What is Graph RAG?
Graph RAG (Retrieval-Augmented Generation) combines the power of Knowledge Graphs with Large Language Models (LLMs) to provide more accurate, context-aware, and connected answers than traditional vector-based RAG.
How it Works
Instead of treating your data as isolated chunks of text, Graph RAG understands the relationships between entities in your documents.
1. Ingestion & Extraction
Documents are processed, and entities (people, places, concepts) and their relationships are extracted using an LLM.
2. Graph Construction
A structured Knowledge Graph is built, linking these entities together to form a web of understandable data.
3. Graph Traversal
When you ask a question, the system traverses the graph to find connected information, even if it's across different documents.
4. Contextual Generation
The LLM generates an answer using this rich, interconnected context, reducing hallucinations and improving depth.
Traditional RAG vs. Graph RAG
Traditional RAG
"Finds matching keywords/vectors. Good for simple fact retrieval but misses the 'big picture' or hidden connections."
Graph RAG
"Understands that 'Alice' works for 'Company X' which released 'Product Y'. Can answer 'What products did Alice's company release?' by traversing relationships."
Why Choose Our Solution?
Unlock the full potential of your unstructured data.
Structured Knowledge
Turn messy documents into a clean, queryable knowledge graph automatically.
Discover Insights
Find hidden connections and trends that simple keyword searches miss completely.
Multi-Hop Reasoning
Answer complex questions that require connecting dots across multiple sources.
Ready to Experience Graph RAG?
Start transforming your data into a knowledge engine today.
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