Graph RAG: Empower Large Language Model with Structured Knowledge

How structured understanding of disparate data sources will address the deficiencies of Large Language Model deployments, and unlock its true value

Key Takeaways

Graph RAG represents a shift from text-centric retrieval to structure-aware understanding to enable:

• More consistent answers

• Better global context

• Reduced hallucination

• Stronger reasoning over relationships

Rather than asking LLMs to guess structure from text at generation time, Graph RAG makes structure explicit upfront.

Limitation of LLM Deployment

As large language models (LLMs) become widely adopted, Retrieval-Augmented Generation (RAG) has emerged as a de facto architecture for bringing proprietary knowledge into AI systems.

However, as real-world data increasingly involves people, relationships, causality, and networked structures, the limitations of traditional RAG begin to surface.

In this article, we share our journey implementing Graph RAG—covering system design, architectural differences, and practical insights from real-world deployment.

What Is Traditional RAG?

A typical RAG system consists of two main stages: Indexing and Query.

Indexing: Turning Documents into Searchable Vectors

The standard indexing pipeline looks like this:

• Ingest documents into the system

• Split them into smaller text segments (chunks)

• Convert each chunk into a vector using an embedding model

• Store those vectors in a vector database

  
  

The core goal of this stage is simple:

Transform human-readable text into a vector space where semantic similarity can be efficiently computed.

Query: Supporting Generation with Similarity Search

When a user asks a question:

• The query is converted into an embedding

• A similarity search is performed in the vector database

• The most relevant chunks are retrieved

• These chunks are assembled as context and passed to the LLM

• The LLM generates the final response

  
  

At its core, RAG helps reduce hallucination and extend LLM knowledge beyond what was seen during training by grounding generation in retrieved content.

When Data Is Inherently Relational: Why RAG Falls Short

In practice, we found that many datasets are not just long-form text. Instead, they naturally encode:

• Relationships between people

• Connections between entities

• Preferences, causality, supply chains, and interaction networks

For example:

• Iris likes Chocolate

• Melody likes Guava and Chocolate

When such information is flattened into text chunks, the semantic meaning remains, but the structure is lost. This is exactly where Graph RAG comes into play.

Graph RAG Indexing: Understand Structure Before Retrieval

Graph RAG does not replace traditional RAG. Instead, it adds a structured understanding layer during indexing.

Graph RAG Indexing Workflow:

• Documents and chunks still exist

• An LLM is used to extract:

  • Entities

  • Relationships

• Structured knowledge is stored in a graph database

• Embeddings are still preserved and stored in a vector database as a complementary signal

In short:

Graph RAG = Structured Knowledge Graphs + Vector-based Semantic Search

This hybrid approach allows the system to reason not just over text similarity, but over relationships. For example: the knowledge of "Melody and Iris share a common interest in chocolate." will be represented in Graph RAG in the structure below:

Graph RAG Query: From “Finding Text” to “Finding Relationships”

Graph RAG introduces an additional structure-aware step at query time.

Key steps in Graph RAG querying:

• User query → embedding

• Vector search identifies relevant entities (by name or ID)

• Related subgraphs are retrieved from the graph database

• The subgraph becomes a high-quality, relationship-aware context

• The LLM generates a response grounded in connected knowledge

As a result, the model is no longer answering only “what looks similar”, but instead “how things are connected.” This distinction becomes critical for complex, multi-hop, or relational questions.

Conclusion

The transition from RAG to Graph-RAG represents the "coming of age" for AI in the enterprise. For domains where knowledge is highly connected—people, organizations, products, workflows— the value of information lies in the connections between data points, and the old way of searching is no longer sufficient. Conventional RAG served us well as a first step, but it lacks the cognitive architecture to handle the nuance of business intelligence to support mission-critical decisions.

With Graph RAG, we are finally moving away from "stochastic parrots" that simply repeat text and moving toward systems that can truly reason through complex data. For the enterprise professionals, this means more than just efficiency—it means having a strategic partner that can illuminate the hidden patterns in your data, quantify your team's impact, and ultimately drive better outcomes.