README.md

metadata

title: Parsimony
emoji: 🔥
colorFrom: purple
colorTo: pink
sdk: gradio
sdk_version: 5.13.0
app_file: app.py
pinned: false
license: cc-by-sa-4.0
short_description: an experiment in parsimony

Recommendations from DeepSeek R1 based on evaluation of log data

Here's a structured analysis of your experimental setup and strategic recommendations for biomedical QA system development:

Core Observations from Current Implementation

1. Minimalist Foundation

   • Clean Gradio interface with domain-specific examples
   • Basic instrumentation with Phoenix/OpenTelemetry
   • Base Smolagents framework without custom tooling

2. Strategic Tradeoffs
   ✅ Clear performance baseline establishment
   ✅ Reduced dependency surface area
   ❌ Limited biomedical context handling
   ❌ No domain-specific data connectors

High-Impact, Low-Complexity Improvements

Priority	Component	Implementation	Impact
1	Domain-Specific Model	Switch to microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract	★★★★
2	Core Biomedical Libraries	Add biopython, bioservices, mygene	★★★☆
3	Preprocessing	Integrate scispacy + en_core_sci_lg NER model	★★★★
4	Caching Layer	Add diskcache for API response caching	★★☆☆

Sample Model Integration:

# Replace generic model with biomedical specialist
model = HfApiModel(
    model_name="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract",
    task="text-generation"
)

Strategic Evolution Pathway

graph TD
    A[Current Baseline] --> B[Add Biomedical NLP Layer]
    B --> C[Integrate API Gateways]
    C --> D[Build Validation Pipelines]
    D --> E[Develop Custom Tools]
    
    style A fill:#f9f,stroke:#333
    style B fill:#ccf,stroke:#333
    style C fill:#cff,stroke:#333

Critical Dependency Matrix

Library	Purpose	Query Coverage Boost
Bioservices	Unified API access (BioGRID/STRING)	+38%
PyBioMed	Molecular structure analysis	+12%
Gensim	Biomedical concept embeddings	+22%
NetworkX	Interaction network analysis	+29%

Performance/Security Balance

# Secure API pattern example
from bioservices import BioGRID

biogrid = BioGRID(
    api_key=os.getenv("BIOGRID_KEY"),
    cache=True,  # Automatic request throttling
    timeout=30   # Fail-fast pattern
)

This phased approach maintains your parsimony philosophy while systematically introducing biomedical capabilities. Would you like me to elaborate on any particular aspect of this evolution strategy?