Core Components of a Graph RAG System: The Anatomy

A Graph RAG system is more than just a piece of code; it is a complex "Information Pipeline." To build one that works at scale, you need to understand the individual "Internal Organs" and how they communicate.

In this lesson, we will deconstruct the anatomy of a production-grade Graph RAG engine. We will look at the Ingestion Pipeline (The Gut), the Knowledge Graph Store (The Heart), the Traversal Engine (The Nervous System), and the Response Synthesizer (The Voice). By the end, you'll see how these pieces come together to form a living, breathing intelligent agent.

1. The Ingestion Pipeline (Entity & Relationship Extraction)

The pipeline's job is to take raw, messy data (PDFs, Logs, Text) and turn it into Nodes and Edges.

The Steps:

Parsing: Extracting clean text from binary files (Like using AWS Textract or LangChain's PDF loader).
NER (Named Entity Recognition): Identifying people, places, and things using LLMs or specialized models.
Relation Extraction: Identifying the "Verb" that connects two nodes.
Schema Alignment: Ensuring your "New" data fits your "Existing" graph (e.g., Do we use LEADER_OF or HEAD_OF as the relationship label?).

2. The Storage Layer (The Knowledge Graph Database)

This is where the structure lives. Unlike a vector database that stores blobs of floating-point numbers, a graph database stores Connections.

Property Graphs: Databases like Neo4j or Amazon Neptune that allow you to store properties (Key-Value pairs) on both nodes and edges.
Indices: Fast lookups so the system can find the node "Google" in the ocean of 1 trillion nodes instantly.

3. The Traversal & Retrieval Engine

This is the "Brain" of the retrieval process. It decides which paths to follow once a starting node is found.

Entity Linking: The crucial first step. If the user says "The G-Company," the system must map that to the node Alphabet Inc.
Expansion Logic: Does the system follow every edge? No. It uses "Weighted Traversal" to follow only the most relevant relationships (e.g., WORKS_AT is usually more important than LIKES_COFFEE).
Path Scoring: Ranking the retrieved paths based on their relevance to the user's specific query.

4. The Context Assembler & Synthesizer (The Prompt Layer)

Once the raw graph data is retrieved (often in a complex format like a Cypher result list), it must be translated for the LLM.

Serialization: Converting (Sudeep)-[:LEADS]->(Engineering) into "Sudeep leads the Engineering department."
Pruning: Removing facts that don't fit the context window.
Synthesis: The LLM reads the refined context and answers the user.

graph TD
    subgraph "Ingestion (Offline)"
    DP[Raw Data] --> EX[Extraction LLM]
    EX --> KG[(Knowledge Graph)]
    end
    
    subgraph "Retrieval (Online)"
    Q[User Question] --> EL[Entity Linker]
    EL --> TR[Traversal Engine]
    TR -->|Fetch Subgraph| KG
    end
    
    subgraph "Generation (Online)"
    TR --> CA[Context Assembler]
    CA --> LLM[Response LLM]
    LLM --> Ans[Final Answer]
    end
    
    style KG fill:#4285F4,color:#fff
    style LLM fill:#34A853,color:#fff

5. The "Evaluator" Component (Quality Control)

In production, you need a component that checks the graph's work.

Faithfulness Check: Did the LLM actually use the retrieved graph facts?
Relationship Audit: Are the extracted edges accurate? (e.g., Did it hallucinate that Sudeep is the CEO of Meta?)

6. Implementation: Mocking a Component Flow in Python

Let's see how these components hand off the data in a simplified script.

# 1. THE INGESTION ENGINE
def ingest(text):
    # Simulated extraction
    return [("Project A", "OWNED_BY", "Jane")]

# 2. THE RETRIEVAL ENGINE (Traversal)
def retrieve(query, graph):
    entities = ["Project A"] # Simulated link
    subgraph = []
    for e in entities:
        # Find all relationships for this entity
        rels = [r for r in graph if r[0] == e]
        subgraph.extend(rels)
    return subgraph

# 3. THE CONTEXT ASSEMBLER
def assemble(subgraph):
    return " . ".join([f"{s} {p} {o}" for s, p, o in subgraph])

# EXECUTION
my_graph = [("Project A", "OWNED_BY", "Jane"), ("Jane", "WORKS_AT", "Google")]
context = assemble(retrieve("Who owns Project A?", my_graph))
print(f"Serialized Context: {context}")

7. Summary and Exercises

A Graph RAG system is a multi-stage machine:

Ingestion creates the nodes/edges.
Storage maintains the structural integrity.
Retrieval navigates the web.
Synthesis turns connections into conversation.

Exercises

Pipeline Mapping: Which component do you think is responsible for "Entity Resolution" (mapping two different names for the same company)? Is it part of Ingestion or Retrieval?
Anatomy Trace: If an agent says "I found the answer in Document X," but the Knowledge Graph doesn't store the "Document ID" on the edge, how would the agent know where it got the info? This shows why Metadata on Edges is critical.
Component Failure: If the "Entity Linker" fails to find the starting node, can the "Traversal Engine" do anything? This is the "Cascade Failure" problem in Graph RAG.

In the next lesson, we will see how this anatomy compares to other "RAG cousins": Graph RAG vs Vector, Hybrid, and Agentic RAG.