Deploy Models with Ray Serve and Novita AI

Outline a strategy for deploying the GPT-5 and Claude Sonnet 4 models used by your LlamaIndex agent using Ray Serve and Novita AI. Describe how Ray Serve would manage the inference endpoints for both models, ensuring scalability and reliability. Explain how Novita AI's capabilities could be integrated to optimize the inference runtime and cost for the financial analysis tasks. Provide conceptual code snippets for setting up a Ray Serve deployment. ```python
from ray import serve @serve.deployment
class GPT5Model: def __init__(self): # Initialize GPT-5 client pass async def __call__(self, text: str): # Call GPT-5 API return {"output": "..."} @serve.deployment
class ClaudeSonnet4Model: def __init__(self): # Initialize Claude Sonnet 4 client pass async def __call__(self, text: str): # Call Claude Sonnet 4 API return {"output": "..."} # serve.run(
# GPT5Model.bind(),
# ClaudeSonnet4Model.bind(),
# ) # Conceptual: LlamaIndex agent configuration to use Serve endpoints
# llm_gpt5 = OpenAI(model="hosted-gpt5", api_base="http://localhost:8000/gpt5")
```