Rusterini 🦀🦆

Rusterini is a direct port of Pyserini written mostly in RUST with bindings to Anserini 🦆 in JAVA using JNI (Java Native Interface) to enable Lucene capabilities.

This was mostly developed as a learning project to explore the speed and memory safety that Rust offers in a Library I am familiar with the inner workings ❗️❗️.

The plan is to expose as much of Pyserini as I can in this repository without directly binding to the python code.

Installation

To install Rusterini, you need to have Rust and Cargo installed on your system. If you don't have Rust installed, you can install it from the official website: https://www.rust-lang.org/tools/install

Once Rust is installed, you can install Rusterini by running the following command:

• Development Install

  • This repo depends on the Rust bindings of FAISS in C++. Thus we have to install Faiss using CMAKE by following the instructions here or Here
  • To Interface with huggingface models for generating sentence embeddings, this project depends on Candle.
  • Clone the repo and experiment away!

• Install From Cargo

  cargo install rusterini

Examples

(1.) Simple Lucene Index Searcher

Below example shows how to search through a Lucene Index of the MS Marco Passage Corpus

use rustserini::searcher::lucene::searcher::{LuceneQuery, LuceneSearcher};

let lucene_searcher = LuceneSearcher::new(String::from("indexes/msmarco-passage/lucene-index-msmarco"), None)?;

let search_query = LuceneQuery::String(
"did scientific minds lead to the success of the manhattan project".to_string(),
);

let hits = lucene_searcher.search(search_query, 10, None, None, false, false)?;

assert_eq!(lucene_searcher.num_docs, 8841823);
assert_eq!(result[0].docid, "0")

(2.) Simple Search example over a FAISS Flat Index

use rustserini::searcher::faiss::model::{AutoQueryEncoder, QueryEncoder};
use rustserini::searcher::faiss::searcher::{FaissSearchReturn, FaissSearcher};

let model_name = "castorini/mdpr-tied-pft-msmarco";
let tokenizer_name =  "castorini/mdpr-tied-pft-msmarco";
let query_encoder: AutoQueryEncoder =
    AutoQueryEncoder::new(model_name, tokenizer_name, true, true);

let mut searcher = FaissSearcher::new(
    "corpus/msmarco-passage-mini/pyserini".to_string(),
    query_encoder,
    768 as usize,
);

let result = searcher.search(
    "did scientific minds lead to the success of the manhattan project".to_string(),
    10,
    false,
);
let result = result?;
match result {
    FaissSearchReturn::Dense(search_results) => {
        println!("Result {:?}", search_results[0].docid);
        assert_eq!(search_results[0].docid, "0")
    }
    _ => panic!("Unexpected result type"),
}

(3.) Embedding Index (Faiss and JSON)

When Encoding a corpus, Pyserini provides capabilities to either write the embeddings to FAISS or in a JSON file. This repo contains examples that can be run as CLI functions with different parameters. Some example below for encoding the msmarco passage corpus using multilingual Dense Passage Retriever(mDPR) on huggingface:

• Create a directory and download the jsonlines corpus

  $ mkdir corpus/msmarco-passage
  $ wget  https://huggingface.co/datasets/Tevatron/msmarco-passage-corpus/resolve/main/corpus.jsonl.gz -P corpus/msmarco-passage

• Index the corpus. Run the command below to run the example shown here::

  $ cargo run --example faiss_embedding_writer --  --corpus corpus/msmarco-passage/corpus.jsonl.gz  --embeddings-dir indexes/msmarco-passage --encoder castorini/mdpr-tied-pft-msmarco --tokenizer castorini/mdpr-tied-pft-msmarco

Benchmark

Coming soon......