Knowledge Graphs Mimic Human Reasoning? - Basics of RAG-Graph (Part 1)
Demystifying Knowledge Graphs

Knowledge Graphs Mimic Human Reasoning? - Basics of RAG-Graph (Part 1)

Dr. Manish Kumar Saraf - DSC, PhD, MBA, Dr. Manish Kumar Saraf - DSC, PhD, MBA,

Dr. Manish Kumar Saraf - DSC, PhD, MBA,

Associate Director | Artificial |Intelligence | Generative AI Architect | MAS (Muti-Agent System | Knowledge Graph | LLMs | Certified | Automation | Vector Database | Author | Innovator

发布日期: 2024年7月15日
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"Does Knowledge Graphs Work like Human Brain"

OR

"Does Human Brain Thinks Like a Knowledge Graph".

While the human brain operates at a far more complex level, knowledge graphs offer a compelling framework for exploring how we store and utilize knowledge. The development of knowledge graphs represents a significant advancement in computational modeling of knowledge representation, potentially paving the way for future breakthroughs in our understanding of the brain.

This blog post delves into Graph Retrieval-Augmented Generation (GraphRAG), a technique that significantly improves the capabilities of large language models (LLMs) . We'll explore the fundamentals of knowledge graphs, RAG pipelines, and how GraphRAG elevates the quality of AI responses.

Understanding GraphRAG: A Powerful Technique for Enhanced AI Reasoning

Demystifying Knowledge Graphs

Imagine a database that stores information not just as isolated facts, but as interconnected entities and relationships. That's the essence of a knowledge graph. It uses nodes (entities) and relationships (connections) to represent the world in a structured and meaningful way. This allows for a deeper understanding of concepts and how they relate to each other.

Here's a breakdown of key knowledge graph components:

  • Nodes: Represent entities like people, places, or things.
  • Relationships: Define the connections between nodes, indicating type and direction (e.g., "works at," "located in").
  • Properties: Provide additional details about nodes and relationships

Knowledge Graph contains NODE (pick color) connected with RELATIONSHIP (grey arrow).

Knowledge graphs offer several advantages over traditional vector-based representations:

  • Declarative Representation: Knowledge graphs are easier for humans and machines to understand due to their explicit structure.
  • Reasoning Capabilities: Relationships within the graph enable LLMs to reason about information and draw inferences.
  • Iterative Development: You can start with a basic knowledge graph and gradually expand it as needed.

Representation of Apple by Human View, Vector View and Knowledge Graph View

RAG: Boosting LLM Accuracy

Retrieval-Augmented Generation (RAG) is a technique that enhances the factual accuracy and consistency of LLM outputs. It works in three stages:

  1. Retrieval: The LLM retrieves relevant information from a knowledge base (like a knowledge graph) based on the prompt or question.
  2. Augmentation: This retrieved information is used to refine the LLM's internal understanding of the world.
  3. Generation: The LLM leverages its knowledge from training data and the retrieved information to generate a more accurate and reliable response.

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A standard RAG pipeline contains three basic components: Retrieve, Augment and Generate


Introducing GraphRAG: Taking RAG to the Next Level

GraphRAG builds upon the foundation of RAG by incorporating a knowledge graph into the process. This additional step allows for a two-level search:

  1. Vectorized Search: This initial search efficiently identifies documents with similar word usage or surface-level features.
  2. Knowledge Graph Search: The LLM leverages the knowledge graph to find semantically similar documents, even if they don't use the same exact words.

By combining these search methods, GraphRAG retrieves documents that are not only similar in vocabulary but also conceptually related based on the knowledge graph. This leads to a more comprehensive and informative set of results.

Benefits of GraphRAG over Standard RAG

  • Higher Quality Responses: GraphRAG delivers more accurate, complete, and insightful answers by leveraging the knowledge graph for context and evidence.

The above table provides a good illustration of higher quality response with vector + GraphRAG approach vs. vector-only RAG approach.

  • Improved Data Understanding: Building a knowledge graph facilitates the development and maintenance of RAG applications, making the process more efficient.
  • Enhanced Explainability, Security and Privacy: Knowledge graphs provide a clearer picture of the reasoning logic behind LLM outputs, leading to better explainability and trust.

The above demonstrates how an implement in a knowledge graph enhance secruity and privacy with fine-grained access controls:

  • Scalability and Cost Efficiency: GraphRAG can achieve high-quality responses with fewer tokens compared to standard RAG, making it more scalable and cost-effective.

GraphRAG pipeline improves score to 86% vs other pipelines

Up Next: Building Your Knowledge Graph

In the next part of this blog series, we'll delve into the exciting world of knowledge graph creation. We'll explore various resources and tools available to help you build your own knowledge graph and unlock the power of GraphRAG for your AI applications.

Additional Resources:

The blogs was written based on my understanding on Neo4J and Knowledge Graph with references to following article. Images are adopted from neo4j.com.

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