ragbio 0.2.0

A retrieval-augmented biomedical literature framework for evidence discovery, citation mapping, and downstream omics analysis

RAG-Powered Biomedical Evidence Framework (ragbio)

A reusable retrieval-augmented generation (RAG) toolkit for biomedical knowledge discovery built on PubMed literature, vector search, and Ollama-based LLMs (DeepSeek / LLaMA3).

ragbio enables study-aware ingestion, embedding, and querying of biomedical literature to support gene–disease–therapy exploration, summarization, and network visualization.

Now published as a pip-installable Python package and designed for integration into research pipelines and bioinformatics workflows.

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Overview

The RAG-powered assistant enables:

• Semantic search over PubMed abstracts
• Study-scoped literature ingestion for reproducibility
• Summarization of complex biomedical evidence using LLMs
• Citation-aware responses grounded in PubMed IDs
• Modular ingestion → embedding → retrieval pipeline
• Optional gene–disease–drug network visualization

Example questions

• Which genes are linked to oxidative stress in Alzheimer’s disease?
• What therapies target amyloid pathways according to recent literature?
• Summarize evidence connecting TP53 variants to cancer therapies.

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Architecture

User Question
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FAISS Vector Retrieval (PubMed Abstracts)
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Top-K Relevant Abstracts
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Ollama LLM (DeepSeek / LLaMA3)
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Grounded Biomedical Summary + PMIDs
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(Optional) Gene–Disease–Drug Network Visualization

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Installation

Install from PyPI (recommended)

pip install ragbio

Development install (from source)

git clone https://github.com/<your-username>/rag-gene-discovery-assistant.git
cd rag-gene-discovery-assistant
pip install -e .

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Usage

1. Ingest PubMed Literature (study-aware)

python -m ragbio.utils.rag_data_loader \
  --study Alzheimer_CaseStudy \
  --search "Alzheimer Disease AND therapy" \
  --retmax 500 \
  --retstart 0

This creates the following structure (default: data/PubMed/):

PubMed/
├── Abstracts/Alzheimer_CaseStudy/
├── Metadata/Alzheimer_CaseStudy/
├── PDFs/Alzheimer_CaseStudy/
└── Index/Alzheimer_CaseStudy/

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2. Generate Embeddings & Build FAISS Index

python -m ragbio.embeddings.embedding_engine \
  --study Alzheimer_CaseStudy

• Reads from Abstracts/<study>/
• Writes FAISS index to Index/<study>/

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3. Run RAG Queries

python -m ragbio.pipeline.rag_pipeline \
  --query "Which therapies target amyloid pathways in Alzheimer’s disease?" \
  --top_k 10 \
  --structured \
  --study Alzheimer_CaseStudy

Outputs are generated per study for clean provenance and reproducibility.

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4. Visualize Gene–Disease–Drug Networks (optional)

Launch the Streamlit app:

streamlit run ragbio/pipeline/rag_cytoscape_streamlit.py --study Alzheimer_CaseStudy

This reads structured outputs and visualizes gene–disease–drug relationships as an interactive network.

Example: Gene–disease–drug co-occurrence network derived from PubMed abstracts.

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5. Optional: Notebook Exploration

Explore example workflows in:

notebooks/RAG_GeneDiscovery_Assistant.ipynb

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Technologies Used

Category	Tools
Embeddings	Ollama embedding models (configurable)
LLMs	DeepSeek, LLaMA3 (via Ollama)
Retrieval	FAISS
Data Sources	PubMed (NCBI Entrez)
Visualization	Streamlit, Cytoscape
Language	Python 3.10+

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Design Principles

• Study-first organization for reproducibility
• Separation of concerns (ingestion ≠ embedding ≠ retrieval)
• Grounded answers with PubMed citations
• Composable modules usable outside the CLI
• Safe defaults with override via CLI or environment variables

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Future Enhancements

• Neo4j-backed gene–disease–drug knowledge graphs
• Comparative evaluation of DeepSeek vs BioGPT outputs
• Variant-level evidence integration
• API support for FastAPI / Django
• Automated citation grounding and confidence scoring
• Multi-study dashboards and comparisons