use std::collections::HashMap; use std::f64; fn update_url_scores(old: &mut HashMap, new: &HashMap) { for (url, score) in new { old.entry(url.to_string()).and_modify(|e| *e += score).or_insert(*score); } } fn normalize_string(input_string: &str) -> String { let string_without_punc: String = input_string.chars().filter(|&c| !c.is_ascii_punctuation()).collect(); let string_without_double_spaces: String = string_without_punc.split_whitespace().collect::>().join(" "); string_without_double_spaces.to_lowercase() } /// SearchEngine represents a search engine that indexes and searches documents based on the BM25 ranking algorithm. /// /// The search engine maintains an index of words and their frequencies in each document, as well as the actual document content. /// It provides methods to index documents, perform searches, and calculate relevance scores using the BM25 algorithm. /// /// # Examples /// /// ``` /// use std::collections::HashMap; /// use rustysearch::search::engine::SearchEngine; /// /// // Create a new search engine with k1 = 1.2 and b = 0.75 /// let mut engine = SearchEngine::new(1.2, 0.75); /// /// // Index a document /// engine.index("https://example.com/doc1", "This is the content of document 1"); /// /// // Perform a search /// let results = engine.search("content"); /// /// // Print the search results /// for (url, score) in results { /// println!("{} - Relevance Score: {}", url, score); /// } /// ``` #[derive(Default, Debug, Clone)] pub struct SearchEngine { index: HashMap>, documents: HashMap, k1: f64, b: f64, } impl SearchEngine { /// Creates a new instance of SearchEngine with the given parameters. /// /// # Arguments /// /// * `k1` - The k1 parameter of the BM25 algorithm. /// * `b` - The b parameter of the BM25 algorithm. /// /// **Returns** /// /// A new instance of SearchEngine. pub fn new(k1: f64, b: f64) -> SearchEngine { SearchEngine { index: HashMap::new(), documents: HashMap::new(), k1, b, } } /// Returns a vector of all the document URLs in the search engine's index. /// /// **Returns** /// /// A vector of document URLs. pub fn posts(&self) -> Vec { self.documents.keys().cloned().collect() } /// Returns the number of documents in the search engine's index. /// /// **Returns** /// /// The number of documents. pub fn number_of_documents(&self) -> usize { self.documents.len() } /// Returns the average document length in terms of number of words. /// /// **Returns** /// /// The average document length. pub fn avdl(&self) -> f64 { let total_length: usize = self.documents.values().map(|d| d.len()).sum(); total_length as f64 / self.documents.len() as f64 } /// Calculates the inverse document frequency (IDF) score for a given keyword. /// /// **Arguments** /// /// * `kw` - The keyword for which to calculate the IDF score. /// /// **Returns** /// /// The IDF score. pub fn idf(&self, kw: &str) -> f64 { let n = self.number_of_documents() as f64; let n_kw = self.get_urls(kw).len() as f64; ((n - n_kw + 0.5) / (n_kw + 0.5) + 1.0).ln() } /// Calculates the BM25 relevance scores for a given keyword. /// /// **Arguments** /// /// * `kw` - The keyword for which to calculate the relevance scores. /// /// **Returns** /// /// A HashMap containing the document URLs as keys and their relevance scores as values. pub fn bm25(&self, kw: &str) -> HashMap { let mut result = HashMap::new(); let idf_score = self.idf(kw); let avdl = self.avdl(); for (url, freq) in self.get_urls(kw) { let numerator = freq as f64 * (self.k1 + 1.0); let denominator = freq as f64 + self.k1 * (1.0 - self.b + self.b * self.documents.get(&url).unwrap().len() as f64 / avdl); result.insert(url.to_string(), idf_score * numerator / denominator); } result } /// Performs a search for the given query and returns the relevance scores for the matching documents. /// /// **Arguments** /// /// * `query` - The search query. /// /// **Returns** /// /// A HashMap containing the document URLs as keys and their relevance scores as values. pub fn search(&mut self, query: &str) -> HashMap { let keywords = normalize_string(query).split_whitespace().map(|s| s.to_string()).collect::>(); let mut url_scores: HashMap = HashMap::new(); for kw in keywords { let kw_urls_score = self.bm25(&kw); update_url_scores(&mut url_scores, &kw_urls_score); } url_scores } /// Indexes a document with the given URL and content. /// /// **Arguments** /// /// * `url` - The URL of the document. /// * `content` - The content of the document. pub fn index(&mut self, url: &str, content: &str) { self.documents.insert(url.to_string(), content.to_string()); let words = normalize_string(content).split_whitespace().map(|s| s.to_string()).collect::>(); for word in words { *self.index.entry(word).or_insert(HashMap::new()).entry(url.to_string()).or_insert(0) += 1; } } /// Bulk indexes multiple documents. /// /// **Arguments** /// /// * `documents` - A vector of tuples containing the URL and content of each document. pub fn bulk_index(&mut self, documents: Vec<(&str, &str)>) { for (url, content) in documents { self.index(url, content); } } /// Returns the URLs and frequencies of a given keyword in the search engine's index. /// /// **Arguments** /// /// * `keyword` - The keyword to search for. /// /// **Returns** /// /// A HashMap containing the document URLs as keys and their frequencies as values. pub fn get_urls(&self, keyword: &str) -> HashMap { let keyword = normalize_string(keyword); self.index.get(&keyword).cloned().unwrap_or(HashMap::new()) } /// Prints the current state of the search engine's index and document collection for debugging purposes. pub fn debug_index(&self) { log::debug!("Index: {:?}", self.index); log::debug!("Documents: {:?}", self.documents); } }