https://github.com/hherb/bmlib
Shared Python library for biomedical literature tools — LLM abstraction, quality assessment, transparency analysis, full-text retrieval, publication ingestion, and database utilities.
https://github.com/hherb/bmlib
biomedical europepmc fulltext library medical medrxiv pubmed python
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Shared Python library for biomedical literature tools — LLM abstraction, quality assessment, transparency analysis, full-text retrieval, publication ingestion, and database utilities.
- Host: GitHub
- URL: https://github.com/hherb/bmlib
- Owner: hherb
- License: other
- Created: 2026-02-12T23:57:57.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-04-05T12:20:50.000Z (3 months ago)
- Last Synced: 2026-04-05T14:21:28.774Z (3 months ago)
- Topics: biomedical, europepmc, fulltext, library, medical, medrxiv, pubmed, python
- Language: Python
- Homepage:
- Size: 323 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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
```bash
# 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
```python
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
```python
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
```python
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
```python
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
```python
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
```python
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
```bash
# 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/](docs/manual/index.md).
## License
AGPL-3.0-or-later