Incident Response: When the Vectors Go Rogue

What do you do when your AI starts giving completely wrong answers? Or when your Vector DB latency jumps from 20ms to 2s? In the world of Vector Databases, incidents are often "Silent but Viral"—the system is technically "Up," but it is delivering poisoned data.

In this final lesson, we learn the Incident Response playbook for vector systems.

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1. Classifying the Crisis

1. Category A: Infrastructure Down: Memory limit reached, CPU pegged at 100%, or 503 errors.
   • Fix: Scale up pods/nodes immediately.
2. Category B: Query Corruption: Search works, but results are garbage.
   • Fix: Identify the "Poisoned" documents and delete them; check for model mismatch.
3. Category C: Retrieval Storm: A recursive agent loop (Module 9.1) is hammering the DB with 1,000 queries per second.
   • Fix: Revoke the agent's API key and implement rate-limiting.

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2. The "Kill Switch" Architecture

You must have the ability to "Turn off the AI" without turning off the whole website.

• Implementation: A simple boolean flip in your configuration (AI_ENABLED = False) that causes the app to fallback to standard Keyword Search or a static "Under Maintenance" message.

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3. Post-Mortem: Finding the Root Cause

When a vector search goes wrong, ask these 3 questions:

1. Was the Query Vector correct? (Did the embedding service fail or return zeros?)
2. Was the Metadata correctly Filtered? (Did we accidentally allow cross-tenant leakage?)
3. Is the Index fragmented? (Does the HNSW index need an "Optimization" or "Compaction" pass?)

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4. Implementation: Emergency Purge (Python)

If a malicious document has been indexed and is "High-jacking" search results, you need a way to nuke it by its metadata.

# Emergency script to remove all documents from a specific source
def emergency_purge(source_name):
    index.delete(
        filter={"source": {"$eq": source_name}},
        namespace="main-data"
    )
    print(f"Purged all vectors from {source_name}")

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5. Summary and Key Takeaways

1. Monitoring is the First Responder: You can't fix what you haven't seen.
2. Aliases are for Fast Recovery: Use the current_docs alias to switch to a known-safe backup index in seconds.
3. Traceability: Every query in your log (Module 16.5) should be traceable back to a specific user and a specific prompt.
4. The Runbook: Keep a document with "Search latency is high" instructions for your on-call engineers.

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Exercise: The On-Call Simulation

1. Scenario: You receive an alert at 2 AM. Your Pinecone index is responding with 429: Too Many Requests.
2. The Cause: A new "Autonomous Agent" feature was released yesterday, and it has entered an infinite retrieval loop.
3. The Task: Outline the 4 steps you would take to restore service without deleting the data.

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Congratulations on completing Module 17! You are now a production-ready Vector Engineer.