Using GDS to Pre-Rank Knowledge for RAG

We have spent this module learning the math of graphs. Now, we bring it all together for the Final Retrieval Step. In a massive graph, your AI agent cannot wait for a PageRank simulation to run in real-time. You must Pre-Rank.

In this lesson, we will look at how to build a "Composite Importance Score." We will see how to combine PageRank (for authority), Community Membership (for context), and Timestamp (for freshness) into a single property stored on the node. We will learn how this pre-ranking allows your RAG system to achieve "O(1)" importance filtering during real-time user interaction.

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1. The Composite Score Formula

You want to give your AI the Best facts. "Best" is a mix of three things:

1. Authority (A): PageRank / Degree.
2. Breadth (B): Betweenness Centrality.
3. Freshness (F): 1 / (days_since_update).

The Logic: Final_Score = (w1 * A) + (w2 * B) + (w3 * F).

• If $w1$ is high, your bot is highly authoritative but might be out of date.
• If $w3$ is high, your bot is very current but might follow recent "Noise."

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2. Pre-Calculating the Ranking in the Pipeline

This ranking shouldn't happen when the user asks a question. It should happen as a Cron Job (e.g., every midnight).

1. Run GDS algorithms (PageRank, Louvain).
2. Calculate Composite Score for every node.
3. Save to Node Property: _rag_rank.
4. Create Index: Ensure there is an index on (:NodeLabel { _rag_rank }).

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3. Real-Time Deployment: The Ranked Expansion

Now, when your AI agent performs a neighborhood expansion:

MATCH (p:Person {name: 'Sudeep'})-[r]-(neighbor) RETURN neighbor.name, neighbor.description ORDER BY neighbor._rag_rank DESC LIMIT 10

The Outcome: In milliseconds, the database returns the 10 most "Mathematically Justified" facts about Sudeep. No complex logic is needed in Python. The "Intelligence" is baked into the data structure itself.

graph TD
    subgraph "The Nightly Build"
    L[Louvain] --> S[Scoring Engine]
    P[PageRank] --> S
    T[Timestamps] --> S
    S -->|SAVE| DB[(Knowledge Graph)]
    end
    
    subgraph "The User Query"
    U[Question] --> R[Retrieval]
    R -->|Sort by _rag_rank| A[LLM Prompt]
    end
    
    style S fill:#f4b400,color:#fff
    style DB fill:#4285F4,color:#fff

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4. Implementation: Updating the Rank Property in Cypher

// Update all Project nodes with a composite weight
MATCH (p:Project)
SET p._rag_rank = (p.pagerank_score * 0.7) + (p.betweenness_score * 0.3)

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5. Summary and Exercises

Pre-ranking turns a "Messy Graph" into an "Ordered Library."

• GDS Insights are static snapshots that must be updated periodically.
• Scoring Formulas allow you to tune the "Personality" of your AI.
• Indexes on scores ensure that ranking adds zero latency.
• Composite Ranking provides the most robust defense against "Information Overload."

Exercises

1. Formulating Rank: If you are building a "Medical Agent," would you give a higher weight ($w$) to PageRank (Scientific authority) or Freshness (Latest news)?
2. Performance Test: Run two queries: one with ORDER BY _rag_rank and one without. Is there a noticeable difference in a 10,000 node graph?
3. The "Ghost" Rank: What happens to the _rag_rank if a node's neighbors are deleted? Does the score stay the same until the next nightly build?

Congratulations! You have completed Module 11: Advanced Graph Data Science for RAG. You have added the "Mathematical Edge" to your system.

In Module 12: Evaluating Graph RAG Systems, we will look at how to prove that all this work actually resulted in a better AI.