A lightweight and thread-safe, full-text search library that provides full control over the scoring calculations.
This start initially as a port of the Node library NDX.
Recipe (title) search with 50k documents.
https://quantleaf.github.io/probly-search-demo/
Three ways to do scoring
ScoreCalculator
trait. Trie based dynamic Inverted Index.
Documentation is under development. For now read the source tests.
Creating an index with a document that has 2 fields. Query documents, and remove a document. ```rust use std::collections::HashSet; use problysearch::{ index::{adddocumenttoindex, createindex, removedocumentfromindex, Index}, query::{ query, score::default::{bm25, zerotoone}, QueryResult, }, };
// Create index with two fields
let mut idx: Index
// Create docs from a custom Doc struct struct Doc { id: usize, title: String, description: String, }
let doc1 = Doc { id: 0, title: "abc".tostring(), description: "dfg".to_string(), };
let doc2 = Doc { id: 1, title: "dfgh".tostring(), description: "abcd".to_string(), };
// Add documents to index
fn tokenizer(s: &str) -> Vec
fn descriptionextract(d: &Doc) -> Option<&str> { Some(d.description.asstr()) }
fn filter(s: &String) -> String { s.to_owned() }
adddocumenttoindex( &mut idx, &[titleextract, descriptionextract], tokenizer, filter, doc1.id, doc_1.clone(), );
adddocumenttoindex( &mut idx, &[titleextract, descriptionextract], tokenizer, filter, doc2.id, doc_2, );
// Search, expect 2 results let mut result = query( &mut idx, &"abc", &mut bm25::new(), tokenizer, filter, &[1., 1.], None, ); asserteq!(result.len(), 2); asserteq!( result[0], QueryResult { key: 0, score: 0.6931471805599453 } ); assert_eq!( result[1], QueryResult { key: 1, score: 0.28104699650060755 } ); ```
Go through source tests in for the BM25 implementation and zero-to-one implementation for more query examples.