BM25S (or BM25-Sparse) is an ultrafast implementation of BM25 in pure Python, powered by Numpy

Welcome to bm25s, a library that implements BM25 in Python, allowing you to rank documents based on a query. BM25 is a widely used ranking function used for text retrieval tasks, and is a core component of search services like Elasticsearch.

It is designed to be:

Fast: bm25s is implemented in pure Python and leverage sparse matrices to store eagerly computed scores for all document tokens. This allows extremely fast scoring at query time, improving performance over popular libraries by orders of magnitude (see benchmarks below).
Simple: bm25s is designed to be easy to use and understand. You can install it with pip and start using it in minutes. There is no dependencies on Java or Pytorch - all you need is Numpy, and optional lightweight dependencies for stemming and speedup via numba compilation.

Below, we compare bm25s with Elasticsearch in terms of speedup over rank-bm25, the most popular Python implementation of BM25. We measure the throughput in queries per second (QPS) on a few popular datasets from BEIR in a single-threaded setting.

comparison

[!IMPORTANT] New in version 0.2.0: We are rolling out support for a numba backend, which gives around 2x speedup for larger datasets! Learn more about it and share your thoughts in the version 0.2.0 release thread.

@misc{bm25s,
      title={BM25S: Orders of magnitude faster lexical search via eager sparse scoring}, 
      author={Xing Han Lù},
      year={2024},
      eprint={2407.03618},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2407.03618},
}

Installation

You can install bm25s with pip:

pip install bm25s

You can install the recommended (but optional) dependencies:

# HIGHLY RECOMMENDED: To install all core dependencies (json loading, progress bar, stemming, JIT compilation)
pip install "bm25s[core]"

# If you just want to use stemming for better results, you can install a stemmer
pip install PyStemmer

# Install all extra dependencies
pip install "bm25s[full]"

Quickstart

Here is a simple example of how to use bm25s:

import bm25s
import Stemmer  # optional: for stemming

# Create your corpus here
corpus = [
    "a cat is a feline and likes to purr",
    "a dog is the human's best friend and loves to play",
    "a bird is a beautiful animal that can fly",
    "a fish is a creature that lives in water and swims",
]

# optional: create a stemmer
stemmer = Stemmer.Stemmer("english")

# Tokenize the corpus and only keep the ids (faster and saves memory)
corpus_tokens = bm25s.tokenize(corpus, stopwords="en", stemmer=stemmer)

# Create the BM25 model and index the corpus
retriever = bm25s.BM25()
retriever.index(corpus_tokens)

# Query the corpus
query = "does the fish purr like a cat?"
query_tokens = bm25s.tokenize(query, stemmer=stemmer)

# Get top-k results as a tuple of (doc ids, scores). Both are arrays of shape (n_queries, k).
# To return docs instead of IDs, set the `corpus=corpus` parameter.
results, scores = retriever.retrieve(query_tokens, k=2)

for i in range(results.shape[1]):
    doc, score = results[0, i], scores[0, i]
    print(f"Rank {i+1} (score: {score:.2f}): {doc}")

# You can save the arrays to a directory...
retriever.save("animal_index_bm25")

# You can save the corpus along with the model
retriever.save("animal_index_bm25", corpus=corpus)

# ...and load them when you need them
import bm25s
reloaded_retriever = bm25s.BM25.load("animal_index_bm25", load_corpus=True)
# set load_corpus=False if you don't need the corpus

Corpus Format

bm25s separates the text you index from the values you get back. Pass strings to bm25s.tokenize(), then pass the resulting tokenized corpus to retriever.index(). The optional corpus argument on BM25(...), retrieve(...), and save(...) is the list of values returned for matching document IDs.

Both plain strings and dictionaries are valid corpus entries:

text_corpus = [
    "a cat is a feline and likes to purr",
    "a dog is the human's best friend and loves to play",
]

metadata_corpus = [
    {"id": "cat-doc", "title": "About Cat", "text": text_corpus[0]},
    {"id": "dog-doc", "title": "About Dog", "text": text_corpus[1]},
]

# Pick the text field(s) you want to index.
corpus_tokens = bm25s.tokenize([doc["text"] for doc in metadata_corpus])

# Retrieved documents will be the original dictionary entries.
retriever = bm25s.BM25(corpus=metadata_corpus)
retriever.index(corpus_tokens)

Dictionaries do not have required keys; use the shape that fits your application, such as id, title, text, or nested metadata. Retrieval is position-based, so corpus[i] is returned for document ID i. Keep the corpus you pass to BM25(...), retrieve(...), or save(...) in the same order and length as the indexed documents. When saving, entries must be strings, dictionaries, lists, or tuples that can be serialized to JSON. String entries are written to corpus.jsonl as {"id": i, "text": doc}; dictionaries, lists, and tuples are written as provided.

For an example that shows how to quickly index a 2M-documents corpus (Natural Questions), check out examples/index_nq.py.

High Level API

[!TIP] New: We now recommend using the BM25 package on PyPI for a simpler, beginner-friendly experience. It includes all necessary dependencies (stemming, CLI, etc.) and provides the same high-level API shown below.

If you want to quickly search on a local file, you can use the bm25s.high_level module:

import bm25s.high_level as bm25

# Load a file (csv, json, jsonl, txt)
# For csv/jsonl, you can specify the column/key to use as document text
corpus = bm25.load("tests/data/dummy.csv", document_column="text")
# Index the corpus
retriever = bm25.index(corpus)

# Search
results = retriever.search(["your query here"], k=5)
for result in results[0]:
    print(result)

The load function handles file reading, while index handles tokenization, indexing, and provides a simple search interface.

Command-Line Interface

bm25s provides a terminal-based CLI for quick indexing and searching without writing Python code.

Indexing Documents

Create an index from a CSV, TXT, JSON, or JSONL file:

# Index a CSV file (uses first column by default)
bm25 index documents.csv -o my_index

# Index with a specific column
bm25 index documents.csv -o my_index -c text

# Index a text file (one document per line)
bm25 index documents.txt -o my_index

# Index a JSONL file
bm25 index documents.jsonl -o my_index -c content

If you don't specify an output directory with -o, the index will be saved to <filename>_index.

User Directory

You can save indices to a central user directory (~/.bm25s/indices/) using the -u flag:

# Save index to ~/.bm25s/indices/my_docs
bm25 index documents.csv -u -o my_docs

# Search using the user directory
bm25 search -u -i my_docs "your query"

Searching

Search an existing index with a query using -i (or --index):

# Basic search (returns top 10 results)
bm25 search -i my_index "what is machine learning?"

# Search with full path
bm25 search -i ./path/to/my_index "your query here"

# Return more results
bm25 search -i my_index "your query here" -k 20

# Save results to a JSON file
bm25 search -i my_index "your query here" -s results.json

Interactive Index Picker

When using -u without specifying an index name, an interactive picker is displayed (requires bm25s[cli]):

# Install CLI extras for colored interactive picker
pip install "bm25s[cli]"

# Interactive picker will show available indices
bm25 search -u "your query"

Example Workflow

Basic usage (index saved to current directory):

# 1. Create a simple text file with documents
echo -e "Machine learning is a subset of AI\nDeep learning uses neural networks\nNatural language processing handles text" > docs.txt

# 2. Index the documents
bm25 index docs.txt -o my_index

# 3. Search the index
bm25 search -i my_index "what is AI?"

With user directory (indices saved to ~/.bm25s/indices/):

# Index to user directory
bm25 index docs.txt -u -o ml_docs

# Search from user directory
bm25 search -u -i ml_docs "what is AI?"

# Or use the interactive picker
bm25 search -u "what is AI?"

CLI Reference

Command	Flag	Description
`index`	`-o, --output`	Output directory for the index
`index`	`-c, --column`	Column name for document text
`index`	`-u, --user`	Save to user directory (`~/.bm25s/indices/`)
`search`	`-i, --index`	Path to index (or name if using `-u`)
`search`	`-k, --top-k`	Number of results (default: 10)
`search`	`-s, --save`	Save results to JSON file
`search`	`-u, --user`	Use user directory; shows picker if `-i` omitted

Flexibility

bm25s provides a flexible API that allows you to customize the BM25 model and the tokenization process. Here are some of the options you can use:

# You can provide a list of queries instead of a single query
queries = ["What is a cat?", "is the bird a dog?"]

# Provide your own stopwords list if you don't like the default one
stopwords = ["a", "the"]

# For stemming, use any function that is callable on each word list
stemmer_fn = lambda lst: [word for word in lst]

# Tokenize the queries
query_token_ids = bm25s.tokenize(queries, stopwords=stopwords, stemmer=stemmer_fn)

# If you want the tokenizer to return strings instead of token ids, you can do this
query_token_strs = bm25s.tokenize(queries, return_ids=False)

# You can use a different corpus for retrieval, e.g., titles instead of full docs
titles = ["About Cat", "About Dog", "About Bird", "About Fish"]

# You can also choose to only return the documents and omit the scores
# note: if you pass a new corpus here, it must have the same length as your indexed corpus
results = retriever.retrieve(query_token_ids, corpus=titles, k=2, return_as="documents")

# The documents are returned as a numpy array of shape (n_queries, k)
for i in range(results.shape[1]):
    print(f"Rank {i+1}: {results[0, i]}")

Memory Efficient Retrieval

bm25s is designed to be memory efficient. You can use the mmap option to load the BM25 index as a memory-mapped file, which allows you to load the index without loading the full index into memory. This is useful when you have a large index and want to save memory:

# Create a BM25 index
# ...

# let's say you have a large corpus
corpus = [
    "a very long document that is very long and has many words",
    "another long document that is long and has many words",
    # ...
]
# Save the BM25 index to a file
retriever.save("bm25s_very_big_index", corpus=corpus)

# Load the BM25 index as a memory-mapped file, which is memory efficient
# and reduce overhead of loading the full index into memory
retriever = bm25s.BM25.load("bm25s_very_big_index", mmap=True)

For an example of how to use retrieve using the mmap=True mode, check out examples/retrieve_nq.py.

Tokenization

In addition to using the simple function bm25s.tokenize, you can also use the Tokenizer class to customize the tokenization process. This is useful when you want to use a different tokenizer, or when you want to use a different tokenization process for queries and documents:

from bm25s.tokenization import Tokenizer

corpus = [
      "a cat is a feline and likes to purr",
      "a dog is the human's best friend and loves to play",
      "a bird is a beautiful animal that can fly",
      "a fish is a creature that lives in water and swims",
]

# Pick your favorite stemmer, and pass 
stemmer = None
stopwords = ["is"]
splitter = lambda x: x.split() # function or regex pattern
# Create a tokenizer
tokenizer = Tokenizer(
      stemmer=stemmer, stopwords=stopwords, splitter=splitter
)

corpus_tokens = tokenizer.tokenize(corpus)

# let's see what the tokens look like
print("tokens:", corpus_tokens)
print("vocab:", tokenizer.get_vocab_dict())

# note: the vocab dict will either be a dict of `word -> id` if you don't have a stemmer, and a dict of `stemmed word -> stem id` if you do.
# You can save the vocab. it's fine to use the same dir as your index if filename doesn't conflict
tokenizer.save_vocab(save_dir="bm25s_very_big_index")

# loading:
new_tokenizer = Tokenizer(stemmer=stemmer, stopwords=[], splitter=splitter)
new_tokenizer.load_vocab("bm25s_very_big_index")
print("vocab reloaded:", new_tokenizer.get_vocab_dict())

# the same can be done for stopwords
print("stopwords before reload:", new_tokenizer.stopwords)
tokenizer.save_stopwords(save_dir="bm25s_very_big_index")
new_tokenizer.load_stopwords("bm25s_very_big_index")
print("stopwords reloaded:", new_tokenizer.stopwords)

You can find advanced examples in examples/tokenizer_class.py, including how to:

Pass a stemmer, stopwords, and splitter function/regex pattern
Control whether vocabulary is updated by tokenizer.tokenize calls or not (by default, it will only be updated during the first call)
Reset the tokenizer to its initial state with tokenizer.reset_vocab()
Use the tokenizer in generator mode to save memory by yielding one document at a time.
Pass different outputs of the tokenizer to the BM25.retrieve function.

Variants

You can use the following variants of BM25 in bm25s (see Kamphuis et al. 2020 for more details):

Original implementation (method="robertson") - we set idf>=0 to avoid negatives
ATIRE (method="atire")
BM25L (method="bm25l")
BM25+ (method="bm25+")
Lucene (method="lucene")

By default, bm25s uses method="lucene", which is Lucene's BM25 implementation (exact version). You can change the method by passing the method argument to the BM25 constructor:

# The IR book recommends default values of k1 between 1.2 and 2.0, and b=0.75
retriever = bm25s.BM25(method="robertson", k1=1.5, b=0.75)

# For BM25+, BM25L, you need a delta parameter (default is 0.5)
retriever = bm25s.BM25(method="bm25+", delta=1.5)

# You can also choose a different "method" for idf, while keeping the default for the rest
# for example, this is equivalent to rank-bm25 when `epsilon=0`
retriever = bm25s.BM25(method="atire", idf_method="robertson")
# and this is equivalent to bm25-pt
retriever = bm25s.BM25(method="atire", idf_method="lucene")

Hugging Face Integration

bm25 can naturally work with Hugging Face's huggingface_hub, allowing you to load and save to the model hub. This is useful for sharing BM25 indices and using community models.

First, make sure you have a valid access token for the Hugging Face model hub. This is needed to save models to the hub, or to load private models. Once you created it, you can add it to your environment variables (e.g. in your .bashrc or .zshrc):

export HUGGING_FACE_HUB_TOKEN="hf_..."

Now, let's install the huggingface_hub library:

pip install huggingface_hub

Let's see how to use BM25SHF.save_to_hub to save a BM25 index to the Hugging Face model hub:

import os
import bm25s
from bm25s.hf import BM25HF

# Create a BM25 index
retriever = BM25HF()
# Create your corpus here
corpus = [
    "a cat is a feline and likes to purr",
    "a dog is the human's best friend and loves to play",
    "a bird is a beautiful animal that can fly",
    "a fish is a creature that lives in water and swims",
]
corpus_tokens = bm25s.tokenize(corpus)
retriever.index(corpus_tokens)

# Set your username and token
user = "your-username"
token = os.environ["HF_TOKEN"]
retriever.save_to_hub(f"{user}/bm25s-animals", token=token, corpus=corpus)
# You can also save it publicly with private=False

Then, you can use the following code to load a BM25 index from the Hugging Face model hub:

import bm25s
from bm25s.hf import BM25HF

# Load a BM25 index from the Hugging Face model hub
user = "your-username"
retriever = BM25HF.load_from_hub(f"{user}/bm25s-animals")

# you can specify revision and load_corpus=True if needed
retriever = BM25HF.load_from_hub(
    f"{user}/bm25s-animals", revision="main", load_corpus=True
)

# if you want a low-memory usage, you can load as memory map with `mmap=True`
retriever = BM25HF.load_from_hub(
    f"{user}/bm25s-animals", load_corpus=True, mmap=True
)

# Query the corpus
query = "does the fish purr like a cat?"

# Tokenize the query
query_tokens = bm25s.tokenize(query)

# Get top-k results as a tuple of (doc ids, scores). Both are arrays of shape (n_queries, k)
results, scores = retriever.retrieve(query_tokens, k=2)

For a complete example, check out:

examples/index_to_hf.py for indexing a corpus and upload to Huggingface Hub
examples/retrieve_from_hf.py for loading an index alongside corpus from Huggingface Hub and querying it.
examples/mcp/create_index.py for creating a test index for the MCP server.
examples/mcp/verify_server.py for programmatically verifying the MCP server.

MCP Server

bm25s comes with a built-in Model Context Protocol (MCP) server, allowing you to expose your BM25 index as a tool for LLMs and other agents.

Installation

To use the MCP server, you need to install bm25s with the mcp extra. We recommend using uv for managing your environment:

# Create a virtual environment
uv venv

# Activate the virtual environment
source .venv/bin/activate

# Install bm25s with the mcp extra
uv pip install "bm25s[mcp]"

# or locally:
uv pip install -e ".[mcp]"

Launching the Server

You can launch the MCP server using the bm25 CLI:

source .venv/bin/activate
bm25 mcp launch --port 8000 --index-dir /path/to/your/index

Example

For example, you can create a test index and launch the server with:

source .venv/bin/activate
python examples/index_nq.py # creates bm25s_indices/nq
bm25 mcp launch --port 8000 --index-dir ./bm25s_indices/nq

Then, you can verify the server with:

python examples/mcp/verify_server.py

Tools

The server exposes the following tools:

retrieve(query: str, k: int = 10): Retrieves the top-k documents for a given query.
get_info(): Returns information about the loaded index (vocabulary size, number of documents, backend).

Example Usage with Claude Desktop

To use bm25s with Claude Desktop, add the following to your claude_desktop_config.json (located at ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "bm25s": {
      "command": "/absolute/path/to/uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/PARENT/FOLDER/bm25s",
        "run",
        "bm25",
        "mcp",
        "launch",
        "--index-dir",
        "/absolute/path/to/your/index"
      ]
    }
  }
}

To find the absolute path to uv, you can run which uv in the terminal.

Comparison

Here are some benchmarks comparing bm25s to other popular BM25 implementations. We compare the following implementations:

bm25s: Our implementation of BM25 in pure Python, powered by Numpy and sparse matrices.
rank-bm25 (Rank): A popular Python implementation of BM25.
bm25_pt (PT): A Pytorch implementation of BM25.
elasticsearch (ES): Elasticsearch with BM25 configurations.

OOM means the implementation ran out of memory during the benchmark.

Throughput (Queries per second)

We compare the throughput of the BM25 implementations on various datasets. The throughput is measured in queries per second (QPS), on a single-threaded Intel Xeon CPU @ 2.70GHz (found on Kaggle). For BM25S, we take the average of 10 runs. Instances exceeding 60 queries/s are in bold.

Dataset	BM25S	Elastic	BM25-PT	Rank-BM25
arguana	573.91	13.67	110.51	2
climate-fever	13.09	4.02	OOM	0.03
cqadupstack	170.91	13.38	OOM	0.77
dbpedia-entity	13.44	10.68	OOM	0.11
fever	20.19	7.45	OOM	0.06
fiqa	507.03	16.96	20.52	4.46
hotpotqa	20.88	7.11	OOM	0.04
msmarco	12.2	11.88	OOM	0.07
nfcorpus	1196.16	45.84	256.67	224.66
nq	41.85	12.16	OOM	0.1
quora	183.53	21.8	6.49	1.18
scidocs	767.05	17.93	41.34	9.01
scifact	952.92	20.81	184.3	47.6
trec-covid	85.64	7.34	3.73	1.48
webis-touche2020	60.59	13.53	OOM	1.1

More detailed benchmarks can be found in the bm25-benchmarks repo.

Disk usage

bm25s is designed to be lightweight. This means the total disk usage of the package is minimal, as it only requires wheels for numpy (18MB), and the package itself is less than 100KB. After installation, the full virtual environment takes more space than rank-bm25 but less than pyserini and bm25_pt:

Package	Disk Usage
venv (no package)	21MB
`bm25s` (ours)	51MB
`rank-bm25`	51MB
`bm25s` (w/ core)	188MB
`elastic`	1183MB
`bm25_pt`	5346MB
`pyserini`	6976MB

The disk usage of the virtual environments is calculated using the following command:

$ du -sh venv* --block-size=1MB
6976    conda-env-pyserini
5346    venv-bm25-pt
51     venv-bm25s
188     venv-bm25s-core
21      venv-empty
51      venv-rank-bm25

For pyserini, we use the recommended installation with conda environment to account for Java dependencies.

Optimized RAM usage

bm25s allows considerable memory saving through the use of memory-mapping, which allows the index to be stored on disk and loaded on demand.

Using the index_nq.py to create an index, we can retrieve with:

examples/retrieve_nq.py: setting mmap=False in the main function to load the index in memory, and mmap=True to load the index as a memory-mapped file.
examples/retrieve_nq_with_batching.py: This takes it a step further by batching the retrieval process, which allows for reloading the index after each batch (see Mmap+Reload below). This is useful when you have a large index and want to save memory.

We show the following results on the NQ dataset (2M+ documents, 100M+ tokens):

Method	Load Index (s)	Retrieval (s)	RAM post-index (GB)	RAM post-retrieve (GB)
In-memory	8.61	21.09	4.36	4.45
Memory-mapped	0.53	20.22	0.49	2.16
Mmap+Reload	0.48	20.96	0.49	0.70

We can see that memory-mapping the index allows for a significant reduction in memory usage, with comparable retrieval times.

Similarly, for MSMARCO (8M+ documents, 300M+ tokens), we show the following results (running on the validation set), although the retrieval did not complete for the in-memory case:

Method	Load Index (s)	Retrieval (s)	RAM post-index (GB)	RAM post-retrieve (GB)
In-memory	25.71	93.66	10.21	10.34
Memory-mapped	1.24	90.41	1.14	4.88
Mmap+Reload	1.17	97.89	1.14	1.38

Acknowledgement

The central idea behind the scoring mechanism in this library is originally from bm25_pt, which was a major inspiration to this project.
The API of the BM25 class is also heavily inspired by the design of BM25-pt, as well as that of rank-bm25.
The multilingual stopwords are sourced from the NLTK stopwords lists.
The numba implementation are inspired by numba implementations originally proposed by baguetter and retriv.
The function bm25s.utils.beir.evaluate is taken from the BEIR library. It follows the same license as the BEIR library, which is Apache 2.0.

Citation

If you use bm25s in your work, please use the following bibtex:

@misc{bm25s,
      title={BM25S: Orders of magnitude faster lexical search via eager sparse scoring}, 
      author={Xing Han Lù},
      year={2024},
      eprint={2407.03618},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2407.03618}, 
}

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