CI/CD for Knowledge Graphs: Data as Code

In traditional software, we have a CI/CD pipeline for our Code. In Graph RAG, we need a CI/CD pipeline for our Knowledge. If you update your "Extraction LLM" and it starts generating malformed nodes, you don't want that to hit your production graph. You need a way to test the "Quality" of the graph before it is promoted.

In this lesson, we will look at Graph DevOps. We will learn how to build a pipeline that includes Schema Validation, Integrity Testing, and the "Staging Graph" pattern. We will see how to treat your graph as "Infrastructure-as-Code," allowing you to roll back a "Bad Fact Ingestion" just as easily as you roll back a bad line of Python.

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1. The Knowledge Pipeline

A professional Graph CI/CD looks like this:

1. Stage 1: Pull Request: You update the schema.cypher file.
2. Stage 2: Validation: A GitHub Action boots up a temporary Neo4j Docker container. It runs your new schema and checks for syntax errors.
3. Stage 3: Integration Test: A small sample of raw data is ingested. A test script verifies that the "Direct Relationships" can still be found.
4. Stage 4: Deployment: If tests pass, the schema changes are applied to the Staging DB, and finally to Production.

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2. Integrity Tests in the Pipeline

Your CI/CD should answer these questions:

• "Does every :Person node still have an id?"
• "Did the update create any orphaned nodes (Degree 0)?"
• "Is the Vector Index still initialized and searchable?"

If any of these fail, the Build Fails. This prevents "Information Decay" from slowly poisoning your production AI.

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3. Blue/Green Graph Deployments

For massive graphs, you can't just "Update" the production DB while agents are querying it.

1. Blue: The current production graph (Read-only).
2. Green: A "Clone" of the production graph where you are running a massive re-ingestion or schema update.
3. Switch: Once the Green graph is ready and verified, you update your API to point to the new Green endpoint.

Benefit: Zero downtime and an instant "Emergency Undo" button.

graph LR
    subgraph "CI Pipeline"
    PR[Pull Request] --> V[Verify Schema]
    V --> T[Test Ingestion]
    end
    
    subgraph "Production"
    T --> B[(Blue DB: Live)]
    T --> G[(Green DB: Updating)]
    G -.->|Verified| B
    end
    
    style B fill:#34A853,color:#fff
    style G fill:#f4b400,color:#fff

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4. Implementation: A GitHub Actions Workflow Snippet

jobs:
  validate-graph:
    runs-on: ubuntu-latest
    services:
      neo4j:
        image: neo4j:latest
        ports:
          - 7687:7687
    steps:
      - name: Run Schema Migration
        run: |
          # Use Cypher Shell to apply your migrations
          cypher-shell -u neo4j -p password -f migrations/v2_schema.cypher
      - name: Verify Indexing
        run: |
          # Run a Python script to verify the graph is operational
          python tests/verify_graph_health.py

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5. Summary and Exercises

CI/CD turns the Knowledge Graph into a Stable Asset.

• Testing Schema as Code prevents breaking changes.
• Automated Verification ensures the "Connectivity" of your graph remains high.
• Blue/Green Deployments provide safety for large-scale updates.
• Integrity Checks act as the "Linter" for your data.

Exercises

1. Pipeline Failure: You update your schema to add a middle_name property. Your pipeline fails. What is the most likely reason? (Hint: Did you forget to update the Unique Constraints?).
2. Staging Choice: Why should you use a "Sample" of data for your CI tests instead of your entire 500GB production database?
3. Visualization: Draw a workflow showing how a developer's idea becomes a "Verified Relationship" in the production graph.

In the next lesson, we will look at the "Keys to the Castle": Security and Access Control in Graph RAG.