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Pyserini

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Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with our group's Anserini IR toolkit, which is built on Lucene. Retrieval using dense representations is provided via integration with Facebook's Faiss library.

Pyserini is primarily designed to provide effective, reproducible, and easy-to-use first-stage retrieval in a multi-stage ranking architecture. Our toolkit is self-contained as a standard Python package and comes with queries, relevance judgments, pre-built indexes, and evaluation scripts for many commonly used IR test collections With Pyserini, it's easy to reproduce runs on a number of standard IR test collections! A low-effort way to try things out is to look at our online notebooks, which will allow you to get started with just a few clicks.

For additional details, our paper in SIGIR 2021 provides a nice overview.

Important Note: Lucene 8 to Lucene 9 Transition

The PyPI release 0.17.1 at commit 33c87c (2022/08/13) is the last official Pyserini release built on Lucene 8, based on Anserini v0.14.4. Main Anserini trunk has been upgraded to Lucene 9.3.

This is an important but disruptive upgrade, as indexes built with Lucene 8 are not backwards compatible with Lucene 9 code (see Anserini #1952). There is a workaround, but we have yet to implement in Pyserini. Furthermore, Lucene 8 code is not able to read indexes built with Lucene 9. An upgrade to Lucene 9 is necessary to use Lucene's HNSW indexes, which will increase the capabilities of Pyserini and open up the design space of dense/sparse hybrids.

Thus, we are in a transition where a development installation is partially broken, since the development installation jar will be based on Lucene 9, but the Pyserini codebase has not been updated to accommodate. For a self-consistent development installation (that passes all tests), grab anserini-0.14.4-fatjar.jar from here to drop into pyserini/resources/jars.

This note supersedes all other Pyserini documentation on this site.

Installation

Install via PyPI (requires Python 3.8+):

pip install pyserini

Sparse retrieval depends on Anserini, which is itself built on Lucene, and thus Java 11.

Dense retrieval depends on neural networks and requires a more complex set of dependencies. A pip installation will automatically pull in the 🤗 Transformers library to satisfy the package requirements. Pyserini also depends on PyTorch and Faiss, but since these packages may require platform-specific custom configuration, they are not explicitly listed in the package requirements. We leave the installation of these packages to you.

The software ecosystem is rapidly evolving and a potential source of frustration is incompatibility among different versions of underlying dependencies. We provide additional detailed installation instructions here.

If you're planning on just using Pyserini, then the pip instructions above are fine. However, if you're planning on contributing to the codebase or want to work with the latest not-yet-released features, you'll need a development installation. Instructions are provided here.

How do I search?

Pyserini supports sparse retrieval (e.g., BM25 ranking using bag-of-words representations), dense retrieval (e.g., nearest-neighbor search on transformer-encoded representations), as well hybrid retrieval that integrates both approaches via linear combination of scores.

Sparse Retrieval

The LuceneSearcher class provides the entry point for retrieval using bag-of-words representations.

Usage

Anserini supports a number of pre-built indexes for common collections that it'll automatically download for you and store in ~/.cache/pyserini/indexes/. Here's how to use a pre-built index for the MS MARCO passage ranking task and issue a query interactively:

from pyserini.search.lucene import LuceneSearcher

searcher = LuceneSearcher.from_prebuilt_index('msmarco-v1-passage')
hits = searcher.search('what is a lobster roll?')

for i in range(0, 10):
    print(f'{i+1:2} {hits[i].docid:7} {hits[i].score:.5f}')

The results should be as follows:

 1 7157707 11.00830
 2 6034357 10.94310
 3 5837606 10.81740
 4 7157715 10.59820
 5 6034350 10.48360
 6 2900045 10.31190
 7 7157713 10.12300
 8 1584344 10.05290
 9 533614  9.96350
10 6234461 9.92200

To further examine the results:

# Grab the raw text:
hits[0].raw

# Grab the raw Lucene Document:
hits[0].lucene_document

Pre-built indexes are hosted on University of Waterloo servers. The following method will list available pre-built indexes:

LuceneSearcher.list_prebuilt_indexes()

A description of what's available can be found here. Alternatively, see this answer for how to download an index manually.

Dense Retrieval

The FaissSearcher class provides the entry point for retrieval using dense transformer-derived representations.

Usage

Anserini supports a number of pre-built indexes for common collections that it'll automatically download for you and store in ~/.cache/pyserini/indexes/. Here's how to use a pre-built index for the MS MARCO passage ranking task and issue a query interactively:

from pyserini.search.faiss import FaissSearcher, TctColBertQueryEncoder

encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')
searcher = FaissSearcher.from_prebuilt_index(
    'msmarco-passage-tct_colbert-hnsw',
    encoder
)
hits = searcher.search('what is a lobster roll')

for i in range(0, 10):
    print(f'{i+1:2} {hits[i].docid:7} {hits[i].score:.5f}')

Usage parallels LuceneSearcher, but for dense retrieval, we need to additionally specify the query encoder.

If you encounter an error (on macOS), you'll need the following:

import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'

The results should be as follows:

 1 7157710 70.53742
 2 7157715 70.50040
 3 7157707 70.13804
 4 6034350 69.93666
 5 6321969 69.62683
 6 4112862 69.34587
 7 5515474 69.21354
 8 7157708 69.08416
 9 6321974 69.06841
10 2920399 69.01737

Hybrid Sparse-Dense Retrieval

The HybridSearcher class provides the entry point to perform hybrid sparse-dense retrieval.

Usage

The HybridSearcher class is constructed from combining the output of LuceneSearcher and FaissSearcher:

from pyserini.search.lucene import LuceneSearcher
from pyserini.search.faiss import FaissSearcher, TctColBertQueryEncoder
from pyserini.search.hybrid import HybridSearcher

ssearcher = LuceneSearcher.from_prebuilt_index('msmarco-v1-passage')
encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')
dsearcher = FaissSearcher.from_prebuilt_index(
    'msmarco-passage-tct_colbert-hnsw',
    encoder
)
hsearcher = HybridSearcher(dsearcher, ssearcher)
hits = hsearcher.search('what is a lobster roll')

for i in range(0, 10):
    print(f'{i+1:2} {hits[i].docid:7} {hits[i].score:.5f}')

The results should be as follows:

 1 7157715 71.56022
 2 7157710 71.52962
 3 7157707 71.23887
 4 6034350 70.98502
 5 6321969 70.61903
 6 4112862 70.33807
 7 5515474 70.20574
 8 6034357 70.11168
 9 5837606 70.09911
10 7157708 70.07636

In general, hybrid retrieval will be more effective than dense retrieval, which will be more effective than sparse retrieval.

How do I fetch a document?

Another commonly used feature in Pyserini is to fetch a document (i.e., its text) given its docid. A sparse (Lucene) index can be configured to include the raw document text, in which case the doc() method can be used to fetch the document:

from pyserini.search.lucene import LuceneSearcher

searcher = LuceneSearcher.from_prebuilt_index('msmarco-v1-passage')
doc = searcher.doc('7157715')
Additional details

From doc, you can access its contents as well as its raw representation. The contents hold the representation of what's actually indexed; the raw representation is usually the original "raw document". A simple example can illustrate this distinction: for an article from CORD-19, raw holds the complete JSON of the article, which obviously includes the article contents, but has metadata and other information as well. The contents contain extracts from the article that's actually indexed (for example, the title and abstract). In most cases, contents can be deterministically reconstructed from raw. When building the index, we specify flags to store contents and/or raw; it is rarely the case that we store both, since that would be a waste of space. In the case of the pre-built msmacro-passage index, we only store raw. Thus:

# Document contents: what's actually indexed.
# Note, this is not stored in the pre-built msmacro-v1-passage index.
doc.contents()
                                                                                                   
# Raw document
doc.raw()

As you'd expected, doc.id() returns the docid, which is 7157715 in this case. Finally, doc.lucene_document() returns the underlying Lucene Document (i.e., a Java object). With that, you get direct access to the complete Lucene API for manipulating documents.

Since each text in the MS MARCO passage corpus is a JSON object, we can read the document into Python and manipulate:

import json
json_doc = json.loads(doc.raw())

json_doc['contents']
# 'contents' of the document:
# A Lobster Roll is a bread roll filled with bite-sized chunks of lobster meat...

Every document has a docid, of type string, assigned by the collection it is part of. In addition, Lucene assigns each document a unique internal id (confusingly, Lucene also calls this the docid), which is an integer numbered sequentially starting from zero to one less than the number of documents in the index. This can be a source of confusion but the meaning is usually clear from context. Where there may be ambiguity, we refer to the external collection docid and Lucene's internal docid to be explicit. Programmatically, the two are distinguished by type: the first is a string and the second is an integer.

As an important side note, Lucene's internal docids are not stable across different index instances. That is, in two different index instances of the same collection, Lucene is likely to have assigned different internal docids for the same document. This is because the internal docids are assigned based on document ingestion order; this will vary due to thread interleaving during indexing (which is usually performed on multiple threads).

The doc method in searcher takes either a string (interpreted as an external collection docid) or an integer (interpreted as Lucene's internal docid) and returns the corresponding document. Thus, a simple way to iterate through all documents in the collection (and for example, print out its external collection docid) is as follows:

for i in range(searcher.num_docs):
    print(searcher.doc(i).docid())

How do I index and search my own documents?

In addition to standard corpora used in IR and NLP research, Pyserini allows you to index and search your own documents.

Sparse Indexes

To build sparse (i.e., Lucene inverted indexes) on your own document collections, follow the instructions below.

Guide to indexing and searching English documents

Pyserini (via Anserini) provides ingestors for document collections in many different formats. The simplest, however, is the following JSON format:

{
  "id": "doc1",
  "contents": "this is the contents."
}

A document is simply comprised of two fields, a docid and contents. Pyserini accepts collections comprised of these documents organized in three different ways:

  • Folder with each JSON in its own file, like this.
  • Folder with files, each of which contains an array of JSON documents, like this.
  • Folder with files, each of which contains a JSON on an individual line, like this (often called JSONL format).

So, the quickest way to get started is to write a script that converts your documents into the above format. Then, you can invoke the indexer (here, we're indexing JSONL, but any of the other formats work as well):

python -m pyserini.index.lucene \
  --collection JsonCollection \
  --input tests/resources/sample_collection_jsonl \
  --index indexes/sample_collection_jsonl \
  --generator DefaultLuceneDocumentGenerator \
  --threads 1 \
  --storePositions --storeDocvectors --storeRaw

Three options control the type of index that is built:

  • --storePositions: builds a standard positional index
  • --storeDocvectors: stores doc vectors (required for relevance feedback)
  • --storeRaw: stores raw documents

If you don't specify any of the three options above, Pyserini builds an index that only stores term frequencies. This is sufficient for simple "bag of words" querying (and yields the smallest index size).

Once indexing is done, you can use SimpleSearcher to search the index:

from pyserini.search.lucene import LuceneSearcher

searcher = LuceneSearcher('indexes/sample_collection_jsonl')
hits = searcher.search('document')

for i in range(len(hits)):
    print(f'{i+1:2} {hits[i].docid:4} {hits[i].score:.5f}')

You should get something like the following:

 1 doc2 0.25620
 2 doc3 0.23140

If you want to perform a batch retrieval run (e.g., directly from the command line), organize all your queries in a tsv file, like here. The format is simple: the first field is a query id, and the second field is the query itself. Note that the file extension must end in .tsv so that Pyserini knows what format the queries are in.

Then, you can run:

python -m pyserini.search.lucene \
  --index indexes/sample_collection_jsonl \
  --topics tests/resources/sample_queries.tsv \
  --output run.sample.txt \
  --bm25

The output:

$ cat run.sample.txt
1 Q0 doc2 1 0.256200 Anserini
1 Q0 doc3 2 0.231400 Anserini
2 Q0 doc1 1 0.534600 Anserini
3 Q0 doc1 1 0.256200 Anserini
3 Q0 doc2 2 0.256199 Anserini
4 Q0 doc3 1 0.483000 Anserini

Note that output run file is in standard TREC format.

You can also add extra fields in your documents when needed, e.g. text features. For example, the SpaCy Named Entity Recognition (NER) result of contents could be stored as an additional field NER.

{
  "id": "doc1",
  "contents": "The Manhattan Project and its atomic bomb helped bring an end to World War II. Its legacy of peaceful uses of atomic energy continues to have an impact on history and science.",
  "NER": {
            "ORG": ["The Manhattan Project"],
            "MONEY": ["World War II"]
         }
}
Guide to indexing and searching non-English documents

Instructions for indexing and searching non-English corpora is quite similar to English corpora, so check out the above guide first.

Here's a sample collection in Chinese in the JSONL format. To index:

python -m pyserini.index.lucene \
  --collection JsonCollection \
  --input tests/resources/sample_collection_jsonl_zh \
  --language zh \
  --index indexes/sample_collection_jsonl_zh \
  --generator DefaultLuceneDocumentGenerator \
  --threads 1 \
  --storePositions --storeDocvectors --storeRaw

The only difference here is that we specify --language zh using the ISO language code.

Using LuceneSearcher to search the index:

from pyserini.search.lucene import LuceneSearcher

searcher = LuceneSearcher('indexes/sample_collection_jsonl_zh')
searcher.set_language('zh')
hits = searcher.search('滑铁卢')

for i in range(len(hits)):
    print(f'{i+1:2} {hits[i].docid:4} {hits[i].score:.5f}')

The only difference is to use set_language to set the language.

To perform a batch run:

python -m pyserini.search.lucene \
  --index indexes/sample_collection_jsonl_zh \
  --topics tests/resources/sample_queries_zh.tsv \
  --output run.sample_zh.txt \
  --language zh \
  --bm25

Here's what the query file looks like, in tsv. Once again, add --language zh.

And the expected output:

$ cat run.sample_zh.txt
1 Q0 doc1 1 1.337800 Anserini
2 Q0 doc3 1 0.119100 Anserini
2 Q0 doc2 2 0.092600 Anserini
2 Q0 doc1 3 0.091100 Anserini

Dense Indexes

To build dense indexes (e.g., Faiss indexes) on your own document collections, follow the instructions below.

Guide to indexing and searching English documents

To build the dense index, Pyserini allows to either directly build Faiss Flat index via pyserini.encode with output --to-faiss, or first encode collections into vectors via pyserini.encode, then build various types of Faiss index via pyserini.index.faiss based on the encoded collections.

To use the pyserini.encode, the input should be in JSONL format. Each line is a json dictionary containing two fields, i.e .id and contents.

  • id is the document id in string.
  • contents contains all the fields of the documents. By default, Pyserini expects the fields in contents are separated by \n. The field's boundary can be controled using --delimiter argument under input, see the example script below.

For example, the following document has four fields in contents, url, title, text and expand, where the value of each field is "www.url.com, title, this is the contents, and document expansion respectively.

{
  "id": "doc1",
  "contents": "www.url.com\ntitle\nthis is the contents.\ndocument expansion"
}

The contents can also only have one fields, as in the tests/resources/simple_cacm_corpus.json sample file:

{
  "id": "CACM-2636",
  "contents": "Generation of Random Correlated Normal ... \n"
}

With the collection in the correct foramt, we can now encode documents with Dense encoders:

python -m pyserini.encode \
  input   --corpus tests/resources/simple_cacm_corpus.json \
          --fields text \  # fields in collection contents
          --delimiter "\n" \
          --shard-id 0 \   # The id of current shard. Default is 0
          --shard-num 1 \  # The total number of shards. Default is 1
  output  --embeddings path/to/output/dir \
          --to-faiss \
  encoder --encoder castorini/tct_colbert-v2-hnp-msmarco \
          --fields text \  # fields to encode, they must appear in the input.fields
          --batch 32 \
          --fp16  # if inference with autocast()
  • the --corpus can be either be a json file, or a directory that contains multiple json files
  • with --to-faiss, the generated embeddings will be stored as FaissIndexIP directly. Otherwise it will be stored in .jsonl format. If in .jsonl format, each line contains following info:
{
  "id": "CACM-2636",
  "contents": "Generation of Random Correlated Normal ... \n"},
  "vector": [0.126, ..., -0.004]
}
  • The shard-id and shard-num arguments are for speeding up the encoding, where the shard-num controls the total shard you want to segment the collection into, and the shard-id is the id of the current shard to encode. For example, if shard-num is 4 and shard-id is 0, the command would create a sub-index for the first 1/4 of the collection. Then you can run 4 process on 4 gpu to speed up the process by 4 times. Once it's done, you can merge the sub-indexes together by:
python -m pyserini.index.merge_faiss_indexes --prefix indexes/dindex-sample-dpr-multi- --shard-num 4

Encode documents with Sparse encoder

python -m pyserini.encode \
  input   --corpus tests/resources/simple_cacm_corpus.json \
          --fields text \
  output  --embeddings path/to/output/dir \
  encoder --encoder castorini/unicoil-d2q-msmarco-passage \
          --fields text \
          --batch 32 \
          --fp16 # if inference with autocast()

The output will be stored in jsonl format. Each line contains following info:

{
  "id": "CACM-2636",
  "contents": "Generation of Random Correlated Normal ... \n",
  "vector": {"generation":  0.12, "of":  0.1, "random":  0, ...}
}

Once the collections are encoded into vectors, we can start to build the index.

Pyserini supports four types of index so far:

  1. HNSWPQ
python -m pyserini.index.faiss \
  --input path/to/encoded/corpus \  # either in the Faiss or the jsonl format
  --output path/to/output/index \
  --hnsw \
  --pq
  1. HNSW
python -m pyserini.index.faiss \
  --input path/to/encoded/corpus \  # either in the Faiss or the jsonl format
  --output path/to/output/index \
  --hnsw
  1. PQ
python -m pyserini.index.faiss \
  --input path/to/encoded/corpus \  # either in the Faiss or the jsonl format
  --output path/to/output/index \
  --pq
  1. Flat

This command is for converting the .jsonl format into Faiss flat format, and generates the same files with pyserini.encode with --to-faiss specified.

python -m pyserini.index.faiss \
  --input path/to/encoded/corpus \  # in jsonl format
  --output path/to/output/index \

Once the index is built, you can use FaissSearcher to search in the collection:

from pyserini.search import FaissSearcher

searcher = FaissSearcher(
    'indexes/dindex-sample-dpr-multi',
    'facebook/dpr-question_encoder-multiset-base'
)
hits = searcher.search('what is a lobster roll')

for i in range(0, 10):
    print(f'{i+1:2} {hits[i].docid:7} {hits[i].score:.5f}')

Two-Click Reproductions

Accompanying our SIGIR 2022 paper, we introduced "two-click reproductions" that allow anyone to reproduce experimental runs with only two clicks (i.e., copy and paste). We provide access to a multitude of experimental conditions organized in the following pages:

Reproduction Guides

With Pyserini, it's easy to reproduce runs on a number of standard IR test collections! We provide a number of pre-built indexes that directly support reproducibility "out of the box". The following guides provide step-by-step instructions:

Sparse Retrieval

Dense Retrieval

Hybrid Sparse-Dense Retrieval

Available Corpora

Corpora Size Checksum
MS MARCO V1 passage: uniCOIL (noexp) 2.7 GB f17ddd8c7c00ff121c3c3b147d2e17d8
MS MARCO V1 passage: uniCOIL (d2q-T5) 3.4 GB 78eef752c78c8691f7d61600ceed306f
MS MARCO V1 doc: uniCOIL (noexp) 11 GB 11b226e1cacd9c8ae0a660fd14cdd710
MS MARCO V1 doc: uniCOIL (d2q-T5) 19 GB 6a00e2c0c375cb1e52c83ae5ac377ebb
MS MARCO V2 passage: uniCOIL (noexp) 24 GB d9cc1ed3049746e68a2c91bf90e5212d
MS MARCO V2 passage: uniCOIL (d2q-T5) 41 GB 1949a00bfd5e1f1a230a04bbc1f01539
MS MARCO V2 doc: uniCOIL (noexp) 55 GB 97ba262c497164de1054f357caea0c63
MS MARCO V2 doc: uniCOIL (d2q-T5) 72 GB c5639748c2cbad0152e10b0ebde3b804

FAQs

Additional Documentation

Release History

Additional technical notes

With v0.11.0.0 and before, Pyserini versions adopted the convention of X.Y.Z.W, where X.Y.Z tracks the version of Anserini, and W is used to distinguish different releases on the Python end. Starting with Anserini v0.12.0, Anserini and Pyserini versions have become decoupled.

Anserini is designed to work with JDK 11. There was a JRE path change above JDK 9 that breaks pyjnius 1.2.0, as documented in this issue, also reported in Anserini here and here. This issue was fixed with pyjnius 1.2.1 (released December 2019). The previous error was documented in this notebook and this notebook documents the fix.

Citation

If you use Pyserini, please cite the following paper:

@INPROCEEDINGS{Lin_etal_SIGIR2021_Pyserini,
   author = "Jimmy Lin and Xueguang Ma and Sheng-Chieh Lin and Jheng-Hong Yang and Ronak Pradeep and Rodrigo Nogueira",
   title = "{Pyserini}: A {Python} Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations",
   booktitle = "Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)",
   year = 2021,
   pages = "2356--2362",
}

Acknowledgments

This research is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.

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Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

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