Imagine effortlessly turning your unstructured data into rich knowledge graphs and querying them with natural language. Over the past year, GraphRAG applications have gotten a lot of attention, yet creating high-quality knowledge graphs from raw data remains a major bottleneck.
The use of large language models (LLMs) for entity relationship extraction (ERE) tasks has significantly eased the knowledge graph construction process. That said, without proper guidance, even the most powerful LLM c...
Read it: Unleashing the Power of Schema: What’s New in the neo4j-graphrag Python Package