A lightweight, reproducible toolkit for LLM-based query reformulation

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Features

• Single Prompt Bank (YAML) with metadata
• Simple DataLoader: Dependency-free file loading for queries, qrels, and contexts
• Format Loaders: Optional BEIR, MS MARCO, and BRIGHT format loaders in querygym.loaders
• OpenAI-compatible LLM client (works with any OpenAI API–compatible endpoint)
• Pyserini optional: either pass contexts (JSONL) or pass a retriever instance to build contexts
• Export-only: emits reformulated queries; optionally generates a bash script for Pyserini + trec_eval

Supported Methods

QueryGym implements the following query reformulation methods:

Method	Description	Paper
GenQR	Generic keyword expansion using LLM	Wang et al., 2023
GenQR Ensemble	Ensemble of 10 instruction variants for diverse keyword expansion	Dhole & Agichtein, 2024
Query2Doc	Generates pseudo-documents from LLM knowledge	Wang et al., 2023
QA Expand	Question-answer based expansion with sub-questions	Seo et al., 2025
MuGI	Multi-granularity information expansion with adaptive concatenation	Zhang et al., 2024
LameR	Context-based passage synthesis using retrieved documents	Mackie et al., 2023
CSQE	Context-based sentence-level query expansion (KEQE + CSQE)	Lee et al., 2024
ThinkQE	Multi-round reasoning-based query expansion with corpus feedback	Le et al., 2025
Query2E	Query to entity/keyword expansion	Jagerman et al., 2023
ReFormeR	Pattern-based, document-conditioned reformulation via learned transformation rules	Bigdeli et al., 2026

For detailed usage and parameters, see the Methods Reference.

Installation

Option 1: Install from PyPI

pip install querygym

Option 2: Use Docker (Recommended for Quick Start)

# GPU version (default)
docker pull ghcr.io/ls3-lab/querygym:latest
docker run -it --gpus all ghcr.io/ls3-lab/querygym:latest

# CPU version (lightweight)
docker pull ghcr.io/ls3-lab/querygym:cpu
docker run -it ghcr.io/ls3-lab/querygym:cpu

# Or use Docker Compose
docker compose run --rm querygym

📖 Docker Setup: See DOCKER_SETUP.md for quick start or the full Docker guide for detailed usage.

Quickstart

Python API (Recommended)

import querygym as qg

# Load data
queries = qg.load_queries("queries.tsv")
qrels = qg.load_qrels("qrels.txt")
contexts = qg.load_contexts("contexts.jsonl")

# Create reformulator
reformulator = qg.create_reformulator("genqr_ensemble", model="gpt-4")

# Reformulate
results = reformulator.reformulate_batch(queries)

# Save
qg.DataLoader.save_queries(
    [qg.QueryItem(r.qid, r.reformulated) for r in results],
    "reformulated.tsv"
)

CLI

pip install -e .[hf,beir,dev]
export OPENAI_API_KEY=sk-...

# Run a method (e.g., genqr_ensemble)
querygym run --method genqr_ensemble \
  --queries-tsv queries.tsv \
  --output-tsv reformulated.tsv \
  --cfg-path querygym/config/defaults.yaml

Loading Datasets

BEIR:

import querygym as qg

# Download with BEIR library
from beir.datasets.data_loader import GenericDataLoader
data_path = GenericDataLoader("nfcorpus").download_and_unzip()

# Load with querygym
queries = qg.loaders.beir.load_queries(data_path)
qrels = qg.loaders.beir.load_qrels(data_path)

MS MARCO:

import querygym as qg

# Load from local files (download with ir_datasets)
queries = qg.loaders.msmarco.load_queries("queries.tsv")
qrels = qg.loaders.msmarco.load_qrels("qrels.tsv")

BRIGHT:

import querygym as qg
from datasets import load_dataset

examples  = load_dataset("xlangai/BRIGHT", "examples")["biology"]
documents = load_dataset("xlangai/BRIGHT", "documents")["biology"]

queries           = qg.loaders.bright.load_queries(examples)           
reasoning_queries = qg.loaders.bright.load_reasoning_queries(examples)  
qrels        = qg.loaders.bright.load_qrels(examples)
corpus       = qg.loaders.bright.load_corpus(documents)

Examples

See the examples directory for:

• Code snippets - Quick reference examples
• Docker examples - Containerized workflows with Jupyter notebooks
• QueryGym + Pyserini - Complete retrieval pipelines
• Methods Reference - Complete guide to all query reformulation methods

Check examples/README.md for the full guide.

Contributing

We welcome contributions! Here's how you can help:

Adding a New Prompt

1. Edit querygym/prompt_bank.yaml
2. Add an entry with fields: id, method_family, version, introduced_by, license, authors, tags, template:{system,user}, notes

Adding a New Method

1. Create a class under querygym/methods/*.py
2. Subclass BaseReformulator, annotate VERSION, and register with @register_method("name")
3. Pull templates via PromptBank.render(prompt_id, query=...)

Reporting Issues

• Found a bug? Open an issue
• Have a feature request? We'd love to hear it!

For detailed development guidelines, see the Contributing Guide in our documentation.

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Citation

If you use QueryGym in your research, please cite:

@inproceedings{10.1145/3774905.3793135,
  author    = {Bigdeli, Amin and Hamidi Rad, Radin and Incesu, Mert and Arabzadeh, Negar and Clarke, Charles and Bagheri, Ebrahim},
  title     = {QueryGym: A Toolkit for Reproducible LLM-Based Query Reformulation},
  booktitle = {Companion Proceedings of the ACM Web Conference 2026},
  series    = {WWW Companion '26},
  year      = {2026},
  pages     = {196--199},
  publisher = {Association for Computing Machinery},
  doi       = {10.1145/3774905.3793135},
  url       = {https://doi.org/10.1145/3774905.3793135}
}