GraphRAG is a RAG framework that utilizes Knowledge Graphs to enhance information retrieval and processing. A significant aspect of this framework is the use of large language models (LLMs) for Named Entity Recognition (NER) within Neo4j.
Related Terms
- How to search within a graph
- Text2Cypher: This feature allows users to interact with the graph in a user-friendly manner, converting natural language queries into Cypher queries.
- How to move datasets into a graph database.
- Graphrag patterns.
- The role of interpretability in understanding graph-based retrieval.
Implementation
I discovered an insightful LinkedIn post discussing the potential of knowledge graphs: This specific graph is called a “Lexical Graph with Extracted Entities”. LinkedIn Post In ML_Tools see: Wikipedia_API.py