Path-Based Retrieval Patterns: Connecting the Dots

If "Neighborhood Retrieval" (Lesson 1) is a 360-degree photo, Path-Based Retrieval is a 10-mile hiking map. This strategy is used whenever a user asks about the Relationship or Dependency between two things (e.g., "Is there a conflict of interest between Sudeep and Vendor X?").

In this lesson, we will explore the logic of the "Bridge." We will learn how to retrieve not just the direct link, but the entire "Influence Chain" between two nodes. We will look at Shortest Paths, All Paths, and the "Evidence String" format that makes these chains understandable to an AI agent.

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1. The Strategy: Depth over Breadth

In path-based retrieval, we don't care about everything Sudeep is doing. We only care about the specific set of steps that lead from Sudeep to the target entity.

The Workflow:

1. Identity Extract: Find the start and end nodes.
2. Breadth-Limited Search: Look for the shortest path between them (max 6 hops).
3. Expansion: Pull the neighbors of the bridge nodes to add context. (e.g., If the path goes through Project Alpha, pull the Project Alpha status too).

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2. Dealing with the "No Path" Null Result

In a graph, two entities might not be connected.

• User: "How is the CEO related to the office fire?"
• Graph: 0 results found.

The RAG Response: This is a valuable signal! You can tell the user: "I performed a 6-hop path analysis of all 10 million facts, and there is no recorded connection between these two entities." This is significantly more "Trustworthy" than a Vector RAG system saying "They both belong to the same company" (which it would say because of semantic overlap).

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3. Serialization: The "Evidence Chain" Format

When you feed a path to an LLM, you should use the Arrow Format.

• RAG Prompt: "Here is the evidence chain I found:"
• Content: (Sudeep) --[CONTROLS]--> (Dept 101) --[APPROVED]--> (Vendor X Contract)

This allows the LLM to structure its answer as: "Sudeep is related to Vendor X because he controls the department that approved their contract."

graph LR
    A[Start: Sudeep] --> B[Link 1: Dept 101]
    B --> C[Link 2: Project A]
    C --> D[End: Vendor X]
    
    subgraph "Path Evidence"
    A --- B --- C --- D
    end
    
    style A fill:#4285F4,color:#fff
    style D fill:#34A853,color:#fff

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4. Implementation: The Multi-Hop "Linker" in Cypher

Let's look at a query that finds the chain of connection.

MATCH (start:Person {id: 'Sudeep'}), (end:Company {id: 'Vendor-X'})
MATCH path = shortestPath((start)-[*1..5]-(end))
UNWIND relationships(path) as r
RETURN startNode(r).name + ' ' + type(r) + ' ' + endNode(r).name as evidence

This returns a list of strings that the AI can read like a story.

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5. Summary and Exercises

Path-based retrieval is the foundation of AI Investigative Logic.

• Depth is prioritized over breadth.
• Narrowing the context window to only relevant bridge-nodes saves tokens.
• Negative Results (No path) provide "Zero Hallucination" certainty.
• Arrow Serialization helps the LLM understand the "Flow" of influence.

Exercises

1. Causality Task: A server goes down. You have a graph of Server -> Service -> Database -> Power. How many hops is the path from Server to Power?
2. False Link: If a path goes from Sudeep through Global_Company to Jane, is that a "Strong" personal connection? (Hint: If the Bridge node has 1 million connections, the path is often meaningless).
3. Prompt Design: Write a prompt for an agent that receives a path of 5 hops and must explain why it "Should" or "Should Not" be a cause for concern.

In the next lesson, we will look at the "Fifth Dimension" of retrieval: Temporal and Sequence Retrieval.