Search Query is a Python package designed to load, lint, translate, save, improve, and automate academic literature search queries. It is extensible and currently supports PubMed, EBSCOHost, and Web of Science. The package can be used programmatically, through the command line, or as a pre-commit hook. It has zero dependencies and integrates in a variety of environments. The parsers and linters are battle-tested on peer-reviewed searchRxiv queries.

Installation

To install search-query, run:

pip install search-query

Quickstart

Creating a query programmatically is simple:

from search_query import OrQuery, AndQuery

# Typical building-blocks approach
digital_synonyms = OrQuery(["digital", "virtual", "online"], field="abstract")
work_synonyms = OrQuery(["work", "labor", "service"], field="abstract")
query = AndQuery([digital_synonyms, work_synonyms])

A query can also be parsed from a string or a JSON search file (see the overview of platform identifiers)

from search_query.parser import parse

query_string = '("digital health"[Title/Abstract]) AND ("privacy"[Title/Abstract])'
query = parse(query_string, platform="pubmed")

The built-in linter functionality validates queries by identifying syntactical errors:

from search_query.parser import parse

query_string = '("digital health"[Title/Abstract]) AND ("privacy"[Title/Abstract]'
query = parse(query_string, platform="pubmed")
# Output:
# ❌ Fatal: unbalanced-parentheses (PARSE_0002)
#   - Unbalanced opening parenthesis
#   Query: ("digital health"[Title/Abstract]) AND ("privacy"[Title/Abstract]
#                                                ^^^

Once a query object is created, it can be translated for different databases. The translation illustrates how the search for Title/Abstract is split into two elements:

from search_query.parser import parse

query_string = '("digital health"[Title/Abstract]) AND ("privacy"[Title/Abstract])'
pubmed_query = parse(query_string, platform="pubmed")
wos_query = pubmed_query.translate(target_syntax="wos")
print(wos_query.to_string())
# Output:
# (AB="digital health" OR TI="digital health") AND (AB="privacy" OR TI="privacy")

The translated query can be saved as follows:

from search_query import SearchFile

search_file = SearchFile(
    filepath="search-file.json",
    search_string=wos_query.to_string(),
    platform="wos",
    version="1",
    authors=[{"name": "Tom Brady"}],
    record_info={},
    date={}
)

search_file.save()

For a more detailed overview of the package’s functionality, see the documentation.

Demo

A Jupyter Notebook demo (hosted on Binder) is available here:

Encounter a problem?

Bug reports or issues can be submitted via the issue tracker or by contacting the developers.

How to cite

Eckhardt, P., Ernst, K., Fleischmann, T., Geßler, A., Schnickmann, K., Thurner, L., and Wagner, G. "search-query: An Open-Source Python Library for Academic Search Queries".

The package was developed as part of Bachelor's theses:

• Fleischmann, T. (2025). Advances in literature search queries: Validation and translation of search strings for EBSCOHost. Otto-Friedrich-University of Bamberg.
• Geßler, A. (2025). Design of an Emulator for API-based Academic Literature Searches. Otto-Friedrich-University of Bamberg.
• Schnickmann, K. (2025). Validating and Parsing Academic Search Queries: A Design Science Approach. Otto-Friedrich-University of Bamberg.
• Eckhardt, P. (2025). Advances in literature searches: Evaluation, analysis, and improvement of Web of Science queries. Otto-Friedrich-University of Bamberg.
• Ernst, K. (2024). Towards more efficient literature search: Design of an open source query translator. Otto-Friedrich-University of Bamberg.

Alternative tools

This Python package is designed for programmatic and CLI-based use, as well as for integration into other literature management tools. For different scenarios, the following related tools may be helpful:

• LitSonar
• Polyglot

License

This project is distributed under the MIT License.