NVIDIA Agent

task_examples/nvidia-agent/
├── main.py                    # Agent with NVIDIA model
├── upsonic_configs.json       # Upsonic CLI configuration
├── .env.example               # Example environment variables
└── README.md                  # Quick start guide

# Set API key
export NVIDIA_API_KEY="your-api-key"

# Or use NGC_API_KEY (alternative)
export NGC_API_KEY="your-api-key"

# Optional: Set custom base URL
export NVIDIA_BASE_URL="https://your-custom-endpoint.com/v1"

# Install dependencies
upsonic install

# Add a package
upsonic add <package> <section>
upsonic add requests api

# Remove a package
upsonic remove <package> <section>
upsonic remove requests api

python3 main.py

upsonic run

curl -X POST http://localhost:8000/call \
  -H "Content-Type: application/json" \
  -d '{"user_query": "What is artificial intelligence?"}'

"What is artificial intelligence?"

"Artificial intelligence (AI) is a branch of computer science that aims to create 
systems capable of performing tasks that typically require human intelligence..."

"""
NVIDIA Agent Example

This example demonstrates how to create and use an Agent with NVIDIA models.
The example shows:
1. Creating a NvidiaModel instance
2. Creating an Agent with the NvidiaModel
3. Creating a Task
4. Executing the task with the agent

This file contains:
- async main(inputs): For use with `upsonic run` CLI command (FastAPI server)
"""

from upsonic import Agent, Task
from upsonic.models.nvidia import NvidiaModel


async def main(inputs: dict) -> dict:
    """
    Async main function for FastAPI server (used by `upsonic run` command).
    
    This function is called by the Upsonic CLI when running the agent as a server.
    It receives inputs from the API request and returns a response dictionary.
    
    Args:
        inputs: Dictionary containing input parameters as defined in upsonic_configs.json
                Expected key: "user_query" (string)
    
    Returns:
        Dictionary with output schema as defined in upsonic_configs.json
        Expected key: "bot_response" (string)
    """
    user_query = inputs.get("user_query", "Hi, how are you?")

    model = NvidiaModel(
        model_name="meta/llama-3.1-nemotron-70b-instruct:1.0"
    )

    agent = Agent(
        model=model,
        name="NVIDIA Agent"
    )
    

    answering_task = Task(
        description=f"Answer the user question: {user_query}"
    )
    
    result = await agent.print_do_async(answering_task)
    
    return {
        "bot_response": result
    }


if __name__ == "__main__":
    import asyncio
    asyncio.run(main({"user_query": "Hi, how are you?"}))

{
    "environment_variables": {
        "UPSONIC_WORKERS_AMOUNT": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "API_WORKERS": {
            "type": "number",
            "description": "The number of workers for the Upsonic API",
            "default": 1
        },
        "RUNNER_CONCURRENCY": {
            "type": "number",
            "description": "The number of runners for the Upsonic API",
            "default": 1
        },
        "NEW_FEATURE_FLAG": {
            "type": "string",
            "description": "New feature flag added in version 2.0",
            "default": "enabled"
        }
    },
    "machine_spec": {
        "cpu": 2,
        "memory": 4096,
        "storage": 1024
    },
    "agent_name": "NVIDIA Agent",
    "description": "NVIDIA-powered AI Agent using Upsonic framework with NvidiaModel",
    "icon": "book",
    "language": "book",
    "streamlit": false,
    "proxy_agent": false,
    "dependencies": {
        "api": [
            "fastapi>=0.115.12",
            "uvicorn>=0.34.2",
            "upsonic",
            "pip"
        ],
        "streamlit": [
            "streamlit==1.32.2",
            "pandas==2.2.1",
            "numpy==1.26.4"
        ],
        "development": [
            "watchdog",
            "python-dotenv",
            "ipdb",
            "pytest",
            "streamlit-autorefresh"
        ]
    },
    "entrypoints": {
        "api_file": "main.py",
        "streamlit_file": "streamlit_app.py"
    },
    "input_schema": {
        "inputs": {
            "user_query": {
                "type": "string",
                "description": "User's question or query for the NVIDIA agent to answer",
                "required": true,
                "default": null
            }
        }
    },
    "output_schema": {
        "bot_response": {
            "type": "string",
            "description": "NVIDIA agent's generated response to the user query"
        }
    }
}