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--- |
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title: Parsimony |
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emoji: 🔥 |
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colorFrom: purple |
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colorTo: pink |
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sdk: gradio |
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sdk_version: 5.13.0 |
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app_file: app.py |
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pinned: false |
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license: cc-by-sa-4.0 |
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short_description: an experiment in parsimony |
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--- |
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## **Building Towards a Smarter Agentic AI** |
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*The balance between simplicity and evolution in a rapidly advancing field.* |
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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. |
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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. |
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### **The Current State: Minimalist by Design** |
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The initial implementation was intentionally lean: |
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- **Interface**: A clean, Gradio-powered UI with domain-specific examples. |
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- **Instrumentation**: Basic monitoring using Phoenix/OpenTelemetry for telemetry insights. |
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- **Framework**: Smolagents provided a lightweight, extensible base to explore agentic capabilities. |
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This minimalist foundation allowed for: |
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✅ Establishing a clear performance baseline. |
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✅ Reducing dependency complexity to focus on core functionality. |
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❌ Acknowledging gaps in domain-specific biomedical context. |
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❌ Recognizing the absence of specialized data connectors (e.g., BioGRID or PubMed integration). |
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### **Strategic Evolution: From Foundation to Functionality** |
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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: |
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**Key Milestones in the Evolution Pathway**: |
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```mermaid |
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graph TD |
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A[Baseline] --> B[Add Biomedical NLP Layer] |
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B --> C[Integrate API Gateways] |
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C --> D[Build Validation Pipelines] |
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D --> E[Develop Custom Tools] |
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``` |
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1. **Domain-Specific Models**: Switch to specialized models like `microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract` for improved contextual understanding. |
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- *Impact*: Enhanced language processing tailored to biomedical QA tasks. |
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2. **Preprocessing Pipelines**: Add **scispacy** and **en_core_sci_lg** for named entity recognition (NER) and text preprocessing. |
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- *Impact*: Improved ability to identify biomedical entities and relationships in unstructured text. |
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3. **Critical Libraries**: Introduce **bioservices**, **PyBioMed**, and **NetworkX** for API access, molecular analysis, and interaction networks. |
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- *Impact*: Enable integration with BioGRID, STRING, and other key data sources. |
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4. **Caching for Efficiency**: Implement tools like `diskcache` to optimize API calls and ensure faster response times. |
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- *Impact*: Reduced latency and cost. |
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### **Key Drivers for Lean Evolution** |
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This approach embodies the principles of lean design: |
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- **Start with What’s Necessary**: Focus on baseline performance before scaling complexity. |
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- **Iterate Responsibly**: Introduce new capabilities (e.g., biomedical NLP or validation pipelines) only when they add measurable value. |
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- **Optimize for Flexibility**: Leverage OpenSource tools like **Smolagents** and **Phoenix** to experiment and adapt quickly. |
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### **Insights from the Journey** |
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Here’s what this process has taught me: |
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1. **Simplicity is a Strength**: A lean start lets you identify what works without the noise of unnecessary features. |
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2. **Feedback Is Essential**: Tools like Phoenix help monitor system performance, guiding refinements with real-world data. |
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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. |
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### **Acknowledging OpenSource Inspiration** |
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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. |
