Merge pull request 'search : recherche fuzzy + suggestions (issue #26)' (#43) from feat/fuzzy-search into main

src/catalog.rs

@@ -80,35 +80,125 @@ impl Catalog {
     }
 
     /// Case-insensitive search over name, display name, description,
-    /// category, tags and note.
+    /// category, tags and note — typo-tolerant, ranked by relevance.
     pub fn search(&self, keyword: &str, category: Option<&str>) -> Vec<&AgentDef> {
         let kw = keyword.to_lowercase();
-        self.agents
+        let mut scored: Vec<(u32, &AgentDef)> = self
+            .agents
             .iter()
-            .filter(|a| {
-                if a.hidden {
-                    return false;
-                }
-                if let Some(cat) = category {
-                    if a.category.as_deref() != Some(cat) {
-                        return false;
-                    }
-                }
-                let hay = format!(
-                    "{} {} {} {} {} {}",
-                    a.name,
-                    a.title(),
-                    a.description.as_deref().unwrap_or(""),
-                    a.category.as_deref().unwrap_or(""),
-                    a.tags.join(" "),
-                    a.note.as_deref().unwrap_or("")
-                )
-                .to_lowercase();
-                hay.contains(&kw)
+            .filter(|a| !a.hidden)
+            .filter(|a| category.map_or(true, |c| a.category.as_deref() == Some(c)))
+            .filter_map(|a| {
+                let name = a.name.to_lowercase();
+                let title = a.title().to_lowercase();
+                let rest = format!(
+                    "{} {} {}",
+                    a.description.as_deref().unwrap_or("").to_lowercase(),
+                    a.category.as_deref().unwrap_or("").to_lowercase(),
+                    a.tags.join(" ").to_lowercase()
+                );
+                let score = score_keyword(&kw, &name)
+                    .saturating_mul(3)
+                    .saturating_add(score_keyword(&kw, &title).saturating_mul(2))
+                    .saturating_add(score_keyword(&kw, &rest));
+                (score > 0).then_some((score, a))
             })
-            .collect()
+            .collect();
+        scored.sort_by(|(sa, a), (sb, b)| sb.cmp(sa).then(a.name.cmp(&b.name)));
+        scored.into_iter().map(|(_, a)| a).collect()
     }
 
+    /// "Did you mean" candidates for a keyword with no results: closest
+    /// agent names and display names by edit distance.
+    pub fn suggest(&self, keyword: &str) -> Vec<String> {
+        let kw = keyword.to_lowercase();
+        let mut out: Vec<(usize, String)> = Vec::new();
+        for a in self.agents.iter().filter(|a| !a.hidden) {
+            for field in [&a.name, &a.title().to_string()] {
+                let f = field.to_lowercase();
+                if f.is_empty() {
+                    continue;
+                }
+                let d = levenshtein(&kw, &f);
+                if d <= 3 && !out.iter().any(|(_, n)| n == &a.name) {
+                    out.push((d, a.name.clone()));
+                }
+            }
+        }
+        out.sort_by(|(da, na), (db, nb)| da.cmp(db).then(na.cmp(nb)));
+        out.truncate(4);
+        out.into_iter().map(|(_, n)| n).collect()
+    }
+}
+
+/// Score a keyword against one haystack field: 100 for a full hit,
+/// then subsequence and typo bonuses; 0 = no relation at all.
+fn score_keyword(keyword: &str, hay: &str) -> u32 {
+    if keyword.is_empty() {
+        return 0;
+    }
+    let tokens: Vec<&str> = keyword.split_whitespace().collect();
+    let mut total = 0u32;
+    for token in &tokens {
+        if token.is_empty() {
+            continue;
+        }
+        let s = score_token(token, hay);
+        if s == 0 {
+            return 0;
+        }
+        total += s;
+    }
+    total
+}
+
+fn score_token(token: &str, hay: &str) -> u32 {
+    if hay.contains(token) {
+        return 100;
+    }
+    if subsequence(token, hay) {
+        return 40;
+    }
+    // Typo tolerance: compare the token with every word of the field.
+    let mut best = usize::MAX;
+    for word in hay.split(|c: char| !c.is_alphanumeric()) {
+        if word.is_empty() {
+            continue;
+        }
+        best = best.min(levenshtein(token, word));
+    }
+    match best {
+        0..=1 => 25,
+        2 => 15,
+        3 => 5,
+        _ => 0,
+    }
+}
+
+/// Do the characters of needle appear in order inside hay?
+fn subsequence(needle: &str, hay: &str) -> bool {
+    let mut it = hay.chars();
+    needle.chars().all(|c| it.any(|h| h == c))
+}
+
+/// Classic Levenshtein distance on characters.
+fn levenshtein(a: &str, b: &str) -> usize {
+    let a: Vec<char> = a.chars().collect();
+    let b: Vec<char> = b.chars().collect();
+    let mut prev: Vec<usize> = (0..=b.len()).collect();
+    let mut cur = vec![0usize; b.len() + 1];
+    for (i, ca) in a.iter().enumerate() {
+        cur[0] = i + 1;
+        for (j, cb) in b.iter().enumerate() {
+            let cost = if ca == cb { 0 } else { 1 };
+            cur[j + 1] = (prev[j] + cost).min(prev[j + 1] + 1).min(cur[j] + 1);
+        }
+        std::mem::swap(&mut prev, &mut cur);
+    }
+    prev[b.len()]
+}
+
+impl Catalog {
     /// Sorted list of all categories present in the catalog.
     pub fn categories(&self) -> Vec<String> {
         let mut cats: Vec<String> = self
@@ -156,4 +246,50 @@ mod tests {
         let cats = cat.categories();
         assert!(cats.contains(&"coding-agent".to_string()));
     }
+
+    #[test]
+    fn fuzzy_search_tolerates_missing_separator() {
+        let cat = catalog();
+        let hits = cat.search("claude cod", None);
+        assert!(
+            hits.iter().any(|a| a.name == "claude-code"),
+            "expected claude-code in {:?}",
+            hits.iter().map(|a| &a.name).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn fuzzy_search_tolerates_typos() {
+        let cat = catalog();
+        let hits = cat.search("cludecode", None);
+        assert!(hits.iter().any(|a| a.name == "claude-code"));
+    }
+
+    #[test]
+    fn exact_matches_rank_first() {
+        let cat = catalog();
+        let hits = cat.search("claude", None);
+        assert!(!hits.is_empty());
+        assert_eq!(
+            hits[0].name, "claude-code",
+            "exact name match must rank first: {:?}",
+            hits.iter().map(|a| &a.name).collect::<Vec<_>>()
+        );
+    }
+
+    #[test]
+    fn suggest_proposes_close_names() {
+        let cat = catalog();
+        let sug = cat.suggest("aiderr");
+        assert!(sug.contains(&"aider".to_string()), "suggestions: {sug:?}");
+        assert!(sug.len() <= 4);
+    }
+
+    #[test]
+    fn levenshtein_distance() {
+        assert_eq!(levenshtein("aider", "aider"), 0);
+        assert_eq!(levenshtein("aider", "aiderr"), 1);
+        assert_eq!(levenshtein("abc", "axc"), 1);
+        assert_eq!(levenshtein("abc", "xyz"), 3);
+    }
 }

src/commands/search_cmd.rs

@@ -53,8 +53,16 @@ pub fn run(app: &App, keyword: &str, category: Option<&str>) -> Result<i32> {
         )
     );
     if app.catalog.search(keyword, category).is_empty() {
-        app.log
-            .info(&format!("no agent matches '{keyword}' (categories: {})", app.catalog.categories().join(", ")));
+        let suggestions = app.catalog.suggest(keyword);
+        if suggestions.is_empty() {
+            app.log
+                .info(&format!("no agent matches '{keyword}' (categories: {})", app.catalog.categories().join(", ")));
+        } else {
+            app.log.info(&format!(
+                "no agent matches '{keyword}' — vouliez-vous dire : {} ?",
+                suggestions.join(", ")
+            ));
+        }
     }
     Ok(0)
 }