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// local_semantic_engine.rs — impl LocalSemanticEngine (included by local_semantic.rs)
// NO `use` imports here — they live in the parent module.
impl Default for LocalSemanticEngine {
fn default() -> Self {
Self::new()
}
}
impl LocalSemanticEngine {
/// Create a new local semantic engine
#[must_use]
pub fn new() -> Self {
Self {
documents: Vec::new(),
dtm: None,
vocabulary: HashMap::new(),
reverse_vocabulary: Vec::new(),
}
}
/// Index a directory of source files
///
/// # Arguments
/// * `path` - Directory path to scan
/// * `language_filter` - Optional language filter (e.g., "rust", "python")
///
/// # Returns
/// Number of documents indexed
pub fn index_directory(
&mut self,
path: &Path,
language_filter: Option<&str>,
) -> Result<usize, String> {
self.documents.clear();
for entry in WalkDir::new(path)
.max_depth(10)
.into_iter()
.filter_map(Result::ok)
{
if !entry.file_type().is_file() {
continue;
}
let file_path = entry.path();
let extension = file_path.extension().and_then(|e| e.to_str()).unwrap_or("");
let language = match extension {
"rs" => "rust",
"py" => "python",
"js" => "javascript",
"ts" => "typescript",
"go" => "go",
"java" => "java",
"c" | "h" => "c",
"cpp" | "hpp" | "cc" | "cxx" | "cu" | "cuh" => "cpp",
"rb" => "ruby",
"php" => "php",
"swift" => "swift",
"kt" => "kotlin",
_ => continue, // Skip non-code files
};
// Apply language filter if specified
if let Some(filter) = language_filter {
if language != filter {
continue;
}
}
// Read file content
if let Ok(content) = std::fs::read_to_string(file_path) {
// Skip very large files (> 100KB) and very small files (< 50 bytes)
if content.len() > 100_000 || content.len() < 50 {
continue;
}
self.documents.push(CodeDocument {
file_path: file_path.to_path_buf(),
content,
language: language.to_string(),
});
}
}
if self.documents.is_empty() {
return Err("No source files found to analyze".to_string());
}
// Build TF-IDF matrix
self.build_tfidf_matrix()?;
Ok(self.documents.len())
}
/// Build TF-IDF matrix from documents
fn build_tfidf_matrix(&mut self) -> Result<(), String> {
if self.documents.is_empty() {
return Err("No documents to analyze".to_string());
}
// Prepare document texts
let texts: Vec<&str> = self.documents.iter().map(|d| d.content.as_str()).collect();
// Create TF-IDF vectorizer with code-friendly settings
let mut vectorizer = TfidfVectorizer::new()
.with_tokenizer(Box::new(WhitespaceTokenizer::new()))
.with_min_df(2) // Minimum document frequency
.with_max_df(0.95) // Maximum document frequency (exclude very common terms)
.with_max_features(1000); // Limit vocabulary size (usize, not Option)
// Fit and transform - returns Matrix<f64>
let matrix = vectorizer
.fit_transform(&texts)
.map_err(|e| format!("TF-IDF vectorization failed: {}", e))?;
// Store the vocabulary
self.vocabulary = vectorizer.vocabulary().clone();
// Build reverse vocabulary (index -> word)
self.reverse_vocabulary = vec![String::new(); self.vocabulary.len()];
for (word, &idx) in &self.vocabulary {
if idx < self.reverse_vocabulary.len() {
self.reverse_vocabulary[idx] = word.clone();
}
}
self.dtm = Some(matrix);
Ok(())
}
/// Extract topics using LDA
///
/// # Arguments
/// * `num_topics` - Number of topics to extract (1-20)
/// * `language_filter` - Optional language filter
///
/// # Returns
/// Topic extraction results
pub fn extract_topics(
&mut self,
num_topics: usize,
language_filter: Option<String>,
) -> Result<LocalTopicResult, String> {
if num_topics == 0 || num_topics > 20 {
return Err("num_topics must be between 1 and 20".to_string());
}
// Re-index if language filter changed
if language_filter.is_some() {
let path = self
.documents
.first()
.map(|d| d.file_path.parent().unwrap_or(Path::new(".")))
.unwrap_or(Path::new("."))
.to_path_buf();
self.index_directory(&path, language_filter.as_deref())?;
}
let dtm = self
.dtm
.as_ref()
.ok_or("No documents indexed. Call index_directory first.")?;
if dtm.n_rows() < num_topics {
return Err(format!(
"Need at least {} documents for {} topics, but only {} indexed",
num_topics,
num_topics,
dtm.n_rows()
));
}
// Run LDA
let mut lda = LatentDirichletAllocation::new(num_topics).with_random_seed(42);
lda.fit(dtm, 50) // 50 iterations
.map_err(|e| format!("LDA failed: {}", e))?;
// Extract top terms per topic
let topic_word = lda
.topic_words()
.map_err(|e| format!("Failed to get topic-word distribution: {}", e))?;
let mut topics = Vec::new();
for topic_id in 0..num_topics {
// Get word weights for this topic
let mut term_weights: Vec<(usize, f64)> = (0..self.reverse_vocabulary.len())
.map(|word_idx| {
let weight = topic_word.get(topic_id, word_idx);
(word_idx, weight)
})
.filter(|(_, w)| *w > 0.0)
.collect();
// Sort by weight descending
term_weights.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
// Take top 10 terms
let top_terms: Vec<(String, f64)> = term_weights
.into_iter()
.take(10)
.filter_map(|(idx, weight)| {
self.reverse_vocabulary
.get(idx)
.filter(|s| !s.is_empty())
.map(|term| (term.clone(), weight))
})
.collect();
// Count documents with high probability for this topic
let doc_count = if let Ok(dt) = lda.document_topics() {
(0..dt.n_rows())
.filter(|&doc_idx| {
let p = dt.get(doc_idx, topic_id);
p > 0.1
})
.count()
} else {
0
};
topics.push(LocalTopic {
id: topic_id,
top_terms,
document_count: doc_count,
});
}
Ok(LocalTopicResult {
topics,
num_documents: self.documents.len(),
})
}
/// Cluster documents using specified method
///
/// # Arguments
/// * `method` - Clustering method: "kmeans", "hierarchical", or "dbscan"
/// * `k` - Number of clusters (required for kmeans)
///
/// # Returns
/// Clustering results
pub fn cluster(&self, method: &str, k: Option<usize>) -> Result<LocalClusterResult, String> {
let dtm = self
.dtm
.as_ref()
.ok_or("No documents indexed. Call index_directory first.")?;
// Convert f64 matrix to f32 for clustering
let n_rows = dtm.n_rows();
let n_cols = dtm.n_cols();
let data_f32: Vec<f32> = (0..n_rows * n_cols)
.map(|i| {
let row = i / n_cols;
let col = i % n_cols;
dtm.get(row, col) as f32
})
.collect();
let matrix_f32 = Matrix::from_vec(n_rows, n_cols, data_f32)
.map_err(|e| format!("Matrix conversion failed: {}", e))?;
let labels: Vec<i32> = match method {
"kmeans" => {
let k_val = k.ok_or("K-means requires --k parameter")?;
if k_val > n_rows {
return Err(format!(
"Cannot create {} clusters from {} documents",
k_val, n_rows
));
}
let mut kmeans = KMeans::new(k_val).with_max_iter(100).with_random_state(42);
kmeans
.fit(&matrix_f32)
.map_err(|e| format!("K-means failed: {}", e))?;
kmeans
.predict(&matrix_f32)
.into_iter()
.map(|l| l as i32)
.collect()
}
"hierarchical" => {
let n_clusters = k.unwrap_or(5.min(n_rows));
let mut agg =
AgglomerativeClustering::new(n_clusters, aprender::cluster::Linkage::Average);
agg.fit(&matrix_f32)
.map_err(|e| format!("Hierarchical clustering failed: {}", e))?;
agg.labels().iter().map(|&l| l as i32).collect()
}
"dbscan" => {
let mut dbscan = DBSCAN::new(0.5, 2);
dbscan
.fit(&matrix_f32)
.map_err(|e| format!("DBSCAN failed: {}", e))?;
dbscan.labels().clone()
}
_ => return Err(format!("Unknown clustering method: {}", method)),
};
// Group documents by cluster
let mut cluster_map: HashMap<i32, Vec<PathBuf>> = HashMap::new();
for (idx, &label) in labels.iter().enumerate() {
if label >= 0 {
// Skip noise points (label = -1 in DBSCAN)
cluster_map
.entry(label)
.or_default()
.push(self.documents[idx].file_path.clone());
}
}
let mut clusters: Vec<LocalCluster> = cluster_map
.into_iter()
.map(|(id, files)| LocalCluster {
id: id as usize,
size: files.len(),
files,
})
.collect();
clusters.sort_by_key(|c| std::cmp::Reverse(c.size));
Ok(LocalClusterResult {
clusters,
method: method.to_string(),
num_documents: self.documents.len(),
})
}
}