Build an Arcade AI actor with FastAPI

Prerequisites

• Set up Arcade AI

• Install the Arcade AI SDK and any necessary dependencies:

  pip install arcade-ai fastapi pydantic

Install a Pre-Built Toolkit

Arcade AI offers a variety of pre-built toolkits that you can install and use immediately. For this guide, we'll use the arcade-math toolkit as an example.

• Install the arcade-math toolkit:

  pip install arcade-math

Create a FastAPI Application to Serve the Toolkit

We'll set up a FastAPI application that acts as an Actor to serve the tools from the installed toolkit.

1. Create a new Python file (e.g., main.py) and import the necessary modules:

   import os
    
   from fastapi import FastAPI, HTTPException
   from pydantic import BaseModel
    
   from arcade.actor.fastapi.actor import FastAPIActor
   from arcade.sdk import Toolkit
   from arcadepy import AsyncArcade
   import arcade_math

2. Ensure Arcade API Key is Set

   The Arcade API key is automatically loaded from the ARCADE_API_KEY environment variable. If you haven't set this yet, you can do so by running:

   export ARCADE_API_KEY="your_api_key"   # On Windows use `set`

3. Initialize the Arcade Client

   client = AsyncArcade()

4. Initialize the FastAPI Application and Actor

   app = FastAPI()
    
   actor_secret = os.environ.get("ARCADE_ACTOR_SECRET")
   actor = FastAPIActor(app, secret=actor_secret)

5. Register the Toolkit with the Actor

   actor.register_toolkit(Toolkit.from_module(arcade_math))

6. Create a Pydantic Model for Chat Requests

   class ChatRequest(BaseModel):
       message: str
       user_id: str

7. Define the Chat Endpoint

   @app.post("/chat")
   async def postChat(request: ChatRequest, tool_choice: str = "execute"):
       try:
           raw_response = await client.chat.completions.create(
               messages=[
                   {"role": "system", "content": "You are a helpful assistant."},
                   {"role": "user", "content": request.message},
               ],
               model="gpt-4o-mini",
               max_tokens=500,
               tools=[],  # List your tools here if needed
               tool_choice=tool_choice,
               user=request.user_id,
           )
       except Exception as e:
           raise HTTPException(status_code=500, detail=str(e))
       else:
           return raw_response.choices

Run Your Application

Ensure you have the ARCADE_ACTOR_SECRET environment variable set for authentication:

export ARCADE_ACTOR_SECRET="your_actor_secret_key"  # On Windows use `set`

Start your FastAPI application using Uvicorn:

uvicorn main:app --host 127.0.0.1 --port 8000

Test the Setup

You can now send a POST request to the /chat endpoint with a message, and the assistant will respond using the tools from the arcade-math toolkit.

Example request using curl:

curl -X POST "http://127.0.0.1:8000/chat" \
     -H "Content-Type: application/json" \
     -d '{"message": "What is the square root of 16?", "user_id": "[email protected]"}'

Customize the Tools (Optional)

If you wish to specify which tools to make available in the assistant's responses, modify the tools parameter in the postChat function:

tools = ["Math.Sqrt"]  # Replace with the actual tool names