Enhance lookup mechanics and priority propagation (`spec/lookup.go`) Remove the requirement of typing a leading `-` when searching for options, allowing flag suggestions to surface naturally during input Accurately forward the `Priority` field from registry specs into the `Suggestion` struct so the scoring engine evaluates relevance properly Integrate scoring into the main suggestion pipeline (`root/suggestions.go`) Collect context signals using `scoring.CollectSignals(cwd, query, rootCmd, store)` with the live working directory retrieved from `spec.GetCWD()` Pass the entire deduplicated list across spec, history and AI items through `scoring.Score(deduped, signals)` to compute scores and sort by descending order before rendering on screen Record execution history in Frecency Store (`root/wrapper.go`) Save the last executed command to `lastSubmittedCommand` whenever Enter is pressed or a suggestion is selected Trigger `store.Record(...)` asynchronously within the `IRIS_CMD_STOP` hook using a dedicated goroutine guarded by panic recovery and timeout limits, preventing blocks on the main shell thread Optimize workspace identification for AI (`internal/ai/context_provider.go`) Use `workspace.DetectCached(cwd)` to instantly fetch ecosystem context across Git, Node, Go, Rust, Python and Docker alongside active git branch state This caching mechanism avoids redundant subprocess execution when outside git repositories, significantly speeding up prompt context generation
207 lines
4.2 KiB
Go
207 lines
4.2 KiB
Go
package scoring
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import (
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"math"
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"sort"
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"strings"
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"github.com/versenilvis/iris/spec"
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)
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type ScoreBreakdown struct {
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BasePriority int
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ContextBonus int
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Frecency int
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MatchQuality int
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}
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type ScoredSuggestion struct {
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spec.Suggestion
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Score float64
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Breakdown ScoreBreakdown
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}
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type ScoreConfig struct {
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WeightBasePriority float64
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WeightContextBonus float64
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WeightFrecency float64
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WeightMatchQuality float64
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}
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var DefaultScoreConfig = ScoreConfig{
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WeightBasePriority: 0.30,
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WeightContextBonus: 0.25,
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WeightFrecency: 0.25,
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WeightMatchQuality: 0.20,
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}
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func Score(suggestions []spec.Suggestion, signals SignalSet) []ScoredSuggestion {
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return ScoreWithConfig(suggestions, signals, DefaultScoreConfig)
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}
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func ScoreWithConfig(suggestions []spec.Suggestion, signals SignalSet, config ScoreConfig) []ScoredSuggestion {
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if len(suggestions) == 0 {
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return nil
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}
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localMap := make(map[string]float64, len(signals.LocalFrecency))
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for _, e := range signals.LocalFrecency {
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localMap[e.Cmd] = e.RawScore
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}
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globalMap := make(map[string]float64, len(signals.GlobalFrecency))
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for _, e := range signals.GlobalFrecency {
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globalMap[e.Cmd] = e.RawScore
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}
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rawFrec := make([]float64, len(suggestions))
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for i, s := range suggestions {
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if score, ok := localMap[s.Cmd]; ok {
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rawFrec[i] = score
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} else if score, ok := globalMap[s.Cmd]; ok {
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rawFrec[i] = score * 0.7
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} else {
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rawFrec[i] = 0
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}
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}
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normFrec := normalizeFrecency(rawFrec)
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scored := make([]ScoredSuggestion, len(suggestions))
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for i, s := range suggestions {
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bp := basePriorityFor(s)
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cb := ApplyContextRules(signals.Workspace, s.Cmd)
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frec := normFrec[i]
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mq := matchQualityScore(s.Cmd, signals.Query)
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total := config.WeightBasePriority*float64(bp) +
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config.WeightContextBonus*float64(cb) +
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config.WeightFrecency*float64(frec) +
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config.WeightMatchQuality*float64(mq)
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scored[i] = ScoredSuggestion{
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Suggestion: s,
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Score: total,
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Breakdown: ScoreBreakdown{
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BasePriority: bp,
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ContextBonus: cb,
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Frecency: frec,
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MatchQuality: mq,
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},
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}
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}
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sort.SliceStable(scored, func(i, j int) bool {
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if scored[i].Score != scored[j].Score {
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return scored[i].Score > scored[j].Score
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}
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if scored[i].Breakdown.Frecency != scored[j].Breakdown.Frecency {
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return scored[i].Breakdown.Frecency > scored[j].Breakdown.Frecency
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}
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if scored[i].Breakdown.ContextBonus != scored[j].Breakdown.ContextBonus {
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return scored[i].Breakdown.ContextBonus > scored[j].Breakdown.ContextBonus
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}
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return scored[i].Cmd < scored[j].Cmd
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})
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return scored
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}
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func basePriorityFor(s spec.Suggestion) int {
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if s.Priority > 0 {
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if s.Priority > 100 {
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return 100
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}
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return s.Priority
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}
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switch s.Source {
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case "spec":
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return 60
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case "ai":
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if s.Confidence > 0 {
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if s.Confidence > 100 {
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return 100
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}
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return s.Confidence
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}
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return 50
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case "history":
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if s.Confidence > 0 {
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if s.Confidence > 100 {
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return 100
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}
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return s.Confidence
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}
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return 40
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default:
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return 50
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}
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}
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func matchQualityScore(cmd, query string) int {
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cmd = strings.TrimSpace(cmd)
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query = strings.TrimSpace(query)
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if query == "" {
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return 100
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}
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if cmd == query {
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return 100
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}
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if strings.HasPrefix(cmd, query) {
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return 100
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}
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if strings.HasPrefix(strings.ToLower(cmd), strings.ToLower(query)) {
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return 80
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}
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if strings.Contains(strings.ToLower(cmd), strings.ToLower(query)) {
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return 50
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}
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if isSubsequence(strings.ToLower(query), strings.ToLower(cmd)) {
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return 30
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}
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return 0
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}
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func isSubsequence(sub, full string) bool {
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subRunes := []rune(sub)
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fullRunes := []rune(full)
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if len(subRunes) == 0 {
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return true
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}
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i := 0
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for j := 0; j < len(fullRunes) && i < len(subRunes); j++ {
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if subRunes[i] == fullRunes[j] {
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i++
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}
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}
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return i == len(subRunes)
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}
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func normalizeFrecency(raw []float64) []int {
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if len(raw) == 0 {
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return nil
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}
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maxRaw := 0.0
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for _, r := range raw {
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if r > maxRaw {
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maxRaw = r
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}
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}
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if maxRaw <= 0 {
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res := make([]int, len(raw))
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return res
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}
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res := make([]int, len(raw))
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for i, r := range raw {
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val := int(math.Round((r / maxRaw) * 100.0))
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if val > 100 {
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val = 100
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} else if val < 0 {
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val = 0
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}
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res[i] = val
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}
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return res
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}
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