bmlib

Shared Python library for biomedical literature tools — LLM abstraction, quality assessment, transparency analysis, full-text retrieval, publication ingestion, and database utilities.

Version: 0.2.1 | License: AGPL-3.0-or-later | Python: >=3.11

Installation

# Core (only jinja2 dependency)
pip install bmlib

# Editable install with all extras
uv pip install -e ".[all]"

Optional dependency groups

Group	Install command	Provides
anthropic	pip install bmlib[anthropic]	Anthropic Claude LLM provider
ollama	pip install bmlib[ollama]	Ollama local LLM provider
openai	pip install bmlib[openai]	OpenAI, DeepSeek, Mistral, Gemini, and OpenAI-compatible providers
postgresql	pip install bmlib[postgresql]	PostgreSQL database backend
transparency	pip install bmlib[transparency]	Transparency analysis (httpx)
publications	pip install bmlib[publications]	Publication ingestion and sync (httpx)
dev	pip install bmlib[dev]	pytest, pytest-cov, ruff
all	pip install bmlib[all]	All of the above

Modules

Module	Description
bmlib.db	Thin database abstraction (SQLite + PostgreSQL) with pure functions over DB-API connections
bmlib.llm	Unified LLM client with pluggable providers (Anthropic, OpenAI, Ollama, DeepSeek, Mistral, Gemini)
bmlib.templates	Jinja2-based prompt template engine with user-override directory fallback
bmlib.agents	Base agent class for LLM-driven tasks with template rendering and JSON parsing
bmlib.quality	3-tier quality assessment pipeline for biomedical publications (metadata → LLM classifier → deep assessment)
bmlib.transparency	Multi-API transparency and bias analysis (PubMed, CrossRef, EuropePMC, OpenAlex, ClinicalTrials.gov)
bmlib.publications	Publication ingestion from PubMed, bioRxiv, medRxiv, and OpenAlex with deduplication and sync
bmlib.fulltext	Full-text retrieval (Europe PMC → Unpaywall → DOI), JATS XML parsing, and disk-based caching

Quick Start

Database

from bmlib.db import connect_sqlite, execute, fetch_all, transaction

conn = connect_sqlite("~/.myapp/data.db")
with transaction(conn):
    execute(conn, "INSERT INTO papers (doi, title) VALUES (?, ?)", ("10.1101/x", "A paper"))
rows = fetch_all(conn, "SELECT * FROM papers")

LLM

from bmlib.llm import LLMClient, LLMMessage

client = LLMClient(default_provider="ollama")
response = client.chat(
    messages=[LLMMessage(role="user", content="Summarise this paper.")],
    model="ollama:medgemma4B_it_q8",
)
print(response.content)

Model strings use the format "provider:model_name":

"anthropic:claude-sonnet-4-20250514"
"openai:gpt-4o"
"ollama:medgemma4B_it_q8"
"deepseek:deepseek-chat"
"mistral:mistral-large-latest"
"gemini:gemini-2.0-flash"

Publication Sync

from datetime import date
from bmlib.db import connect_sqlite
from bmlib.publications import sync

conn = connect_sqlite("publications.db")
report = sync(
    conn,
    sources=["pubmed", "biorxiv"],
    date_from=date(2025, 1, 1),
    date_to=date(2025, 1, 7),
    email="researcher@example.com",
)
print(f"Added: {report.records_added}, Merged: {report.records_merged}")

Full-Text Retrieval

from bmlib.fulltext import FullTextService, FullTextCache

service = FullTextService(email="researcher@example.com")
result = service.fetch_fulltext(pmc_id="PMC7614751", doi="10.1234/example")

if result.source == "europepmc" and result.html:
    cache = FullTextCache()  # uses platform default directory
    cache.save_html(result.html, "PMC7614751")

Quality Assessment

from bmlib.llm import LLMClient
from bmlib.quality import QualityManager

llm = LLMClient()
manager = QualityManager(
    llm=llm,
    classifier_model="anthropic:claude-3-haiku-20240307",
    assessor_model="anthropic:claude-sonnet-4-20250514",
)

assessment = manager.assess(
    title="A Randomized Controlled Trial of ...",
    abstract="We conducted a double-blind RCT ...",
    publication_types=["Randomized Controlled Trial"],
)
print(assessment.study_design, assessment.quality_tier)

Transparency Analysis

from bmlib.transparency import TransparencyAnalyzer

analyzer = TransparencyAnalyzer(email="researcher@example.com")
result = analyzer.analyze("doc-001", doi="10.1038/s41586-024-00001-0")
print(result.transparency_score, result.risk_level)

Development

# Install with dev dependencies
uv pip install -e ".[all]"

# Run tests
pytest tests/ -v

# Lint and format
ruff check .
ruff format --check .

Documentation

Full API documentation is available in docs/manual/.

License

AGPL-3.0-or-later