Simple agentic workflow for summmary generation through agents with Google Cloud and Google Studio
Week 1: Foundation & Prototyping (3 hours)
Day 1 (1 hour): Project Setup & Introduction to Google AI Studio:
Create a new project in Google AI Studio.
Explore the available models and their capabilities (e.g., Gemini Pro).
Familiarize yourself with the prompt engineering interface and basic prompting techniques.
Set up a dedicated repository on your GitHub account for this project. Include a README.md file outlining the project goal, technologies used, and setup instructions (even if basic initially).
Day 2 (1 hour): Define the Agent's Task:
Choose a simple, well-defined task for your agent. Examples:
Content Summarization: Summarize short articles or text snippets.
Simple Question Answering: Answer basic questions based on a provided context.
Creative Text Generation (constrained): Generate short poems or stories based on a theme.
Clearly define the input and expected output for your chosen task.
Day 3 (1 hour): Initial Prototyping in AI Studio:
Start experimenting with prompts in Google AI Studio to achieve the desired output for your chosen task.
Iterate on your prompts, trying different phrasings and parameters.
Document your successful prompts and observations in a separate file (e.g., prompts.md in your GitHub repo).
Week 2: Moving to Python & Basic Integration (3 hours)
Day 4 (1 hour): Setting up the Python Environment:
Install the necessary Google Cloud client library for Python (google-cloud-aiplatform).
Authenticate your Google Cloud account to access Vertex AI (you might need to enable the Vertex AI API and set up credentials).
Create a basic Python script (main.py) in your GitHub repository.
Day 5 (1 hour): Basic Interaction with a Foundation Model via Python:
Write Python code using the google-cloud-aiplatform library to send a simple prompt to a Gemini model (or another suitable foundation model) in Vertex AI.
Display the model's response in your script.
Start incorporating your successful prompts from AI Studio into your Python code.
Day 6 (1 hour): Structuring Your Code:
Refactor your Python code to separate concerns (e.g., prompt definition, API interaction, output handling).
Create functions for key functionalities.
Update your README.md with instructions on how to run your Python script.
Week 3: Enhancing the Agent's Logic (3 hours)
Day 7 (1 hour): Adding Basic Memory or Context Handling (Optional, depending on task complexity):
Explore ways to maintain a simple conversation history or provide context to the agent in your Python code. This could involve storing previous turns or passing relevant information in the prompt.
Day 8 (1 hour): Implementing Input Handling and Output Formatting:
Enhance your Python script to take user input (e.g., via the command line).
Format the model's output in a user-friendly way.
Day 9 (1 hour): Error Handling and Basic Logging:
Implement basic error handling in your Python code to gracefully manage potential API issues.
Add simple logging to track the agent's interactions and any errors.
Week 4: Documentation, Refinement, and Next Steps (3 hours)
Day 10 (1 hour): Comprehensive Documentation:
Update your README.md file with a more detailed explanation of the project, how to run it, and any design decisions.
Add comments to your Python code to explain its functionality.
Day 11 (1 hour): Code Refinement and Testing:
Review your Python code for clarity, efficiency, and adherence to basic coding best practices.
Test your agent thoroughly with different inputs and scenarios.
Day 12 (1 hour): Future Enhancements and "Deployment" Considerations (Conceptual):
Document potential next steps for the project (e.g., more complex task, better memory, integration with other tools, basic deployment using Vertex AI Endpoints – even if you don't implement it within this timeframe).
Reflect on your learning experience and note down any challenges you faced and how you overcame them.