recognition and relationship extraction techniques. The system automatically analyzes named entities (e.g., people, organizations, technical terms, etc.) and their interrelationships in the content when processing documents to build an inferable knowledge network. When processing a complex query such as "how is AI related to cloud computing", the system is able to provide cross-document related answers based on the semantic connections of the graph.
In practical applications, researchers use the function to discover cross-domain innovation point associations in the thesis library, and enterprise users use it to analyze the competitive landscape of the market. The system supports customized graph construction conditions (via filters parameter) and query depth control (hop_depth parameter), and can achieve up to 3 degrees of relational reasoning.
Performance tests show that Knowledge Graph improves relevance scores for complex question retrieval by an average of 621 TP3 T. This feature is particularly suitable for scenarios that require deep semantic analysis, such as academic research, business intelligence analysis, and other specialized areas.
This answer comes from the articleMorphik Core: an open source RAG platform for processing multimodal dataThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil