Versioning and Schema Evolution: Managing Change

Data is not a static monolith; it is a river. Departments are renamed. Projects are merged. Taxonomies are updated. If your Graph RAG system relies on a rigid schema that can never change, it will be obsolete within six months.

In this final lesson of Module 5, we will learn how to design for Evolution. We will explore techniques like Soft Type Changes, Migration Edges, and Meta-Versioning. We will see how to update your graph's structure without breaking the existing "Logic Paths" that your AI agents depend on.

1. The Challenge: Breaking the AI's "Grammar"

When you change a Label from :Staff to :Employee, your AI agent—which was prompt-engineered to look for :Staff—will suddenly return zero results.

The Goal: Maintain backward compatibility for the "Neural Engine" (LLM) while upgrading the "Symbolic Engine" (The Database).

2. Strategy A: Multi-Labeling (The Soft Transition)

In a Property Graph, a node can have multiple labels.

Step 1: Current state: :Staff.
Step 2: Add secondary label: :Staff:Employee.
Step 3: Update AI prompts to use :Employee.
Step 4: (Months later) Remove the :Staff label.

This "Rolling Update" ensures that the AI never hits a "Schema Wall" where everything stops working.

3. Strategy B: The "Evolution" Edge

If you merge two project nodes into one, don't delete the old IDs! Create a specialized relationship that tells the graph engine where the knowledge went.

(:Project {id: 'Old_78'}) -[:EVOLVED_INTO]-> (:Project {id: 'New_99'})

AI Reasoning: If a user asks about an old project ID, the agent can follow the EVOLVED_INTO edge and say: "Project 78 has been merged into Project 99. Here is the current status..."

4. Metadata Versioning on Edges

Always add a schema_version or a created_at property to your edges.

[Sudeep] -[:LEADS {v: 1.2}]-> [Titan]

If you change the "Definitions" of what it means to "Lead" a project (e.g., from "Is a manager" to "Is a tech lead"), the version tag allows you to filter out old, legacy relationships during retrieval.

graph TD
    V1[Schema V1: :Staff] --> V2[Schema V2: :Staff:Employee]
    V2 --> V3[Schema V3: :Employee]
    
    subgraph "The Graceful Migration"
    V2
    end

5. Implementation: A Schema Version Checker in Python

Let's write a simple wrapper that warns your RAG pipeline if it's using an outdated entity type.

CURRENT_VERSION = 2.0

class GraphSchema:
    ALLOWED_LABELS = {
        "Employee": 2.0,
        "Staff": 1.0, # Deprecated
        "Consultant": 2.0
    }

    @classmethod
    def validate_query(cls, label):
        version = cls.ALLOWED_LABELS.get(label)
        if version is None:
            raise ValueError(f"Unknown label: {label}")
        if version < CURRENT_VERSION:
            print(f"WARNING: Label '{label}' is deprecated. Upgrade to use 'Employee'.")
        return True

# If your prompt generator tries to use 'Staff'
GraphSchema.validate_query("Staff")

6. Summary and Exercises

Schema change is a management task, not just a technical one.

Multi-labeling allows for zero-downtime structural updates.
Evolution edges preserve the "Lineage" of entities.
Version properties help filter out high-entropy legacy data.
Consistency between the Graph and the Prompt is the key to reliability.

Exercises

Migration Path: You are renaming the relationship [:LOVES] to [:LIKES]. Outline the 3 steps you would take to migrate your 1-million-edge graph without the AI agent noticing.
Legacy Knowledge: Why might you want to keep "Deprecated" nodes in your graph instead of deleting them? (Hint: Historical analysis questions like "What was our structure in 2019?").
The "Breaking" Query: Write a Cypher query that finds all nodes with the old label and "Adds" the new label to them.

Congratulations! You have completed Module 5: Designing the Knowledge Graph Layer. You have moved from a theorist to an architect.

In Module 6: Data Ingestion and Graph Construction, we will start building the "Factories" that populate your beautiful architecture with real-world data.