novelai-api VS fastapi

Here, I tinkered with it a bit myself, this is a good place to get you started. https://github.com/Aedial/novelai-api https://api.novelai.net/docs/ or specifically https://api.novelai.net/docs/#/%2Fai%2F/AIController_generateVoice

Btw Aedial has already written a Python API if anyone is interested: https://github.com/arthus-leroy/novelai-api

This project was inspired by FastAPI and aims to provide a similar developer-friendly experience

[FastAPI] https://fastapi.tiangolo.com/) for the web server

🔗 New to FastAPI? Check out FastAPI's official documentation.

Last year I wrote a series of articles explaining how FastAPI works. It was a rewarding experience, since I was able to both share my knowledge and learn more about it.

The advancement of AI has empowered businesses to incorporate intelligent automation into their applications. A serverless Generative AI (GenAI) API enables developers to harness cutting-edge AI models without the burden of infrastructure management. This guide walks you through building a scalable and cost-effective GenAI API using FastAPI, a high-performance Python framework with built-in async support and seamless AWS integration. By deploying FastAPI on AWS Lambda with AWS API Gateway, you can create a fully managed, pay-per-use architecture that eliminates server maintenance.

Finally, whatever file launches your application (in this case, mine is main.py), it is conventional to include that if __name__ == "main": block at the bottom (shown above), to specify the entrypoint to your application. This isn't strictly necessary in many use cases—for instance, if you're writing a REST API using FastAPI and launching it via uvicorn, you don't need this block. But it also doesn't hurt to have it regardless. It's a helpful reminder of where things begin.

**Conclusion — **Deploying a FastAPI application to Render is a straightforward and efficient process. With just a few configuration steps, you can easily go from development to a live, scalable web service.

FastAPI Documentation

FastAPI

LangServe leverages the power of FastAPI and pydantic to create a robust and efficient serving layer for your LangChain applications. It essentially wraps your LangChain chains or agents, turning them into FastAPI endpoints.