Knowledge Graphs and Structured Data: The GraphRAG Paradigm

Vector Search is "Fuzzy." It can tell you that a document about "Cats" is similar to a document about "Kittens." However, it is poor at answering questions about specific, many-to-many relationships. For example: "Who is the second-degree connection of Jane Doe that worked at Google and also knows Python?"

For these "Relationship-First" questions, we use Knowledge Graphs (KG). When we combine Knowledge Graphs with Gemini agents, we create GraphRAG—a system that can navigate complex connections with surgical precision. In this lesson, we will explore the architecture of Knowledge Graphs and learn how to help our agents query them natively.

1. What is a Knowledge Graph?

A Knowledge Graph represents data not as "chunks" of text, but as Entities (Nodes) and Relationships (Edges).

Nodes: (e.g., "Sudeep", "Gemini ADK", "Google").
Edges: (e.g., "Sudeep" CRESATED "Gemini ADK", "Gemini ADK" USES "Google Models").

Why Agents love Graphs:

Explicitness: Paths are defined. There is no guessing about which person is the manager of which department.
Multi-Hop Efficiency: An agent can navigate 5 "Edges" to find a connection that would be impossible to find via vector similarity.
Entity Resolution: A graph ensures that "Sundar" and "Sundar Pichai" are the same node, preventing the confusion that often plagues vector-based RAG.

2. The Text-to-Cypher Pattern

Most modern Knowledge Graphs (like Neo4j) use a query language called Cypher. Just as we teach agents to write SQL, we can teach them to write Cypher.

The Workflow:

The Goal: "Find all employees who report to Sarah and have a Python certificate."
The Generation: The agent generates a Cypher query: MATCH (e:Employee)-[:REPORTS_TO]->(m:Manager {name: 'Sarah'}) WHERE 'Python' IN e.skills RETURN e.name
The Execution: The ADK runs the query against the GraphDB.
The Answer: The agent interprets the list of names and responds to the user.

3. GraphRAG: Combining Vectors and Vertices

The most advanced Gemini ADK systems don't choose between Vector and Graph; they use both.

Vector Step: Search for "Documents that mention the new project."
Graph Step: Identify the "Entities" mentioned in those documents (e.g., people, dates, budget).
Synthesis: The agent uses the Graph to see how those people and dates are related to the broader company structure.

graph LR
    subgraph "Vector Space"
    A[Raw Docs] --> B[Embedded Chunks]
    end
    
    subgraph "Graph Space"
    C[Entity: Person] <-->|Works At| D[Entity: Company]
    C <-->|Author Of| B
    end
    
    E[Gemini Agent] -->|Queries| B
    E -->|Queries| C
    
    style E fill:#4285F4,color:#fff

4. Building a Knowledge Base for a Graph Agent

To build a KG-enabled agent, you need a Knowledge Extraction Pipeline.

Extract Entities: Use Gemini to read a document and identify all people, organizations, and technologies.
Identify Relationships: Ask Gemini: "How is Person X related to Company Y in this text?"
Ingest: Save these as Nodes and Edges in your GraphDB.

5. Implementation: The Cypher Generator Tool

Let's look at how we define a tool that allows an agent to query a Knowledge Graph.

import google.generativeai as genai

# A mock function simulating a GraphDB connection
def query_graph_db(cypher_query: str):
    """
    Executes a Cypher query against the corporate knowledge graph.
    Returns the nodes and relationships as a list.
    """
    print(f"EXECUTING GRAPH QUERY: {cypher_query}")
    # Simulator return
    return [{"name": "John Doe", "role": "Lead Developer"}]

model = genai.GenerativeModel('gemini-1.5-pro')

def graph_agent_session():
    # We give the agent the SCHEMA of our graph in the system prompt
    system_prompt = """
    You are a Graph Navigator. You have access to a Neo4j Knowledge Graph.
    Nodes: (:Person {name, role}), (:Skill {name})
    Edges: (Person)-[:HAS_SKILL]->(Skill), (Person)-[:REPORTS_TO]->(Person)
    """
    
    agent = genai.GenerativeModel(
        model_name='gemini-1.5-pro',
        system_instruction=system_prompt,
        tools=[query_graph_db]
    )
    
    chat = agent.start_chat(enable_automatic_function_calling=True)
    resp = chat.send_message("Who is in the engineering team and knows Python?")
    return resp.text

# graph_agent_session()

6. Challenges of Graph-Based Agency

Schema Complexity: If your graph has 500 different "Relationship Types," the agent might get confused. Solution: Use a "Sub-graph" strategy where you only show the agent the relevant parts of the schema for the current task.
Data Freshness: Graphs are expensive to update compared to vector indices.
Query Security: Just like SQL injection, an agent could generate a Cypher query that deletes the entire graph. Solution: Use a "Read-Only" user for the agent's database connection.

7. Use Case: Fraud Detection and Entity Resolution

Graphs are the industry standard for fraud detection.

Scenario: An agent is processing an insurance claim.
Task: "Check if this claimant's phone number is associated with any other previous suspicious claims."
Graph Logic: The agent traverses the (Claim)-[HASH_PHONE]->(Phone) relationships to look for "Collision" nodes that indicate a fraud ring.

8. Summary and Exercises

Knowledge Graphs provide the Logical Structure that vectors lack.

Entities and Relationships are the building blocks of the graph.
Text-to-Cypher allows agents to query the graph naturally.
GraphRAG combines semantic similarity with relational precision.
Security requires strict read-only boundaries.

Exercises

Schema Drafting: You are building a "Social Network Agent." Define the 3 Node types and 3 Edge types you would need to answer the question: "Who are the mutual friends of Alice and Bob?"
Logic Mapping: Why is it hard for a Vector Search to answer "How many people work in the HR department?" vs. why it is easy for a Knowledge Graph?
Query Practice: Write a Cypher query (or a natural language description of one) that finds a "Path" between a Developer and a CEO in a company graph.

In the next module, we leave the "Knowledge" behind and explore the "Operations" of our agents: Deployment and Scaling.