Skip to content
Switch branches/tags

Latest commit


Failed to load latest commit information.
Latest commit message
Commit time


Generic badge Maven Central PyPI PyPI Download Stats LICENSE

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.


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.

Quick Links

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.

Sparse Retrieval

The SimpleSearcher class provides the entry point for sparse retrieval using bag-of-words representations. 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 import SimpleSearcher

searcher = SimpleSearcher.from_prebuilt_index('msmarco-passage')
hits ='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:

# Grab the raw Lucene Document:

Pre-built indexes are hosted on University of Waterloo servers. The following method will list available pre-built 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 SimpleDenseSearcher class provides the entry point for dense retrieval, and its usage is quite similar to SimpleSearcher. The only additional thing we need to specify for dense retrieval is the query encoder.

from pyserini.dsearch import SimpleDenseSearcher, TctColBertQueryEncoder

encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')
searcher = SimpleDenseSearcher.from_prebuilt_index(
hits ='what is a lobster roll')

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

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

import os

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:

from import SimpleSearcher
from pyserini.dsearch import SimpleDenseSearcher, TctColBertQueryEncoder
from pyserini.hsearch import HybridSearcher

ssearcher = SimpleSearcher.from_prebuilt_index('msmarco-passage')
encoder = TctColBertQueryEncoder('castorini/tct_colbert-msmarco')
dsearcher = SimpleDenseSearcher.from_prebuilt_index(
hsearcher = HybridSearcher(dsearcher, ssearcher)
hits ='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. This is easy to do:

from import SimpleSearcher

searcher = SimpleSearcher.from_prebuilt_index('msmarco-passage')
doc = searcher.doc('7157715')

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-passage index.
# Raw document

As you'd expected, 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())

# '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):

How do I index and search my own documents?

To build sparse (i.e., Lucene inverted indexes) on your own document collections, following the instructions below. To build dense indexes (e.g., the output of transformer encoders) on your own document collections, see instructions here. The following covers English documents; if you want to index and search multilingual documents, check out this answer.

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 \
  --input integrations/resources/sample_collection_jsonl \
  --collection JsonCollection \
  --generator DefaultLuceneDocumentGenerator \
  --index indexes/sample_collection_jsonl \
  --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 import SimpleSearcher

searcher = SimpleSearcher('indexes/sample_collection_jsonl')
hits ='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 \
    --topics integrations/resources/sample_queries.tsv \
    --index indexes/sample_collection_jsonl \
    --output run.sample.txt \

$ 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"]

Reproduction Guides

With Pyserini, it's easy to reproduce runs on a number of standard IR test collections!

Sparse Retrieval

Dense Retrieval


Pyserini provides baselines for a number of datasets.

Additional Documentation

Known Issues

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.

Release History

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.