GraphRAG

This technique allows a system to answer questions where the truth lies in how different facts relate to one another, not just in finding documents that contain the same words as your question. It prepares you for systems that can reason through complex information chains, combine keyword matching with deep meaning analysis, and build personal assistants for companies that truly understand their own records. By relying on these structured connections, the system avoids inventing plausible-sounding lies—a problem known as hallucination—because it follows verified links between facts rather than guessing based on probability.

Imagine you have a box of recipe cards. Each card has a title and a list of ingredients. If someone asks, 'What can I make with paneer and spinach?', you can flip through the cards and find the ones that mention both. That is like finding similar documents: it looks for matches in meaning. But now suppose someone asks, 'Which of my friends would enjoy a dinner party with both a vegetarian and a strict vegan guest?' The answer is not on any single card. You need to know that Priya is vegetarian, that Rohan is vegan, that both of them like Italian food, and that the restaurant you have in mind serves both. That knowledge is spread across multiple cards, and the answer comes from connecting them: Priya likes vegetarian meals, which connects to the restaurant's menu, which connects to Rohan's preference. That is the problem GraphRAG solves.

To solve this, we build a map. On this map, individual items like people or places are dots. The facts linking them are lines. When you ask a question that requires linking these items together, GraphRAG does not just look for matching words. It starts at one dot (for example, Priya), follows the line to the next dot (the restaurant), and follows another line to Rohan. It walks the path. The key insight is that the answer is not in any single document; it is in the connections between facts scattered across many documents.

Think of a city's road network. A map is a graph: intersections are entities like 'Chai Point' or 'Metro Station', and roads are relationships like 'is next to' or 'is 200 metres from'. If someone asks, 'How do I get from the station to the bookshop?', you look at the map and follow the roads. Now imagine you have a stack of photo albums, each with pictures of a different part of the city. If you only had the photos, you would have to flip through every album to find a picture of the station and a picture of the bookshop, then try to guess if they are connected—but they might not even be in the same album. That is like traditional search: you have the pieces, but not the connections. GraphRAG is the map. It is built by looking at all the photos (documents), extracting the landmarks (entities) and the roads between them (relationships), and drawing them on a single map. When someone asks a question that spans multiple landmarks, you trace the route on the map instead of searching through photos. Where the analogy stops: a map is static, but a knowledge graph can be updated as new documents arrive, and it can show any kind of relationship, not just physical distance, such as 'works for' or 'is allergic to'.

GraphRAG is a method that converts a collection of documents into a structured map of facts (entities) and the links between them (relationships), then answers questions by following those links to find connected information rather than relying solely on word matching.

This concept builds on Embeddings, which is how text is converted into numerical representations for search, and Vector Databases, which are systems that store and retrieve those numbers efficiently. It also connects closely with Reranking and Retrieval Quality, because even with a map, you must still judge how well the retrieved information matches your intent. Most importantly, it complements Hybrid and Semantic Search, as you will often combine simple word matching with this deeper connection tracking. It serves as the foundation for Agentic Retrieval, where an AI actively chooses which paths to walk on the map, and Enterprise Knowledge Assistants, where the map is built from a company's private documents.