ZIm/crates/assistant/src/search.rs
Antonio Scandurra 3a79aa85f4
Fuzzy-match lines when applying edits from the assistant (#12056)
This uses Jaro-Winkler similarity for now, which seemed to produce
pretty good results in my tests. We can easily swap it with something
else if needed.

Release Notes:

- N/A
2024-05-20 17:02:15 +02:00

171 lines
4.9 KiB
Rust

use language::Rope;
use std::ops::Range;
/// Search the given buffer for the given substring, ignoring any differences
/// in line indentation between the query and the buffer.
///
/// Returns a vector of ranges of byte offsets in the buffer corresponding
/// to the entire lines of the buffer.
pub fn fuzzy_search_lines(haystack: &Rope, needle: &str) -> Option<Range<usize>> {
const SIMILARITY_THRESHOLD: f64 = 0.8;
let mut best_match: Option<(Range<usize>, f64)> = None; // (range, score)
let mut haystack_lines = haystack.chunks().lines();
let mut haystack_line_start = 0;
while let Some(mut haystack_line) = haystack_lines.next() {
let next_haystack_line_start = haystack_line_start + haystack_line.len() + 1;
let mut advanced_to_next_haystack_line = false;
let mut matched = true;
let match_start = haystack_line_start;
let mut match_end = next_haystack_line_start;
let mut match_score = 0.0;
let mut needle_lines = needle.lines().peekable();
while let Some(needle_line) = needle_lines.next() {
let similarity = line_similarity(haystack_line, needle_line);
if similarity >= SIMILARITY_THRESHOLD {
match_end = haystack_lines.offset();
match_score += similarity;
if needle_lines.peek().is_some() {
if let Some(next_haystack_line) = haystack_lines.next() {
advanced_to_next_haystack_line = true;
haystack_line = next_haystack_line;
} else {
matched = false;
break;
}
} else {
break;
}
} else {
matched = false;
break;
}
}
if matched
&& best_match
.as_ref()
.map(|(_, best_score)| match_score > *best_score)
.unwrap_or(true)
{
best_match = Some((match_start..match_end, match_score));
}
if advanced_to_next_haystack_line {
haystack_lines.seek(next_haystack_line_start);
}
haystack_line_start = next_haystack_line_start;
}
best_match.map(|(range, _)| range)
}
/// Calculates the similarity between two lines, ignoring leading and trailing whitespace,
/// using the Jaro-Winkler distance.
///
/// Returns a value between 0.0 and 1.0, where 1.0 indicates an exact match.
fn line_similarity(line1: &str, line2: &str) -> f64 {
strsim::jaro_winkler(line1.trim(), line2.trim())
}
#[cfg(test)]
mod test {
use super::*;
use gpui::{AppContext, Context as _};
use language::Buffer;
use unindent::Unindent as _;
use util::test::marked_text_ranges;
#[gpui::test]
fn test_fuzzy_search_lines(cx: &mut AppContext) {
let (text, expected_ranges) = marked_text_ranges(
&r#"
fn main() {
if a() {
assert_eq!(
1 + 2,
does_not_match,
);
}
println!("hi");
assert_eq!(
1 + 2,
3,
); // this last line does not match
« assert_eq!(
1 + 2,
3,
);
»
« assert_eq!(
"something",
"else",
);
»
}
"#
.unindent(),
false,
);
let buffer = cx.new_model(|cx| Buffer::local(&text, cx));
let snapshot = buffer.read_with(cx, |buffer, _| buffer.snapshot());
let actual_range = fuzzy_search_lines(
snapshot.as_rope(),
&"
assert_eq!(
1 + 2,
3,
);
"
.unindent(),
)
.unwrap();
assert_eq!(actual_range, expected_ranges[0]);
let actual_range = fuzzy_search_lines(
snapshot.as_rope(),
&"
assert_eq!(
1 + 2,
3,
);
"
.unindent(),
)
.unwrap();
assert_eq!(actual_range, expected_ranges[0]);
let actual_range = fuzzy_search_lines(
snapshot.as_rope(),
&"
asst_eq!(
\"something\",
\"els\"
)
"
.unindent(),
)
.unwrap();
assert_eq!(actual_range, expected_ranges[1]);
let actual_range = fuzzy_search_lines(
snapshot.as_rope(),
&"
assert_eq!(
2 + 1,
3,
);
"
.unindent(),
);
assert_eq!(actual_range, None);
}
}