Queryome

Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature

Queryome is a multi-agent deep research system for biomedical literature. It combines semantic vector search, BM25 keyword search, and LLM-powered reasoning to provide comprehensive answers to complex biomedical research questions. This repository contains the command-line version of Queryome; a desktop app is available at https://www.queryome.app/.

Features

Hybrid Search: Combines FAISS vector search with BM25 keyword search for optimal retrieval
Multi-Agent Architecture: Uses specialized agents (PI Agent, SubAgent Team, Synthesizer) for planning, execution, and synthesis
Multiple Search Indices: Searches across title/abstract, author keywords, and MeSH terms
Comprehensive Reports: Generates well-structured research reports with proper citations

Installation

1. Set up Environment Variables

Export your OpenAI API key:

export OPENAI_API_KEY="your-openai-api-key-here"

You can add this to your ~/.bashrc or ~/.zshrc for persistence.

2. Download Indices

Download the pre-built search indices:

wget https://kiharalab.org/queryome/indices.tar.gz
tar -xzf indices.tar.gz

This will create an indices/ directory containing:

vector_db/ - FAISS vector index and SQLite database
- faiss.index - FAISS vector index
- articles.db - SQLite database with article metadata
bm25_title_abstract/ - BM25 index for title and abstract search
bm25_author_keywords/ - BM25 index for author keywords search
bm25_mesh_terms/ - BM25 index for MeSH terms search

3. Create Conda Environment

Create and activate a new conda environment:

conda create -n queryome python=3.10
conda activate queryome

Install the required dependencies:

pip install openai numpy torch faiss-cpu bm25s PyStemmer sentence-transformers numba

Usage

Command Line Interface

Interactive Mode:

python queryome_cli.py

Single Query:

python queryome_cli.py --query "What are the latest treatments for Type 2 diabetes?"

With Custom Log Directory:

python queryome_cli.py --query "Efficacy of metformin in elderly patients" --log-dir ./my_logs

Python API

Single Query:

from queryome import Queryome

queryome = Queryome()
result = queryome.research("What are the latest treatments for Type 2 diabetes?")
print(result)

Multiple Queries (Batch Processing):

from queryome import Queryome, batch_research

# Using the Queryome class
queryome = Queryome()
queries = [
    "Efficacy of metformin in elderly patients",
    "Side effects of insulin therapy",
    "Latest diabetes management guidelines"
]
results = queryome.research_multiple(queries)

for r in results:
    print(f"Query: {r['query']}")
    print(f"Result: {r['result']}")
    print("---")

# Or use the convenience function
results = batch_research(queries)

Configuration Options

When initializing Queryome, you can customize:

queryome = Queryome(
    log_dir="./custom_logs",           # Custom log directory
    enable_search_engine=True,          # Enable/disable search engine
    openai_api_key="your-key",          # Provide API key programmatically
    embedding_device="cuda:0"           # GPU device for embeddings
)

Authors

Pranav Punuru¹, Nabil Ibtehaz², Swagarika Giri², Harsha Srirangam², Emilia Tugolukova¹, and Daisuke Kihara^1,2,*

¹ Department of Biological Sciences, Purdue University, West Lafayette, IN 47906, USA ² Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA

* Corresponding Author Email: dkihara@purdue.edu

Citation

If you use Queryome in your research, please cite:

@article{punuru2025queryome,
  title={Queryome: Orchestrating Retrieval, Reasoning, and Synthesis across Biomedical Literature},
  author={Punuru, Pranav and Ibtehaz, Nabil and Giri, Swagarika and Srirangam, Harsha and Tugolukova, Emilia and Kihara, Daisuke},
  year={2025}
}

License

This project is developed by the Kihara Lab at Purdue University.

Contact

For questions or support, please contact:

Email: dkihara@purdue.edu
Lab Website: https://kiharalab.org

kiharalab/queryome_cli

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