Temporal and Sequence Retrieval: The Time-Aware AI

Most RAG systems live in the "Eternal Now." They retrieve the most similar fact, regardless of whether it happened yesterday or five years ago. This is why AI often gets frustrated with outdated information. A Graph RAG system, however, can treat Time as a first-class dimension.

In this lesson, we will explore Temporal Retrieval Strategies. We will learn how to retrieve a "Sequence of Events" using NEXT relationships, how to filter graph walks based on Valid_From dates, and how to build a context that tells the AI: "X was true until Y happened, and now Z is the current state."

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1. The "Chain of Events" Pattern

In many specialized graphs (like logs, medical histories, or version control), nodes aren't just connected by "Subject"—they are connected by Time.

The Pattern: (Event_1) -[:NEXT]-> (Event_2) -[:NEXT]-> (Event_3)

When a user asks: "What happened after the server update?", the retrieval engine finds the Update node and simply follows the [:NEXT] edges forward in time. This provides the AI with a Chronological Narrative that is impossible to maintain with a simple vector search.

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2. Point-in-Time Traversal

Sometimes you want to answer: "What was Sudeep's role on June 1st, 2022?"

To solve this, your graph must have Temporal Properties on its edges.

• (Sudeep) -[:HELD_POSITION {from: 2020, to: 2023}]-> (Manager)

The Strategy: Before the graph walk begins, the "Time Filter" is applied to the Cypher query. The agent only "Sees" the edges that were valid at the specified time. This is called Graph Versioning.

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3. Dealing with "Current State" vs. "Legacy"

A common RAG failure is retrieving an old, debunked fact because it still has high semantic similarity.

Graph Solution: MATCH (p:Project {status: 'Active'})<-[:MENTIONS]-(doc:Document)

• By anchoring your search in the "Active" node, you automatically ignore the documents attached to the "Archived" or "Deprecated" nodes.

graph LR
    E1((Event 1: 09:00)) -->|NEXT| E2((Event 2: 10:00))
    E2 -->|NEXT| E3((Event 3: 11:00))
    
    subgraph "The Time Window"
    E2
    E3
    end
    
    style E2 fill:#34A853,color:#fff
    style E3 fill:#34A853,color:#fff

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4. Implementation: Chronological Fact Retrieval in Python

Let's look at how we retrieve a sequence of facts.

def get_chronology(entity_id, limit=5):
    # This query finds the starting event and walks the time-chain
    query = """
    MATCH (start:Event {id: $id})-[:NEXT*1..$limit]->(next_event)
    RETURN next_event.time as time, next_event.description as what
    ORDER BY next_event.time ASC
    """
    results = db.run(query, {"id": entity_id, "limit": limit})
    return "\n".join([f"At {r['time']}: {r['what']}" for r in results])

# RESULT:
# At 12:00: Started Database Migration
# At 12:05: Migration Failed
# At 12:06: Triggered Rollback

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5. Summary and Exercises

Temporal retrieval turns a "Pile of Facts" into a Timeline.

• [:NEXT] edges create an explicit sequence of events.
• Temporal Properties allow for "Time-Travel" queries (What was true then?).
• Status Anchors prevent the AI from retrieving stale, deprecated information.
• Chronological formatting builds the best narrative for incident reports and progress logs.

Exercises

1. Sequence Map: Think of a cooking recipe. How would you represent the "Order" of steps in a graph? Is it a sequence of NEXT edges or a hierarchy of PART_OF edges?
2. The "Stale" Test: If you have two nodes for a "Address," how does the AI know which one to tell the user? (Hint: Use an isActive: true property or compare updatedAt timestamps).
3. Prompt Design: Write a system prompt for a bot that receives 3 events and must determine if the "Sequence" reveals a specific problem (e.g., an unauthorized access).

In the next lesson, we will look at the global view: Community-Based Global Summarization.