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103 lines
3.9 KiB
Markdown
103 lines
3.9 KiB
Markdown
# Bingo Digital Pet Agent
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This sample agent demonstrates static instruction functionality through a lovable digital pet named Bingo! The agent showcases how static instructions (personality) are placed in system_instruction for caching while dynamic instructions are added to user contents, affecting the cacheable prefix of the final model prompt.
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**Prompt Construction & Caching**: The final model prompt is constructed as: `system_instruction + tools + tool_config + contents`. Static instructions are placed in system_instruction, while dynamic instructions are appended to user contents (which are part of contents along with historical chat history). This means the prefix (system_instruction + tools + tool_config) remains cacheable while only the contents portion changes between requests.
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## Features
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### Static Instructions (Bingo's Personality)
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- **Constant personality**: Core traits and behavior patterns never change
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- **Context caching**: Personality definition is cached for performance
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- **Base character**: Defines Bingo as a friendly, energetic digital pet companion
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### Dynamic Instructions (Hunger-Based Moods)
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- **Ultra-fast hunger progression**: full (0-2s) → satisfied (2-6s) → a_little_hungry (6-12s) → hungry (12-24s) → very_hungry (24-36s) → starving (36s+)
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- **Session-aware**: Mood changes based on feeding timestamp in session state
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- **Realistic behavior**: Different responses based on how hungry Bingo is
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### Tools
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- **eat**: Allows users to feed Bingo, updating session state with timestamp
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## Usage
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### Setup API Credentials
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Create a `.env` file in the project root with your API credentials:
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```bash
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# Choose Model Backend: 0 -> ML Dev, 1 -> Vertex
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GOOGLE_GENAI_USE_ENTERPRISE=1
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# ML Dev backend config
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GOOGLE_API_KEY=your_google_api_key_here
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# Vertex backend config
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GOOGLE_CLOUD_PROJECT=your_project_id
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GOOGLE_CLOUD_LOCATION=us-central1
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```
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The agent will automatically load environment variables on startup.
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### Default Behavior (Hunger State Demonstration)
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Run the agent to see Bingo in different hunger states:
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```bash
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cd contributing/samples
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PYTHONPATH=../../src python -m static_instruction.main
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```
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This will demonstrate all hunger states by simulating different feeding times and showing how Bingo's mood changes while his core personality remains cached.
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### Interactive Chat with Bingo (adk web)
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For a more interactive experience, use the ADK web interface to chat with Bingo in real-time:
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```bash
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cd contributing/samples
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PYTHONPATH=../../src adk web .
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```
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This will start a web interface where you can:
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- **Select the agent**: Choose "static_instruction" from the dropdown in the top-left corner
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- **Chat naturally** with Bingo and see his personality
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- **Feed him** using commands like "feed Bingo" or "give him a treat"
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- **Watch hunger progression** as Bingo gets hungrier over time
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- **See mood changes** in real-time based on his hunger state
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- **Experience begging** when Bingo gets very hungry and asks for food
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The web interface shows how static instructions (personality) remain cached while dynamic instructions (hunger state) change based on your interactions and feeding times.
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### Sample Prompts for Feeding Bingo
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When chatting with Bingo, you can feed him using prompts like:
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**Direct feeding commands:**
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- "Feed Bingo"
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- "Give Bingo some food"
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- "Here's a treat for you"
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- "Time to eat, Bingo!"
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- "Have some kibble"
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**When Bingo is begging for food:**
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- Listen for Bingo saying things like "I'm so hungry", "please feed me", "I need food"
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- Respond with feeding commands above
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- Bingo will automatically use the eat tool when very hungry/starving
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## Agent Structure
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```
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static_instruction/
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├── __init__.py # Package initialization
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├── agent.py # Main agent definition with static/dynamic instructions
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├── main.py # Runner script with hunger state demonstration
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└── README.md # This documentation
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```
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