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Add offline merchant category suggestions

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Vijayakanth Manoharan
2026-03-23 13:28:00 -04:00
parent 12c72ddcad
commit 696d393fca
11 changed files with 352 additions and 31 deletions
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23
openspec/changes/monthly-expense-tracker-v1/specs/category-suggestion/spec.md Normal file
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## ADDED Requirements
### Requirement: System suggests categories from merchant names
The system SHALL support merchant-name-based category suggestion for expense entry while keeping all suggestion logic fully offline.
#### Scenario: Known merchant resolves from deterministic rules
- **WHEN** the user enters a merchant or shop name that matches a known merchant rule
- **THEN** the system assigns the mapped category without needing model inference
#### Scenario: Unknown merchant falls back to local model
- **WHEN** the user enters a merchant or shop name that does not match a known merchant rule
- **THEN** the system asks the local AI service for a category suggestion and returns the suggested category
### Requirement: Ambiguous suggestions remain user-controlled
The system SHALL keep the final saved category under user control for ambiguous or model-generated suggestions.
#### Scenario: User confirms model suggestion before save
- **WHEN** the category suggestion comes from model inference instead of a deterministic rule
- **THEN** the user can review and confirm or change the category before the expense is saved
#### Scenario: No cloud fallback is used
- **WHEN** the local suggestion service is unavailable
- **THEN** the system continues to allow manual category selection and does not send merchant data to a hosted provider

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openspec/changes/monthly-expense-tracker-v1/specs/monthly-insights/spec.md
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## ADDED Requirements
### Requirement: User can generate monthly AI insights on demand
The system SHALL allow the user to manually generate AI insights for any month with existing or sparse data by sending structured monthly context to the configured `OpenAI` provider.
The system SHALL allow the user to manually generate monthly AI insights for any month with existing or sparse data by sending structured monthly context to a fully offline local inference runtime.
#### Scenario: Insights are generated for a month with data
- **WHEN** the user requests insight generation for a month with recorded activity
- **THEN** the system sends monthly aggregates plus transaction samples to the AI service and returns a rendered narrative summary with structured supporting totals
- **THEN** the system sends monthly aggregates plus transaction samples to the local AI service and returns a rendered narrative summary with structured supporting totals
#### Scenario: Prior month insights can be generated
- **WHEN** the user requests insight generation for a previous month that has recorded data
@@ -21,3 +21,10 @@ The system SHALL keep AI insight generation read-only and return a safe fallback
#### Scenario: AI does not mutate financial records
- **WHEN** the system generates or stores monthly insights
- **THEN** no expense or paycheck records are created, updated, or deleted as part of that request
### Requirement: Insight generation remains private and resilient offline
The system SHALL keep monthly insight generation fully offline and provide a clear fallback response when the local model runtime or selected model is unavailable.
#### Scenario: Local runtime is unavailable
- **WHEN** the user requests monthly insights while the local AI runtime is not running or the configured model is unavailable
- **THEN** the system returns a clear setup or availability message instead of attempting a cloud fallback