Introducing o1: OpenAI's New Reasoning Model Series on Azure 🚀

rahul188 — Mon, 30 Sep 2024 06:23:41 +0000

My POC with OpenAI's o1 Models on Azure 🏄‍♂️

Hey folks! I just wrapped up a super cool Proof of Concept (POC) with OpenAI’s latest models, o1-preview and o1-mini, on Microsoft Azure, and it’s been an absolute game-changer! 😍 Let me share my journey and how these AI models have kicked my learning into high gear. 📈

Why I Chose o1 Models 🌟

When I heard about the new o1 models being available on Azure, I knew I had to give them a shot. Here's why:

🧠 Advanced reasoning for complex tasks
💡 Great for brainstorming and problem-solving
🕵️ Detailed document analysis
🪄 Efficient workflow management

My POC Adventure 🛠️

1. Tackling Complex Coding 💻💥

I decided to test the o1-preview model with some advanced coding challenges. This model didn’t just help me generate complex algorithms; it practically held my hand through the toughest parts! 👐

Before o1: Spent hours scratching my head 🤯
After o1: Solved coding problems in minutes! ⏱️🎉

2. Brainstorming Sessions 🌪️🧩

For my next experiment, I used the o1-mini model to brainstorm solutions for a tricky project. It was like having a super-smart buddy to bounce ideas off. 🤝✨

Before o1: Felt overwhelmed with options 😵
After o1: Clear, actionable ideas! 🌟👍

3. Document Detective Work 🕵️📑

I work with a lot of historical data and json dumps files, so I tested the document comparison capabilities. The o1 models caught tiny differences that I would have definitely missed. 👀

Before o1: Hours spent double-checking 🕒
After o1: Accurate analysis in no time! 🚀

4. Workflow Magic 🪄🗂️

Lastly, I put the models to the test with some workflow management. They handled tasks that require short-context understanding like pros! 🎩✨

Before o1: Manual and tiring 😔
After o1: Smooth sailing! 🌊

How It Boosted My Learning 📈

Using the o1 models has been like getting a turbo boost for my brain. Here’s what I learned:

Enhanced Problem-Solving: Tackling complex problems with AI guidance is highly effective.
Improved Efficiency: Workflow tasks are now way more organized and less time-consuming.
Sharper Analysis Skills: Enhanced my ability to compare and analyze detailed documents.

Innovating at scale with Azure

The latest OpenAI models are backed with all the capabilities of the Azure platform, including enterprise-grade security, flexible deployment options, and broad regional availability, which helps customers meet data residency and compliance needs. We have over 60,000 Azure AI customers with exciting production use cases across industries.

How it works?

Ready to Try It Yourself? 🚀

If you’re excited to see what these models can do for you, head over to the Azure AI Studio model catalog and apply for access. Trust me, you won’t regret it! 🌐

How to Get Started

Visit Azure AI Studio: Go to the Azure AI Studio model catalog.
Apply for Access: Fill in the required details to get exclusive access.
Explore the Models: Start experimenting with o1-preview and o1-mini.
Share Your Experience: Let the world know about your amazing journey with o1 models!

Credits:

Let’s make learning and development amazing together! 🎉🌟

FastAPI - The Simplicity

rahul188 — Fri, 24 Feb 2023 04:56:01 +0000

While studying in College and having multiple projects ideas in my mind. i always thinking why there is no such simple way to build the apis & now it seems i missed one of the option.

When it comes to building APIs, Python has always been a popular choice due to its ease of use, readability, and the large number of libraries available. One of the newer frameworks in the Python ecosystem is FastAPI. In this blog, we'll take a look at what FastAPI is, why you might want to use it, and some of its key features.

What is FastAPI?

FastAPI is a modern, fast (hence the name), web framework for building APIs with Python 3.7+ based on standard Python type hints. It was created by Sebastián Ramírez and is an open-source project with a thriving community. FastAPI is built on top of Starlette for the web parts and Pydantic for the data parts.

Why use FastAPI?

One of the key benefits of using FastAPI is its speed. It is built on top of Starlette, which is an async web framework that is designed to be lightweight and fast. This means that FastAPI is able to handle a large number of requests quickly and efficiently, making it an excellent choice for building high-performance APIs.

FastAPI is also highly customizable, with a wide range of options for handling authentication, authorization, and other features. This means that it can be tailored to fit the needs of a wide range of projects and use cases.

Key Features of FastAPI

FastAPI has a number of key features that make it an attractive choice for building APIs:

Fast
Easy to use
Automatic documentation generation
Highly customizable
WebSocket support
Type checking

Let's Build One Project Using FastApis -

Language Translation | English To Hindi

Below Piece of code will helps For Language Translation

from googletrans import Translator
translator = Translator()

translation = translator.translate("All The Best", dest='hi')
print(translation)

How to create Endpoints in FastApis -

from fastapi import FastAPI

app = FastAPI()


@app.get("/")
async def root():
    return {"message": "Hello World"}

As we can use jinja templating in Fast Apis will create normal HTML Page Which will send Api Request & get the converted response from backend.

So out project folder structure will look like below -

How to send Api request to Backend -

    <script>
      const box = document.getElementById("box");
      // const box1 = document.getElementById("value")

      uploadform.onsubmit = async (e) => {
        e.preventDefault();

        let res = await fetch("/english_text", {
          method: "POST",
          body: new FormData(uploadform),
        });

        if (res.ok) {
          let result = await res.text();

          box.innerHTML = result;
        } else {
          box.innerHTML = `Response error: ${res.status}`;
        };
      };
    </script>

Now We are all set to boot up the FastApi Server.

So let's pull the code & follow below steps with python3.

git clone https://github.com/rahul188/Fast_API_Demo.git
python3 -m venv env
source env/bin/activate
pip install -r requirements.txt
uvicorn application:app --reload

the most beautiful thing about FastApi is the documentation. as we can see in below it will generate Api document as soon as we write the code.

Conclusion

FastAPI is a modern, fast web framework for building APIs with Python 3.7+ that is built on top of Starlette and Pydantic. It is highly customizable, easy to use, and provides automatic API documentation generation. FastAPI's speed and flexibility make it an excellent choice for building high-performance APIs that can be tailored to fit the needs of a wide range of projects and use cases.

DEV Community: rahul188