feat(api): implement bulk inventory creation admin API endpoint #1159
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… data source route
…uerying For clarity of how they are prioritized in the loop
…ogging of sources
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Review by Korbit AI
Korbit automatically attempts to detect when you fix issues in new commits.
| Category | Issue | Fix Detected |
|---|---|---|
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Incorrect String Type Check ▹ view | |
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Incorrect Error Array Type Definition ▹ view | |
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Overly Restrictive Population Check ▹ view | |
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Missing Type Enforcement for Priority Range ▹ view | |
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Inefficient Data Type for Priority Field ▹ view | |
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Insufficient Locode Format Validation ▹ view | |
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Inefficient Request Body Handling ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| app/src/app/api/v0/city/[city]/user/[user]/route.ts | ✅ |
| app/src/app/api/v0/admin/bulk/route.ts | ✅ |
| app/src/backend/AdminService.ts | ✅ |
| app/src/app/api/v0/datasource/[inventoryId]/route.ts | ✅ |
| app/src/backend/DataSourceService.ts | ✅ |
| app/src/models/DataSourceI18n.ts | ✅ |
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| inventoryId: string, | ||
| cityLocode: string, | ||
| ): Promise<{ locode: string; error: string }[]> { | ||
| const errors: any[] = []; |
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Incorrect Error Array Type Definition 
Tell me more
What is the issue?
The errors array type in connectAllDataSources is defined as any[], but the function's return type and actual usage indicates it should be an array of objects with locode and error properties.
Why this matters
This type mismatch could lead to runtime errors if the wrong error structure is pushed to the array, as TypeScript's type checking is bypassed with 'any'.
Suggested change ∙ Feature Preview
Define a proper type for the errors array:
const errors: { locode: string; error: string }[] = [];
💬 Chat with Korbit by mentioning @korbit-ai.
app/src/backend/DataSourceService.ts
Outdated
| inventory.year!, | ||
| ) as PopulationAttributes; | ||
| // TODO allow country downscaling to work if there is no region population? | ||
| if (!cityPopulation || !countryPopulation || !regionPopulation) { |
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Overly Restrictive Population Check
Tell me more
What is the issue?
The code requires all population data (city, country, and region) to be present, but there's a TODO comment suggesting that country downscaling should work without region population.
Why this matters
This restrictive check prevents valid use cases where population-based downscaling could still work with just country data, reducing the functionality of the system unnecessarily.
Suggested change ∙ Feature Preview
Modify the check to allow country-based downscaling when region data is missing:
if (!cityPopulation || !countryPopulation) {
populationIssue = "missing-population";
} else {
countryPopulationScaleFactor = cityPopulation.population / countryPopulation.countryPopulation!;
if (regionPopulation) {
regionPopulationScaleFactor = cityPopulation.population / regionPopulation.regionPopulation!;
}
}💬 Chat with Korbit by mentioning @korbit-ai.
| import { z } from "zod"; | ||
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| const createBulkInventoriesRequest = z.object({ | ||
| cityLocodes: z.array(z.string()), // List of city locodes |
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Insufficient Locode Format Validation
Tell me more
What is the issue?
The cityLocodes validation accepts any string without validating if it's a proper locode format.
Why this matters
Invalid locode formats could cause downstream processing issues or data inconsistencies in the inventory creation process.
Suggested change ∙ Feature Preview
Add a regex pattern to validate locode format:
const LOCODE_PATTERN = /^[A-Z]{5}$/;
const createBulkInventoriesRequest = z.object({
cityLocodes: z.array(z.string().regex(LOCODE_PATTERN, "Invalid locode format")).min(1),
// ... rest of the schema
});💬 Chat with Korbit by mentioning @korbit-ai.
| import { NextResponse } from "next/server"; | ||
| import { z } from "zod"; | ||
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| const createBulkInventoriesRequest = z.object({ |
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Description by Korbit AI
What change is being made?
Implement a new API endpoint for bulk inventory creation aimed at admin users, incorporate priority handling for data sources, refactor DataSourceService to improve code reusability, and update the database schema to include a priority column in the DataSourceI18n model.
Why are these changes being made?
These changes enhance the system's functionality by allowing administrators to efficiently create multiple inventories at once, automatically determining the best data sources based on priority. The refactoring in DataSourceService improves code organization and maintainability, promoting future scalability, while the database schema update supports priority sorting for more precise data sourcing.