{"id":2203,"date":"2025-07-22T10:16:20","date_gmt":"2025-07-22T10:16:20","guid":{"rendered":"https:\/\/gyrus.ai\/blog\/?p=2203"},"modified":"2026-01-23T11:54:23","modified_gmt":"2026-01-23T11:54:23","slug":"rag-worked-but-search-graphrag-works-better","status":"publish","type":"post","link":"https:\/\/gyrus.ai\/blog\/rag-worked-but-search-graphrag-works-better\/","title":{"rendered":"RAG Worked. But for Search, GraphRAG Works Better."},"content":{"rendered":"

Imagine RAG like searching a library by keywords, generating keyword hits that are then passed to a language model for some dot-connecting. Works well when answers to simple queries are needed, but what happens when linked facts from different places are needed?\u00a0<\/span><\/p>\n

GraphRAG<\/a> structures knowledge into entities and relations, allowing answers to be formed in a more considered and connected fashion. It is like moving away from disorganized index cards to a smart map of relations. This is an excellent option for media search<\/a>, for whom “who said what when, in what context” matters enormously.<\/span><\/p>\n

How RAG Works (Briefly):<\/strong><\/h3>\n

\"Knowledge<\/p>\n

In RAG, chunks of documents are converted into vectors (numerical form). These vectors are then searched against your query by matching the best correspondences. The retrieved chunks are passed into the LLM<\/a>, together with the user\u2019s question, thus giving an answer based on the given content.\u00a0<\/span><\/p>\n

It is excellent for Q&A, but it loses the context when the relationship extends across chunks or when reasoning must follow a chain.<\/span><\/p>\n

What is GraphRAG – Simply Explained<\/strong><\/h2>\n

At a high level, the way GraphRAG works is that it creates the knowledge graph from data sources. So each real-world entity(item\/person, event, scene) gets converted into a node.<\/span><\/p>\n

Then, relationships such as “spoke about”, “appeared with”, “follows” become edge-relations.<\/span><\/p>\n

\"LLM<\/p>\n

Why GraphRAG Outperforms RAG:<\/strong><\/h3>\n

With a custom embedding framework, this specifies the semantic search workflow of Gyrus’ solution<\/a>:<\/span><\/p>\n

\"Gyrus<\/h3>\n

1. Clearer Reasoning (Multi-Hop):\u00a0<\/strong><\/p>\n

GraphRAG is a multi-step approach:<\/span><\/p>\n

For instance, “Find all scenes where Alice mentions topic A after event B.” <\/span>RAG cannot follow that path because it seems, to it, that the chunks are isolated.<\/span><\/p>\n

2. More Accurate & Trustworthy:\u00a0<\/strong><\/p>\n

GraphRAG lets one trace reasoning: “Alice node \u2192 edge mentions \u2192 topic node \u2192 clip node.” You can explain in detail how the answer was constructed, making the process far more transparent and worth trusting.<\/span><\/p>\n

3. Efficient Retrieval:<\/strong><\/p>\n

Instead of issuing irrelevant chunk retrieval, GraphRAG can find the relevant subgraph instead-meaning shorter, faster, and more focused prompt creation.\u00a0<\/span><\/p>\n

4. Handles Structured Knowledge Naturally:\u00a0<\/strong><\/p>\n

Graphs become very useful when knowledge becomes relational, such as timelines, speaker-to-scene associations, or event sequencing. RAG can’t implicitly represent this kind of structure-GraphRAG can.<\/span><\/p>\n

GraphRAG in Intelligent Media Search:<\/strong><\/h3>\n

Here’s how our system unites everything: <\/span><\/p>\n

\"Gyrus<\/p>\n