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A Lucene toolkit for replicable information retrieval research
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Anserini is an open-source information retrieval toolkit built on Lucene that aims to bridge the gap between academic information retrieval research and the practice of building real-world search applications. This effort grew out of a reproducibility study of various open-source retrieval engines in 2016 (Lin et al., ECIR 2016). Additional details can be found in a short paper (Yang et al., SIGIR 2017) and a journal article (Yang et al., JDIQ 2018).

Getting Started

Anserini requires Java 8 (note that there are known issues with Java 10 and Java 11) and Maven 3.3+. Oracle JVM is necessary to replicate our regression results; there are known issues with OpenJDK (see this and this). Build using Maven:

mvn clean package appassembler:assemble

The eval/ directory contains evaluation tools and scripts, including trec_eval,, ndeval. Before using trec_eval, unpack and compile it, as follows:

tar xvfz trec_eval.9.0.4.tar.gz && cd trec_eval.9.0.4 && make

Before using ndeval, compile it as follows:

cd ndeval && make

Running Standard IR Experiments

Anserini is designed to support experiments on various standard TREC collections out of the box. Each collection is associated with regression tests for replicability. Note that these regressions capture the "out of the box" experience, based on default parameter settings.

Additional regressions:


Additional Documentation

  • Axiomatic Reranking

  • IndexUtils is a utility to interact with an index using the command line (e.g., print index statistics). Refer to target/appassembler/bin/IndexUtils -h for more details.

  • MapCollections is a generic mapper framework for processing a document collection in parallel. Developers can write their own mappers for different tasks: one simple example is CountDocumentMapper which counts the number of documents in a collection:

    target/appassembler/bin/MapCollections -collection ClueWeb09Collection \
      -threads 16 -input ~/collections/web/ClueWeb09b/ClueWeb09_English_1/ \
      -mapper CountDocumentMapper -context CountDocumentMapperContext

Python Interface

Anserini was designed with Python integration in mind, for connecting with popular deep learning toolkits such as PyTorch. This is accomplished via pyjnius. The SimpleSearcher class provides a simple Python/Java bridge, shown below:

import jnius_config

from jnius import autoclass
JString = autoclass('java.lang.String')
JSearcher = autoclass('')

searcher = JSearcher(JString('lucene-index.robust04.pos+docvectors+rawdocs'))
hits ='hubble space telescope'))

# the docid of the 1st hit

# the internal Lucene docid of the 1st hit

# the score of the 1st hit

# the full document of the 1st hit

Release History



This research has been supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and the U.S. National Science Foundation under IIS-1423002 and CNS-1405688. Any opinions, findings, and conclusions or recommendations expressed do not necessarily reflect the views of the sponsors.

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