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---
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:**
```python
# Replace generic model with biomedical specialist
model = HfApiModel(
model_name="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract",
task="text-generation"
)
```
### Strategic Evolution Pathway
```mermaid
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
```python
# 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?
|