bsparse 0.3.1

toolkit for creating and searching sparse representations

bsparse

bsparse is a toolkit for creating, indexing, and searching learned sparse representations

Usage examples

# Recommended way to install requirements:
# (using pip only works too, but uv is much faster)
pipx install uv
# Create virtual environment
uv venv venv
# Activate
source venv/bin/activate
# Install requirements
uv pip install -r requirements.txt

The base package only needs lightweight dependencies: indexing and searching with the anserini/seismic backends start from already-encoded JSONL files, so they work without torch. Optional dependency groups cover the rest:

• encode: torch, transformers, etc. for encoding text into sparse representations
• anserini: pyjnius for the Anserini search backend (a JAR is also required; see below)
• seismic: pyseismic-lsr for the Seismic backend
• all: everything above

# e.g., install everything needed to encode and to search with the Seismic backend
uv pip install 'bsparse[encode,seismic]'

# Request access to splade-v3: https://huggingface.co/naver/splade-v3
# Get your huggingface API token and then:
export HF_TOKEN="the token"

# load Python virtual environment
source venv/bin/activate

# optional: spot check output from a model
python -m bsparse.cli check --text "tesla net worth"

# create query representations:
python -m bsparse.cli encode --out nfcorpus-queries.jsonl \
  --dataset irds --type query --name beir/nfcorpus  --batch-size 64

# create doc representations:
python -m bsparse.cli encode --out nfcorpus-docs.jsonl \
  --dataset irds --type doc --name beir/nfcorpus  --batch-size 64

# search and evaluate without building an index:
python -m bsparse.cli memsearch --out nfcorpus.run --docs nfcorpus-docs.jsonl --queries nfcorpus-queries.jsonl --qrels beir/nfcorpus/test


# alternatively, you can build an index and search it

# 1) setup: compile ScaledJsonVectorCollection.java and add it to anserini-1.0.0-fatjar.jar
$ wget -c https://repo1.maven.org/maven2/io/anserini/anserini/1.0.0/anserini-1.0.0-fatjar.jar
$ cd java
$ javac -cp ../anserini-1.0.0-fatjar.jar io/anserini/collection/*.java
$ cp ../anserini-1.0.0-fatjar.jar ../anserini-1.0.0-fatjar-bsparse.jar
$ jar uf ../anserini-1.0.0-fatjar-bsparse.jar io/anserini/collection/*.class

# 2) build index
java -cp anserini-1.0.0-fatjar-AY.jar  io.anserini.index.IndexCollection \
  -generator DefaultLuceneDocumentGenerator -impact -pretokenized \
  -threads 16 -collection ScaledJsonVectorCollection \
  -input /path/to/encoded-text -index /path/to/encoded-text-index

# 3) search index
# Create sparse query representations in `$QUERY_VECTORS` and create an index in `$INDEX`, then:
python -m bsparse.cli search --index $INDEX --queries $QUERY_VECTORS --out test.run --topk 1000

Seismic backend

Seismic is an alternative backend that indexes learned sparse representations natively in Python (no Java/JAR required). The encoded JSONL files produced by encode are already in the format Seismic expects, so the same doc/query files work for both backends.

# install the Seismic Python bindings (optional dependency; only needed for this backend)
uv pip install 'bsparse[seismic]'  # or: uv pip install pyseismic-lsr
# for best performance, build against your CPU instead:
# RUSTFLAGS="-C target-cpu=native" uv pip install --no-binary :all: pyseismic-lsr

# 1) build a Seismic index from encoded docs
python -m bsparse.cli index --backend seismic --input nfcorpus-docs.jsonl --index $INDEX
# --input accepts multiple files, gzipped (.gz) input, and directories of .jsonl/.jsonl.gz files;
# if the in-memory API gives you trouble, --build-method file falls back to concatenating
# the inputs into a temporary uncompressed JSONL file and using Seismic's file-based build
#
# note: seismic appends ".index.seismic" to the path, so the on-disk file is $INDEX.index.seismic;
# search --index accepts either the build-time path or the full on-disk filename
#
# indexing hyperparameters are flags with defaults, e.g.:
#   --n-postings 3000 --centroid-fraction 0.2 --summary-energy 0.5 --max-fraction 6 --min-cluster-size 2 --nknn 0
#
# use --variant large_vocab for collections with more than 65k unique tokens

# 2) search the index and evaluate
python -m bsparse.cli search --backend seismic --index $INDEX \
  --queries nfcorpus-queries.jsonl --out test.run --topk 1000 \
  --query-cut 10 --heap-factor 0.8 --qrels beir/nfcorpus/test

# query-time thread count is index-independent and set via the environment:
#   SEISMIC_THREADS=16 python -m bsparse.cli search --backend seismic ...