Graph-based retrieval is displacing the pure vector store in production RAG stacks. Here is how Graphify works, why it outperforms flat embeddings on multi-hop questions, and what it takes to migrate.
Introduction Enterprises are increasingly turning to large language models (LLMs) to power conversational assistants, knowledge‑base search, and decision‑support tools. While LLMs excel at generating fluent text, they struggle with grounded, up‑to‑date factuality when the underlying data is scattered across documents, databases, and legacy systems. Graph Retrieval‑Augmented Generation (GraphRAG) addresses this gap by coupling an LLM with a knowledge graph that stores both entities and the relationships between them. The graph acts as a structured memory that the model can query, retrieve, and reason over, delivering context‑rich answers that are both accurate and explainable. ...