/* DBLingo — Manufacturing & IoT industry content pack. * Every level has proprietary exercise content specific to manufacturing workflows. */ registerDBLingoIndustry(createDBLingoIndustryPack({ id: 'manufacture', name: 'Manufacturing & IoT', shortName: 'Manufacturing', description: 'MongoDB connects equipment telemetry, asset documents, parts catalogs, work orders, and AI maintenance copilots in one flexible operational model.', promise: 'MongoDB connects equipment telemetry, asset documents, parts catalogs, work orders, and AI maintenance copilots in one flexible operational model.', searchPhrase: 'bearing temperature anomaly', nouns: { profile: 'asset', profilePlural: 'assets', profileCollection: 'assets', item: 'part', itemPlural: 'parts', itemCollection: 'parts', catalog: 'parts catalog', user: 'plant operator', transaction: 'sensor event', transactionPlural: 'sensor events', documentPlural: 'maintenance notes' }, stream: { eventCollection: 'sensorEvents', alertCollection: 'maintenanceAlerts', eventPlural: 'sensor events', criticalLabel: 'equipment-risk anomalies', scoreField: 'anomalyScore', threshold: 88, alertType: 'predictive_maintenance', sourceDb: 'factory', outcome: 'maintenance queues and plant-floor dashboards' }, aha: [ { title: 'AHA: Equipment context belongs with telemetry', message: 'When sensor readings live alongside asset metadata, teams diagnose problems faster without joining across systems.' }, { title: 'AHA: Flexible schemas handle every machine model', message: 'Different equipment types have different attributes. MongoDB documents adapt without schema migrations.' }, { title: 'AHA: Aggregation turns raw sensor events into uptime insight', message: 'Pipeline stages filter, bucket, and rank telemetry in-database — no ETL export needed.' }, { title: 'AHA: Atlas Search finds maintenance knowledge fast', message: 'Full-text search over maintenance logs lets technicians find past fixes in seconds.' } ], levels: { d1: { title: 'Build an equipment asset document', kind: 'shape', prompt: 'Drag the right values into the asset record for a CNC milling machine.', sub: 'Asset documents combine scalar values with maintenance arrays and embedded location context.', why: 'Equipment assets in manufacturing vary widely — pumps have flow rates, CNC machines have spindle specs, and each has its own maintenance history and location. MongoDB documents keep that context together without forcing a universal table.', skeleton: [ { key: '_id', type: 'oid', value: 'ObjectId("73d...")' }, { key: 'serial', type: 'slot', slot: 'serial' }, { key: 'spindleRpm', type: 'slot', slot: 'rpm' }, { key: 'maintenanceTags', type: 'slot', slot: 'tags' }, { key: 'location', type: 'slot', slot: 'location' } ], bank: [ { id: 'serial', label: '"CNC-4401"', kind: 'value' }, { id: 'rpm', label: '12000', kind: 'value' }, { id: 'tags', label: '["calibrated", "preventive-maintenance"]', kind: 'value' }, { id: 'location', label: '{ plant: "Plant-B", bay: 3 }', kind: 'value' }, { id: 'd1', label: 'CNC-4401', kind: 'value' }, { id: 'd2', label: '"12000"', kind: 'value' }, { id: 'd3', label: '{ plant: Plant-B, bay: 3 }', kind: 'value' } ], answer: { serial: 'serial', rpm: 'rpm', tags: 'tags', location: 'location' } }, d2: { title: 'insertOne() — log a sensor reading', kind: 'blocks', prompt: 'Record a temperature sensor reading into the sensorEvents collection.', sub: 'Each sensor reading becomes an immutable document for time-series analysis.', why: 'IoT sensor data arrives at massive scale. `insertOne()` captures each reading with full context (asset, metric, timestamp) for real-time anomaly detection and predictive maintenance.', snippet: [ 'db.', { slot: 'col' }, '.', { slot: 'op' }, '({', '\n assetId: "CNC-4401",', '\n metric: "temperature",', '\n value: ', { slot: 'val' }, ',', '\n unit: ', { slot: 'unit' }, '\n})' ], bank: [ { id: 'op', label: 'insertOne', kind: 'op', answer: 'op' }, { id: 'col', label: 'sensorEvents', kind: 'field', answer: 'col' }, { id: 'val', label: '78.4', kind: 'value', answer: 'val' }, { id: 'unit', label: '"celsius"', kind: 'value', answer: 'unit' }, { id: 'x1', label: 'addOne', kind: 'op' }, { id: 'x2', label: 'telemetry', kind: 'field' }, { id: 'x3', label: '"78.4"', kind: 'value' } ] }, d3: { title: 'updateOne() — mark asset for maintenance', kind: 'fill', prompt: 'Set asset CNC-4401 status to "maintenance_required".', sub: '$set patches the maintenance status without replacing calibration and history data.', why: 'Flagging equipment for maintenance must preserve all operational data (calibration, history, specs) while updating only the status field for the maintenance workflow.', snippet: [ 'db.assets.updateOne(', '\n { serial: "CNC-4401" },', '\n { ', { blank: 'op' }, ': { ', { blank: 'field' }, ': ', { blank: 'val' }, ' } }', '\n)' ], choices: { op: { options: ['$set', '$push', '$inc', '$replace'], answer: '$set' }, field: { options: ['status', 'state', 'maintenance', 'flag'], answer: 'status' }, val: { options: ['"maintenance_required"', 'maintenance_required', 'true', '"offline"'], answer: '"maintenance_required"' } } }, d4: { title: 'deleteOne() — purge old work order', kind: 'fill', prompt: 'Remove the oldest completed work order from the workOrders collection.', sub: 'deleteOne() removes one document — sort targets the oldest completed order.', why: 'Completed work orders accumulate and waste storage. Sort by completedAt ascending to archive the oldest, ensuring active or in-progress orders are never accidentally deleted.', snippet: [ 'db.workOrders.', { blank: 'op' }, '(', '\n { status: ', { blank: 'val' }, ' },', '\n { sort: { completedAt: ', { blank: 'dir' }, ' } }', '\n)' ], choices: { op: { options: ['deleteOne', 'removeOne', 'dropOne'], answer: 'deleteOne' }, val: { options: ['"completed"', 'completed', '"archived"'], answer: '"completed"' }, dir: { options: ['1', '-1', '"asc"'], answer: '1' } } }, q1: { title: 'Find assets by type', kind: 'blocks', prompt: 'Find every asset of type "milling" for the CNC maintenance schedule.', sub: 'Equality queries on equipment type enable type-based maintenance planning.', why: 'Maintenance schedules group by equipment type. An index on `type` returns all milling machines instantly for coordinated service windows.', snippet: [ 'db.assets.find({ ', { slot: 'field' }, ': ', { slot: 'val' }, ' })' ], bank: [ { id: 'field', label: 'type', kind: 'field', answer: 'field' }, { id: 'val', label: '"milling"', kind: 'value', answer: 'val' }, { id: 'x1', label: 'category', kind: 'field' }, { id: 'x2', label: 'milling', kind: 'value' }, { id: 'x3', label: 'machineType', kind: 'field' } ] }, q2: { title: '$gt — high-temperature alerts', kind: 'blocks', prompt: 'Find sensor events where temperature exceeded 95°C for anomaly review.', sub: '$match with $gt filters readings above the safe operating threshold.', why: 'Equipment protection requires immediate flagging of threshold breaches. $match with $gt on an indexed `value` field surfaces dangerous readings instantly for the control room.', snippet: [ 'db.sensorEvents.aggregate([\n { ', { slot: 'stage' }, ': { value: { ', { slot: 'op' }, ': ', { slot: 'val' }, ' } } }\n])' ], bank: [ { id: 'stage', label: '$match', kind: 'stage', answer: 'stage' }, { id: 'op', label: '$gt', kind: 'op', answer: 'op' }, { id: 'val', label: '95', kind: 'value', answer: 'val' }, { id: 'x1', label: '$filter', kind: 'stage' }, { id: 'x2', label: '$gte', kind: 'op' }, { id: 'x3', label: '$lt', kind: 'op' }, { id: 'x4', label: '"95"', kind: 'value' } ] }, q3: { title: '$in — multi-status asset query', kind: 'fill', prompt: 'Find assets in "maintenance_required" or "offline" status.', sub: '$in matches any status — perfect for maintenance planning dashboards.', why: 'Plant managers need to see all non-operational equipment at once. `$in` queries multiple statuses efficiently for the maintenance planning board.', snippet: [ 'db.assets.find({\n status: { ', { blank: 'op' }, ': ', { blank: 'arr' }, ' }\n})' ], choices: { op: { options: ['$in', '$any', '$or', '$contains'], answer: '$in' }, arr: { options: ['["maintenance_required", "offline"]', '"maintenance_required, offline"', '{ maintenance_required, offline }', '[maintenance_required, offline]'], answer: '["maintenance_required", "offline"]' } } }, q4: { title: 'Compound filter — type + temperature', kind: 'fill', prompt: 'Find operational milling machines with temperature readings above 80°C.', sub: 'Combine equality and range for targeted monitoring.', why: 'Monitoring specific equipment types at elevated temperatures prevents failures. MongoDB ANDs type, status, and temperature range implicitly.', snippet: [ 'db.sensorEvents.find({\n assetType: "milling",\n metric: "temperature",\n value: { ', { blank: 'op' }, ': 80 }\n})' ], choices: { op: { options: ['$gt', '$gte', '$eq', '>='], answer: '$gt' } } }, a1: { collection: 'maintenanceEvents', // shown in the solved-query preview title: 'Downtime hours by asset', kind: 'reorder', prompt: 'Compute total downtime hours per asset in 2024 — re-order the stages.', sub: 'Filter to 2024 downtime events, group by asset, sort by hours, limit to worst.', why: 'Identifying worst-performing assets drives capital investment decisions. Filter to downtime events, group by assetId to sum hours, sort descending, and flag the worst offenders.', stages: [ { id: 'm', code: '$match: { type: "downtime", timestamp: { $gte: ISODate("2024-01-01") } }', sub: 'filter to 2024 downtime', correct: 0 }, { id: 'g', code: '$group: { _id: "$assetId", downtimeHours: { $sum: "$duration" } }', sub: 'sum hours per asset', correct: 1 }, { id: 's', code: '$sort: { downtimeHours: -1 }', sub: 'worst performers first', correct: 2 }, { id: 'l', code: '$limit: 10', sub: 'top 10 problem assets', correct: 3 } ], initial: ['l', 's', 'm', 'g'] }, a2: { title: '$project — maintenance report', kind: 'fill', prompt: 'Return just the asset serial and last maintenance date — hide _id.', sub: '$project shapes output for the maintenance compliance report.', why: 'Compliance reports need exact field shapes. `$project` with `_id: 0` delivers only serial and lastMaintenance for the regulatory submission.', snippet: [ 'db.assets.aggregate([\n { $project: {\n _id: ', { blank: 'id' }, ',\n serial: ', { blank: 'one' }, ',\n lastMaintenance: ', { blank: 'one2' }, '\n } }\n])' ], choices: { id: { options: ['0', '1', 'false', 'null'], answer: '0' }, one: { options: ['1', '0', 'true', '"yes"'], answer: '1' }, one2: { options: ['1', '0', '"$lastMaintenance"', 'yes'], answer: '1' } } }, a3: { collection: 'sensorEvents', // shown in the solved-query preview title: '$lookup — enrich events with asset context', kind: 'reorder', prompt: 'Attach asset location and type to each sensor event for spatial analysis.', sub: '$lookup joins asset data, $unwind flattens, $project picks analysis fields.', why: 'Sensor analysis needs equipment context. `$lookup` brings asset location and type in, enabling spatial and type-based anomaly correlation without denormalization.', stages: [ { id: 'lo', code: '$lookup: { from: "assets", localField: "assetId", foreignField: "_id", as: "asset" }', sub: 'join asset record', correct: 0 }, { id: 'un', code: '$unwind: "$asset"', sub: 'flatten to single doc', correct: 1 }, { id: 'pr', code: '$project: { _id: 0, metric: 1, value: 1, location: "$asset.location", assetType: "$asset.type" }', sub: 'keep reading + context', correct: 2 } ], initial: ['pr', 'lo', 'un'] }, a4: { title: '$sort + $limit — latest readings', kind: 'blocks', prompt: 'Show the 5 most recent sensor readings.', sub: 'Sort by timestamp descending, limit to 5 for the real-time dashboard.', why: 'Control room dashboards need the freshest sensor data. `$sort` + `$limit` streams only the top-N readings without sorting the entire telemetry log.', snippet: [ 'db.sensorEvents.aggregate([\n { ', { slot: 'sort' }, ': { timestamp: ', { slot: 'dir' }, ' } },\n { ', { slot: 'limit' }, ': ', { slot: 'n' }, ' }\n])' ], bank: [ { id: 'sort', label: '$sort', kind: 'stage', answer: 'sort' }, { id: 'limit', label: '$limit', kind: 'stage', answer: 'limit' }, { id: 'dir', label: '-1', kind: 'value', answer: 'dir' }, { id: 'n', label: '5', kind: 'value', answer: 'n' }, { id: 'x1', label: '$top', kind: 'stage' }, { id: 'x2', label: '$first', kind: 'stage' }, { id: 'x3', label: '1', kind: 'value' }, { id: 'x4', label: '"5"', kind: 'value' } ] }, i1: { title: 'Index strategies for sensor data', kind: 'index', prompt: 'The `sensorEvents` collection has four query patterns. Pick the best index for each.', sub: 'Match each IoT access pattern to the right index type.', why: 'IoT queries span asset lookups, asset+time combos, full-text maintenance log searches, and low-cardinality flags. Each needs the right index at telemetry scale.', collection: 'sensorEvents', fields: [ { name: 'assetId', type: 'ObjectId', need: 'single', used: 'db.sensorEvents.find({ assetId })' }, { name: 'assetId + timestamp', type: 'ObjectId + Date', need: 'compound', used: 'db.sensorEvents.find({ assetId }).sort({ timestamp: -1 })' }, { name: 'notes', type: 'String', need: 'search', used: 'Atlas Search over maintenance notes for knowledge retrieval' }, { name: 'isProcessed', type: 'Bool', need: 'none', used: 'low cardinality — only true/false' } ], bank: [ { id: 'single', label: 'Single-field', kind: 'index' }, { id: 'compound', label: 'Compound', kind: 'index' }, { id: 'search', label: 'Atlas Search', kind: 'index' }, { id: 'none', label: 'No index', kind: 'index' } ] }, i2: { title: 'ESR for telemetry queries', kind: 'reorder', prompt: 'Order this compound index for: find temperature readings above threshold, sorted by time.', sub: 'For: db.sensorEvents.find({ metric: "temperature", value: { $gt: 80 } }).sort({ timestamp: -1 })', why: 'ESR in IoT: equality (metric) narrows to temperature readings, sort (timestamp) delivers chronological order, range (value) scans only above-threshold readings.', stages: [ { id: 'e', code: 'metric: 1', sub: 'filter: { metric: "temperature" }', correct: 0 }, { id: 's', code: 'timestamp: -1', sub: '.sort({ timestamp: -1 })', correct: 1 }, { id: 'r', code: 'value: 1', sub: 'filter: { value: { $gt: 80 } }', correct: 2 } ], initial: ['r', 's', 'e'] }, i3: { title: 'TTL — expire raw telemetry', kind: 'fill', prompt: 'Auto-delete raw sensor events 30 days after ingestion.', sub: 'TTL indexes enforce data lifecycle — keep aggregates, expire raw readings.', why: 'Raw telemetry generates massive volume. A TTL index on `ingestedAt` auto-removes raw readings after 30 days (2592000 seconds), while pre-aggregated summaries persist for long-term analysis.', snippet: [ 'db.sensorEvents.', { blank: 'op' }, '(\n { ingestedAt: 1 },\n { ', { blank: 'key' }, ': ', { blank: 'sec' }, ' }\n)' ], choices: { op: { options: ['createIndex', 'addIndex', 'index', 'ensureIndex'], answer: 'createIndex' }, key: { options: ['expireAfterSeconds', 'ttl', 'expires', 'maxAge'], answer: 'expireAfterSeconds' }, sec: { options: ['2592000', '30', '720', '86400'], answer: '2592000' } } }, i4: { title: 'Covered query — asset status check', kind: 'fill', prompt: 'Query only fields in the index { serial: 1, status: 1 } for zero-fetch lookups.', sub: 'Covered queries return from the index — critical for real-time SCADA integrations.', why: 'SCADA systems poll asset status constantly. A covered query on { serial, status } returns from the index alone — zero document fetches at industrial polling rates.', snippet: [ 'db.assets.find(\n { serial: ', { blank: 'val' }, ' },\n { _id: ', { blank: 'id' }, ', serial: 1, status: 1 }\n).', { blank: 'verb' }, '("executionStats")' ], choices: { val: { options: ['"CNC-4401"', 'CNC-4401', '*', '{}'], answer: '"CNC-4401"' }, id: { options: ['0', '1', 'null', 'true'], answer: '0' }, verb: { options: ['explain', 'plan', 'analyze', 'trace'], answer: 'explain' } } }, v1: { title: '$vectorSearch — similar failure patterns', kind: 'fill', prompt: 'Find the 5 sensor events most similar to a known failure signature.', sub: 'Atlas Vector Search identifies equipment degradation using telemetry embeddings.', why: 'Predictive maintenance uses sensor embeddings to capture failure "signatures". Vector Search finds events with similar degradation patterns — catching problems before they become breakdowns.', snippet: [ 'db.sensorEvents.aggregate([{\n $vectorSearch: {\n index: "telemetry_embed_idx",\n path: ', { blank: 'path' }, ',\n queryVector: failureSignature,\n numCandidates: 200,\n ', { blank: 'limit' }, ': 5\n }\n}])' ], choices: { path: { options: ['"embedding"', '"vector"', 'embedding', '"signature"'], answer: '"embedding"' }, limit: { options: ['limit', 'numResults', 'topK', 'k'], answer: 'limit' } } }, v2: { title: '$search — maintenance knowledge', kind: 'blocks', prompt: 'Use Atlas Search to find maintenance notes about "bearing temperature anomaly".', sub: 'Atlas Search helps technicians find past fixes from maintenance logs.', why: 'Technicians need past repair knowledge during active issues. Atlas Search over maintenance notes finds relevant procedures and past fixes in seconds — reducing mean time to repair.', snippet: [ 'db.maintenanceLogs.aggregate([\n { ', { slot: 'stage' }, ': {\n ', { slot: 'operator' }, ': {\n path: ', { slot: 'path' }, ',\n query: ', { slot: 'query' }, '\n }\n } }\n])' ], bank: [ { id: 'stage', label: '$search', kind: 'stage', answer: 'stage' }, { id: 'operator', label: 'text', kind: 'op', answer: 'operator' }, { id: 'path', label: '"notes"', kind: 'field', answer: 'path' }, { id: 'query', label: '"bearing temperature anomaly"', kind: 'value', answer: 'query' }, { id: 'x1', label: '$text', kind: 'op' }, { id: 'x2', label: 'find', kind: 'op' }, { id: 'x3', label: 'bearing temperature anomaly', kind: 'value' } ] }, v3: { title: 'Atlas Trigger — predictive alert', kind: 'fill', prompt: 'Fire a maintenance alert whenever a critical sensor threshold is breached.', sub: 'Database triggers react to dangerous readings and alert maintenance teams.', why: 'Equipment protection requires immediate response to threshold breaches. Atlas Triggers fire on insert and can alert maintenance within seconds — preventing cascading failures.', snippet: [ '// Atlas Trigger config\n{\n type: "DATABASE",\n database: "plant",\n collection: ', { blank: 'col' }, ',\n operationTypes: [', { blank: 'evt' }, '],\n function: ', { blank: 'fn' }, '\n}' ], choices: { col: { options: ['"sensorEvents"', 'sensorEvents', 'SensorEvents', '*'], answer: '"sensorEvents"' }, evt: { options: ['"insert"', '"create"', '"write"', '"new"'], answer: '"insert"' }, fn: { options: ['"alertMaintenance"', 'alertMaintenance', 'fn()', 'alert()'], answer: '"alertMaintenance"' } } }, v4: { collection: 'maintenanceDocs', // shown in the solved-query preview title: 'RAG — maintenance copilot', kind: 'reorder', prompt: 'Build a RAG pipeline for a maintenance copilot: scope to plant, retrieve semantically, trim for the LLM.', sub: 'Vector search scoped to plant → drop weak matches → project for the LLM.', why: 'Maintenance copilots must only access the plant\'s own procedures. Pre-filtering by plantId ensures safety-critical isolation, and trimming keeps the LLM focused on relevant repair steps.', stages: [ { id: 'vs', code: '$vectorSearch: { index: "maint_embed", path: "embedding", queryVector: q, limit: 8, filter: { plantId } }', sub: 'k-NN scoped to this plant', correct: 0 }, { id: 'pj', code: '$project: { _id: 0, procedure: 1, equipment: 1, score: { $meta: "vectorSearchScore" } }', sub: 'trim for the LLM', correct: 2 }, { id: 'ms', code: '$match: { score: { $gt: 0.80 } }', sub: 'drop weak matches', correct: 1 }, ], initial: ['pj', 'ms', 'vs'] } } }));