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078e5e817c
- macOS, Linux badge and AI suggestions showcase in README - Update user guide and docs for development
28 lines
1.4 KiB
Markdown
28 lines
1.4 KiB
Markdown
# Scoring & ranking architecture (`internal/scoring/`)
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The scoring engine ranks suggestions by combining frecency algorithms, workflow sequence learning, and item-type priority rules.
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## Core concepts
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### 1. Frecency calculation (`internal/scoring/frecency.go`)
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Frecency combines execution **frequency** with **recency** decay:
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$$\text{Score} = \text{Frequency} \times e^{-\lambda \Delta t}$$
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Commands executed recently receive a higher score multiplier that decays over time.
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### 2. Workflow sequence learning (`internal/scoring/context_rules.go`)
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Iris tracks sequential command pairs to learn common developer workflows (e.g. `git add` $\rightarrow$ `git commit`, `go build` $\rightarrow$ `./iris`). When a parent command skeleton matches the previous command, related suggestions receive a priority boost.
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### 3. Skeleton extraction (`internal/scoring/skeleton.go`)
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`ExtractSkeleton(cmd)` normalizes full command strings into structural skeletons by removing specific arguments and flags (e.g. `git commit -m "feat: test"` $\rightarrow$ `git commit`).
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### 4. Spec priority & flag gating (`spec/lookup.go`)
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Within spec completion mode:
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- Files and subcommands default to standard priority (`Priority = 30`).
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- Flags and options default to low priority (`Priority = 10`) when typing arguments.
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- When the user explicitly types `-` or `--`, flags are promoted (`Priority = 80`). |