ConfidentialMind GraphRAG is a knowledge graph-based retrieval-augmented generation system that achieves 20-30% more accurate responses.
GraphRAG vs Basic RAG
RAG improves LLM's limited capabilities of generating text based on its training data by retrieving additional information from files and database. LLM models use the findings to generate better answers to the user query. But it comes with a challenge. These machine-learning models struggle to create highly accurate responses when data is stored in vector databases. GraphRAG is a technique that improves that by storing data in a graph-based structure. In that space, it creates better contextual relationships between content. This allows LLM models to understand the meaning behind data and use these to generate even better responses. Here are the two approaches explained:
Basic RAG
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Stores data in vector databases
Creates vector-based relationships
Linear data retrieval
GraphRAG
Stores data in knowledge graphs
Creates semantic-based relationships
Advanced query matching with relevant data
The Real Advantage of Using GraphRAG System
GraphRAG shows significant improvements over the basic RAG system, with a particularly impressive 30% improvement in exact matches.
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Create a connection with your data without moving data away from your environment.
Our platform creates easy-to-use APIs, so you can simply integrate the GraphRAG system into your internal tools, products, or services.
GraphRAG is a technique that improves basic RAG capabilities by storing data in the knowledge graph structure, not a vector database. In that space, semantic relationships between related and unrelated data are created. So, LLM models can retrieve more relevant information based on the users' query and generate better answers.
Here is a simplified version of how GraphRAG works:
- Graph construction - The system creates a graph structure using all the data you provide access to. Information is stored using nodes as entities, and edges as relationships.
- Relevant information retrieval - When users write a query, the system matches it with the most relevant documents found in the graph.
- Accurate response generation - LLM models use the findings to generate accurate responses to the users' queries.
The alternative is to use basic RAG, which is faster in generating outputs but less accurate.
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