File size: 4,347 Bytes
033421d cddd35a 9a66f8f cddd35a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
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
---
## **Building Towards a Smarter Agentic AI**
*The balance between simplicity and evolution in a rapidly advancing field.*
Developing agentic AI systems is a fascinating challenge, particularly when focusing on the delicate balance between **lean design** and **scalable evolution**. My recent experimentation with a prototype—powered by **Smolagents** and instrumented via **Phoenix/OpenTelemetry** — has reinforced some valuable principles about starting small and building incrementally.
This isn't a finished product; it’s a **work in progress**. But that’s where the real insights come from—learning to make purposeful decisions at each step while keeping future growth in mind.
---
### **The Current State: Minimalist by Design**
The initial implementation was intentionally lean:
- **Interface**: A clean, Gradio-powered UI with domain-specific examples.
- **Instrumentation**: Basic monitoring using Phoenix/OpenTelemetry for telemetry insights.
- **Framework**: Smolagents provided a lightweight, extensible base to explore agentic capabilities.
This minimalist foundation allowed for:
✅ Establishing a clear performance baseline.
✅ Reducing dependency complexity to focus on core functionality.
❌ Acknowledging gaps in domain-specific biomedical context.
❌ Recognizing the absence of specialized data connectors (e.g., BioGRID or PubMed integration).
---
### **Strategic Evolution: From Foundation to Functionality**
With the baseline established, the next phase focuses on layering **biomedical context** and **domain-specific capabilities** into the system, guided by a phased and deliberate approach:
**Key Milestones in the Evolution Pathway**:
```mermaid
graph TD
A[Baseline] --> B[Add Biomedical NLP Layer]
B --> C[Integrate API Gateways]
C --> D[Build Validation Pipelines]
D --> E[Develop Custom Tools]
```
1. **Domain-Specific Models**: Switch to specialized models like `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract` for improved contextual understanding.
- *Impact*: Enhanced language processing tailored to biomedical QA tasks.
2. **Preprocessing Pipelines**: Add **scispacy** and **en_core_sci_lg** for named entity recognition (NER) and text preprocessing.
- *Impact*: Improved ability to identify biomedical entities and relationships in unstructured text.
3. **Critical Libraries**: Introduce **bioservices**, **PyBioMed**, and **NetworkX** for API access, molecular analysis, and interaction networks.
- *Impact*: Enable integration with BioGRID, STRING, and other key data sources.
4. **Caching for Efficiency**: Implement tools like `diskcache` to optimize API calls and ensure faster response times.
- *Impact*: Reduced latency and cost.
---
### **Key Drivers for Lean Evolution**
This approach embodies the principles of lean design:
- **Start with What’s Necessary**: Focus on baseline performance before scaling complexity.
- **Iterate Responsibly**: Introduce new capabilities (e.g., biomedical NLP or validation pipelines) only when they add measurable value.
- **Optimize for Flexibility**: Leverage OpenSource tools like **Smolagents** and **Phoenix** to experiment and adapt quickly.
---
### **Insights from the Journey**
Here’s what this process has taught me:
1. **Simplicity is a Strength**: A lean start lets you identify what works without the noise of unnecessary features.
2. **Feedback Is Essential**: Tools like Phoenix help monitor system performance, guiding refinements with real-world data.
3. **Build for Impact, Not Features**: Every addition should serve the end user, whether it’s a researcher validating hypotheses or a clinician seeking actionable insights.
---
### **Acknowledging OpenSource Inspiration**
None of this would be possible without the incredible efforts of the **OpenSource community**. Platforms like **Hugging Face** and telemetry tools like **Arize Phoenix** empower developers to build impactful, scalable systems without reinventing the wheel. Their contributions serve as a reminder that innovation grows through collaboration. |