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style: Apply cargo fmt and clippy fixes across codebase

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Automated formatting and lint corrections from parallel agent work:

- cargo fmt: import reordering (alphabetical), line wrapping to respect
  max width, trailing comma normalization, destructuring alignment,
  function signature reformatting, match arm formatting
- clippy (pedantic): Range::contains() instead of manual comparisons,
  i64::from() instead of `as i64` casts, .clamp() instead of
  .max().min() chains, let-chain refactors (if-let with &&),
  #[allow(clippy::too_many_arguments)] and
  #[allow(clippy::field_reassign_with_default)] where warranted
- Removed trailing blank lines and extra whitespace

No behavioral changes. All existing tests pass unmodified.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Taylor Eernisse
2026-02-03 13:01:59 -05:00
parent ff94f24702
commit a50fc78823
42 changed files with 1431 additions and 623 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
src/search/filters.rs
View File

@@ -138,10 +138,7 @@ pub fn apply_filters(
}
let limit = filters.clamp_limit();
sql.push_str(&format!(
" ORDER BY j.key LIMIT ?{}",
param_idx
));
sql.push_str(&format!(" ORDER BY j.key LIMIT ?{}", param_idx));
params.push(Box::new(limit as i64));
let param_refs: Vec<&dyn rusqlite::types::ToSql> = params.iter().map(|p| p.as_ref()).collect();

14
src/search/fts.rs
View File

@@ -39,13 +39,13 @@ pub fn to_fts_query(raw: &str, mode: FtsQueryMode) -> String {
.split_whitespace()
.map(|token| {
// Check if token ends with * and the rest is alphanumeric
if token.ends_with('*') {
let stem = &token[..token.len() - 1];
if !stem.is_empty() && stem.chars().all(|c| c.is_alphanumeric() || c == '_') {
// Preserve prefix search: "stem"*
let escaped = stem.replace('"', "\"\"");
return format!("\"{}\"*", escaped);
}
if let Some(stem) = token.strip_suffix('*')
&& !stem.is_empty()
&& stem.chars().all(|c| c.is_alphanumeric() || c == '_')
{
// Preserve prefix search: "stem"*
let escaped = stem.replace('"', "\"\"");
return format!("\"{}\"*", escaped);
}
// Default: wrap in quotes, escape internal quotes
let escaped = token.replace('"', "\"\"");

76
src/search/hybrid.rs
View File

@@ -4,8 +4,8 @@ use rusqlite::Connection;
use crate::core::error::Result;
use crate::embedding::ollama::OllamaClient;
use crate::search::{rank_rrf, search_fts, search_vector, FtsQueryMode};
use crate::search::filters::{apply_filters, SearchFilters};
use crate::search::filters::{SearchFilters, apply_filters};
use crate::search::{FtsQueryMode, rank_rrf, search_fts, search_vector};
const BASE_RECALL_MIN: usize = 50;
const FILTERED_RECALL_MIN: usize = 200;
@@ -65,9 +65,9 @@ pub async fn search_hybrid(
// Adaptive recall
let requested = filters.clamp_limit();
let top_k = if filters.has_any_filter() {
(requested * 50).max(FILTERED_RECALL_MIN).min(RECALL_CAP)
(requested * 50).clamp(FILTERED_RECALL_MIN, RECALL_CAP)
} else {
(requested * 10).max(BASE_RECALL_MIN).min(RECALL_CAP)
(requested * 10).clamp(BASE_RECALL_MIN, RECALL_CAP)
};
let (fts_tuples, vec_tuples) = match mode {
@@ -88,10 +88,7 @@ pub async fn search_hybrid(
};
let query_embedding = client.embed_batch(vec![query.to_string()]).await?;
let embedding = query_embedding
.into_iter()
.next()
.unwrap_or_default();
let embedding = query_embedding.into_iter().next().unwrap_or_default();
if embedding.is_empty() {
return Err(crate::core::error::LoreError::Other(
@@ -115,41 +112,34 @@ pub async fn search_hybrid(
.collect();
match client {
Some(client) => {
match client.embed_batch(vec![query.to_string()]).await {
Ok(query_embedding) => {
let embedding = query_embedding
.into_iter()
.next()
.unwrap_or_default();
Some(client) => match client.embed_batch(vec![query.to_string()]).await {
Ok(query_embedding) => {
let embedding = query_embedding.into_iter().next().unwrap_or_default();
let vec_tuples = if embedding.is_empty() {
warnings.push(
"Ollama returned empty embedding, using FTS only.".into(),
);
Vec::new()
} else {
let vec_results = search_vector(conn, &embedding, top_k)?;
vec_results
.iter()
.map(|r| (r.document_id, r.distance))
.collect()
};
let vec_tuples = if embedding.is_empty() {
warnings
.push("Ollama returned empty embedding, using FTS only.".into());
Vec::new()
} else {
let vec_results = search_vector(conn, &embedding, top_k)?;
vec_results
.iter()
.map(|r| (r.document_id, r.distance))
.collect()
};
(fts_tuples, vec_tuples)
}
Err(e) => {
warnings.push(
format!("Embedding failed ({}), falling back to lexical search.", e),
);
(fts_tuples, Vec::new())
}
(fts_tuples, vec_tuples)
}
}
Err(e) => {
warnings.push(format!(
"Embedding failed ({}), falling back to lexical search.",
e
));
(fts_tuples, Vec::new())
}
},
None => {
warnings.push(
"Ollama unavailable, falling back to lexical search.".into(),
);
warnings.push("Ollama unavailable, falling back to lexical search.".into());
(fts_tuples, Vec::new())
}
}
@@ -217,7 +207,7 @@ mod tests {
..Default::default()
};
let requested = filters.clamp_limit();
let top_k = (requested * 10).max(BASE_RECALL_MIN).min(RECALL_CAP);
let top_k = (requested * 10).clamp(BASE_RECALL_MIN, RECALL_CAP);
assert_eq!(top_k, 200);
}
@@ -229,7 +219,7 @@ mod tests {
..Default::default()
};
let requested = filters.clamp_limit();
let top_k = (requested * 50).max(FILTERED_RECALL_MIN).min(RECALL_CAP);
let top_k = (requested * 50).clamp(FILTERED_RECALL_MIN, RECALL_CAP);
assert_eq!(top_k, 1000);
}
@@ -241,7 +231,7 @@ mod tests {
..Default::default()
};
let requested = filters.clamp_limit();
let top_k = (requested * 50).max(FILTERED_RECALL_MIN).min(RECALL_CAP);
let top_k = (requested * 50).clamp(FILTERED_RECALL_MIN, RECALL_CAP);
assert_eq!(top_k, RECALL_CAP); // 5000 capped to 1500
}
@@ -252,7 +242,7 @@ mod tests {
..Default::default()
};
let requested = filters.clamp_limit();
let top_k = (requested * 10).max(BASE_RECALL_MIN).min(RECALL_CAP);
let top_k = (requested * 10).clamp(BASE_RECALL_MIN, RECALL_CAP);
assert_eq!(top_k, BASE_RECALL_MIN); // 10 -> 50
}
}

12
src/search/mod.rs
View File

@@ -4,11 +4,11 @@ mod hybrid;
mod rrf;
mod vector;
pub use filters::{PathFilter, SearchFilters, apply_filters};
pub use fts::{
generate_fallback_snippet, get_result_snippet, search_fts, to_fts_query, FtsQueryMode,
FtsResult,
FtsQueryMode, FtsResult, generate_fallback_snippet, get_result_snippet, search_fts,
to_fts_query,
};
pub use filters::{apply_filters, PathFilter, SearchFilters};
pub use rrf::{rank_rrf, RrfResult};
pub use vector::{search_vector, VectorResult};
pub use hybrid::{search_hybrid, HybridResult, SearchMode};
pub use hybrid::{HybridResult, SearchMode, search_hybrid};
pub use rrf::{RrfResult, rank_rrf};
pub use vector::{VectorResult, search_vector};

31
src/search/rrf.rs
View File

@@ -22,10 +22,7 @@ pub struct RrfResult {
/// Ranks are 1-indexed (first result = rank 1).
///
/// Score = sum of 1/(k + rank) for each list containing the document.
pub fn rank_rrf(
vector_results: &[(i64, f64)],
fts_results: &[(i64, f64)],
) -> Vec<RrfResult> {
pub fn rank_rrf(vector_results: &[(i64, f64)], fts_results: &[(i64, f64)]) -> Vec<RrfResult> {
if vector_results.is_empty() && fts_results.is_empty() {
return Vec::new();
}
@@ -63,14 +60,16 @@ pub fn rank_rrf(
.collect();
// Sort descending by rrf_score
results.sort_by(|a, b| b.rrf_score.partial_cmp(&a.rrf_score).unwrap_or(std::cmp::Ordering::Equal));
results.sort_by(|a, b| {
b.rrf_score
.partial_cmp(&a.rrf_score)
.unwrap_or(std::cmp::Ordering::Equal)
});
// Normalize: best = 1.0
if let Some(max_score) = results.first().map(|r| r.rrf_score) {
if max_score > 0.0 {
for result in &mut results {
result.normalized_score = result.rrf_score / max_score;
}
if let Some(max_score) = results.first().map(|r| r.rrf_score).filter(|&s| s > 0.0) {
for result in &mut results {
result.normalized_score = result.rrf_score / max_score;
}
}
@@ -92,8 +91,16 @@ mod tests {
// Doc 1 score should be higher than doc 2 and doc 3
let doc1 = &results[0];
let doc2_score = results.iter().find(|r| r.document_id == 2).unwrap().rrf_score;
let doc3_score = results.iter().find(|r| r.document_id == 3).unwrap().rrf_score;
let doc2_score = results
.iter()
.find(|r| r.document_id == 2)
.unwrap()
.rrf_score;
let doc3_score = results
.iter()
.find(|r| r.document_id == 3)
.unwrap()
.rrf_score;
assert!(doc1.rrf_score > doc2_score);
assert!(doc1.rrf_score > doc3_score);
}

13
src/search/vector.rs
View File

@@ -70,7 +70,7 @@ pub fn search_vector(
FROM embeddings
WHERE embedding MATCH ?1
AND k = ?2
ORDER BY distance"
ORDER BY distance",
)?;
let rows: Vec<(i64, f64)> = stmt
@@ -137,11 +137,7 @@ mod tests {
#[test]
fn test_dedup_respects_limit() {
let rows = vec![
(1000_i64, 0.1_f64),
(2000, 0.2),
(3000, 0.3),
];
let rows = vec![(1000_i64, 0.1_f64), (2000, 0.2), (3000, 0.3)];
let results = search_vector_dedup(rows, 2);
assert_eq!(results.len(), 2);
}
@@ -161,7 +157,10 @@ mod tests {
}
let mut results: Vec<VectorResult> = best
.into_iter()
.map(|(document_id, distance)| VectorResult { document_id, distance })
.map(|(document_id, distance)| VectorResult {
document_id,
distance,
})
.collect();
results.sort_by(|a, b| a.distance.total_cmp(&b.distance));
results.truncate(limit);