data_processing_agentic

Demonstrating the Reflection Design Pattern for Dynamic Data Processing via Agent-Based Architecture

Reflection Design Pattern in Agent-Based Data Processing

This repository demonstrates how to apply the Reflection Design Pattern in a data processing context using an agent-based architecture. The goal is to enable dynamic method invocation and flexible behavior modification at runtime, promoting extensibility and reducing tight coupling between components.

🔍 Key Concepts

• Reflection Design Pattern: Enables dynamic inspection and invocation of classes, methods, and fields at runtime.
• Agent-Based Architecture: Each agent encapsulates specific processing logic and can be dynamically triggered based on runtime conditions.
• Decoupled Processing: Agents are not hardcoded, allowing for scalable and maintainable data workflows.

📈 Why Reflection Pattern?

Traditional systems use static processing pipelines. Our reflective approach enables:

Runtime Adaptation to unknown data types
Continuous Improvement through learned preferences
Reduced Configuration through intelligent auto-discovery
Enhanced Maintainability with clear separation of concerns

📁 Project Structure

Description of Flowchart:

Data → processed by Agent
Agent → invokes Reflection Invoker
Reflection Invoker → dynamically selects and calls Processing Logic
Processing Logic → returns results back to the agent for further steps.

🛠️ Technology Stack

Python 3.8+ with advanced metaprogramming

🚀 How to Run

1. Clone the repository:

   git clone https://github.com/your-username/reflection-agent-data-processing.git
   curl -fsSL https://ollama.com/install.sh | sh
   ollama server
   ollama pull ollama3  # pull one of this model: gemma4B_v gemma12B_v qwen3 gemini ollama3.2 deepseek
   cd data_processing_agentic
   uv add -r requirements.txt
   uv run main.py