Outage Analyzer

Finds similar past outages and their causes.

Industry	Level	Difficulty	Recommended mode
Energy & utilities	2 of 5	Intermediate	flat

Coming soon

A complete, runnable Outage Analyzer reference implementation is in progress. The architecture and core code below already work with SrvDB 0.3.

The problem

• Outages repeat in patterns.
• Reports are free text.
• Restoration needs fast context.

How SrvDB powers Outage Analyzer

1. Outage reports are embedded.
2. New events retrieve similar past outages and fixes.
3. Clusters reveal recurring causes.
4. Updated after each event.

What changes

Today	With Outage Analyzer on SrvDB
Slow diagnosis	Similar past events
Hidden patterns	Clusters
Lost lessons	Recalled

How it works

1. Embed reports.
2. Query new events.
3. Review causes.
4. Restore.
5. Record outcomes.

SrvDB features it uses

• Exact search (flat): every query compares against every stored vector, so results are exact and explainable.
• True cosine scores: scores in [-1, 1] mean the same in every mode, so thresholds are stable.
• upsert: inserts new items and replaces changed ones, so the knowledge base stays current.
• Crash-safe persist(): after it returns, data survives a crash or power loss and reopens exactly as committed.

Use cases

• Storm outages
• Equipment failures
• Vegetation faults
• Protection trips
• Substation events
• Customer impact analysis

Core code

embed() is your embedding model (for example sentence-transformers, or a function that turns your records into feature vectors).

import json
import srvdb

db = srvdb.Database("./outage-analyzer-db", dimension=384)

def ingest(items):
    """items: list of (id, text, metadata dict)"""
    db.upsert(
        ids=[i for i, _, _ in items],
        embeddings=[embed(t) for _, t, _ in items],
        metadatas=[json.dumps(m) for _, _, m in items],
    )
    db.persist()                      # crash-safe checkpoint

def similar_outages(event, k=5):
    hits = db.search(embed(event), k)
    return [(json.loads(db.get(i)), round(score, 3)) for i, score in hits]

Limits and honest notes

• Operators decide actions.

Build roadmap

1. Prototype: load a sample of real data with flat mode and check that searches return what an expert expects.
2. Measure: if you move to an approximate mode, compare its results with flat on the same queries (recall).
3. Pipeline: add continuous upsert and decide when to call persist().
4. Harden: test restarts and crashes, add logging, review every output path with domain experts.
5. Deploy: ship the database directory with the application or service.

All Energy & utilities projects

Level	Project	What it does
1 · Beginner	Procedure Finder	Operators find the right procedure from a description.
2 · Intermediate	Outage Analyzer (this page)	Finds similar past outages and their causes.
3 · Advanced	Inspection Image Matcher	Matches drone and field inspection images to known defects.
4 · Expert	Field Operations Agent	An offline agent for crews with procedures, asset history and notes.
5 · Frontier	Grid Analog Forecaster	Finds historical analogues of current weather and load patterns.

See also: Use cases · Live examples (coming soon)