Building AI Application with DeepSeek-V3

DeepSeek-V3, the newest model from Chinese AI firm DeepSeek, is making a big impact in the AI world. It is outperforming many top proprietary AI models and showing that open-source AI can lead the way. It helps create smart, efficient, and scalable solutions while being economical since it is free to use. In this guide, we will learn how to use DeepSeek-V3 to build an AI application. We will also explore how DeepSeek-V3 makes it simple to develop fast, flexible, and reliable AI systems that can handle various tasks with ease.

Why Use DeepSeek-V3?

DeepSeek-V3 is a strong, open-source AI model that makes building AI applications simple and efficient. It operates at impressive speeds, processing up to 60 tokens per second, making it faster than many other models. This speed allows you to get results quickly and improve your productivity. Moreover, being free and open-source, it’s accessible to everyone without any cost concerns. Also, its straightforward setup ensures that even beginners can use it with ease.

DeepSeek-V3 is also scalable, so it works well for both small projects and large, complex applications. With its powerful features, you can create smart AI tools that save time, reduce effort, and increase efficiency. The model is very flexible and can be used for many tasks like analyzing text, solving problems, creating content, and writing code. Whether you’re a developer, student, or business owner, you can adjust DeepSeek-V3 to fit your needs.

Apart from its ease of use and versatility, one of the main reasons I chose DeepSeek-V3  is because it’s simply better than most other models. The below figure illustrates how DeepSeek-V3 is performing with other state-of-the-art models like Llama-3.1-405, GPT-4O-0513, and Claude-3.5-Sonnet-1022a.

Also Read: DeepSeek V3 vs Claude Sonnet 3.5: Which is Better?
Also Read: DeepSeek V3 vs GPT-4o: Can Open-Source AI Compete with GPT-4o’s Power?

Building an AI Application Using DeepSeek-V3

In this section, I will walk you through the process of building an AI application using DeepSeek-V3. We will be building an app that will search the web, find trending topics, and list them out for us.

For this, we will first cover the necessary prerequisites and set up the environment. Then I will guide you on how to make API calls, formulate prompts, and save the generated content in Markdown format. By the end, you’ll have a working application that can suggest trending topics in Generative AI for writing blogs and articles. So let’s begin.

Prerequisites

• Familiarity with the terminal or command prompt is necessary.
• Ability to set environment variables on your system.
• Capability to run programs using the terminal or command prompt.
• Python must be installed:  https://www.python.org/downloads/

Accessing the API

We will be using Hyperbolic Labs to access the DeepSeek-V3 model. Here’s how you can get the API:

1. Go to https://app.hyperbolic.xyz/ and create an account.
   
2. Click on Models and select DeepSeek-V3.
   
3. Now copy the API and begin building the application.

Building the Application

Now that you have the API, let’s move to the code editor and build our application.

Step 1: Calling the API

This code sets up the necessary information to make a request to an API (a service on the web) that can generate chat responses. Here’s a breakdown of the above process:

1. URL
   1. url = “https://api.hyperbolic.xyz/v1/chat/completions”
      This is the web address (endpoint) of the API that you want to interact with. The API will likely help you complete or generate chat messages, similar to how conversational AI models work.
2. Headers
   1. Content-Type”: “application/json”
      This tells the server that the information you’re sending will be in a special format called JSON, which is commonly used for exchanging data over the internet.
   2. “Authorization”: “Bearer <token>”
      This part is used for security. The Bearer token is a secret code that proves you have permission to use the API. It’s like a password that allows you to access the service.
   3. To use this in a program, you would need to send this information in a request using something like Python’s requests library. This code alone just prepares the URL and headers, but it doesn’t yet send a request. You’ll need to use a function like requests.post() to call the API and get a response.

Tip: Remember to replace the <token> with your own real API token for the code to work properly.

Step 2:  Formulating Prompts to Guide AI Content Generation

This code shows how an effective prompt helps the DeepSeek-V3 model generate content about trending topics in Generative AI. The prompt covers areas like new applications, advancements, and ethical issues. This will guide the AI to suggest blog and article topics for both technical and general readers.

Let me break down the code for you.

1. Data Payload
   • The data variable contains the main content and instructions you’re sending to the API. It’s a JSON object, which represents the data you want the API to process.
2. Messages
   • This section specifies the list of messages that the AI model will receive. In this case, it includes a single message coming from the “user.”
   • The role: “user” indicates that the message content is coming from you (the user).
   • The content section contains your detailed instructions for the AI, asking it to suggest blog or article topics related to Generative AI (GenAI). You provide specific themes, like applications, ethical issues, and future trends in the field.
3. Model
   • “model”: “deepseek-ai/DeepSeek-V3” tells the API which AI model to use. In this case, you’re selecting the DeepSeek-V3 model, designed for generating chat responses or content.
4. Max Tokens
   • “max_tokens”: 2048 limits the length of the AI’s response. It tells the model that it can generate up to 2048 tokens (a token is typically a word or part of a word).
5. Temperature
   • “temperature”: 0.1 affects the level of creativity in the AI’s response. A value close to 0 makes the AI’s answers more focused and predictable, while a value near 1 encourages more svaried and creative responses. With 0.1, the AI’s answers will be more structured and reliable.
6. Top-p
   • “top_p”: 0.9 sets the parameter for nucleus sampling, which limits the selection of possible next words. It defines the cumulative probability of considering the most likely options for generating the response. When the number is set to 0.9, the AI only considers the top 90% of the most likely phrases to provide a clear and relevant response.

Nucleus sampling is a technique that AI models employ to determine the next word in a phrase. Instead of examining all possible words, it chooses a smaller set of terms that are more likely to make sense in the context. This group is called the “nucleus,” and its size depends on a setting called “top-p.”

For example, if top-p is set to 0.9, the model chooses from the smallest group of words that together add up to 90% of the total likelihood. This method helps the AI create more natural and creative responses, while still focusing on the most likely words.

Step 3: Saving the Output in Markdown Format(.md)

This part of the code sends a request to the API, gets the response, and saves the result in a file named “gen_ai_topics.md” (in Markdown format). If everything goes well, the response is written to the file, and a success message is printed. If there’s an error, the error details are printed instead.

Output:

Learn More: Andrej Karpathy Praises DeepSeek V3’s Frontier LLM, Trained on a $6M Budget

Conclusion

In this article, we have learned how to build an AI application using DeepSeek-V3, a fast and efficient open-source AI model. DeepSeek-V3 is flexible and can handle different tasks, making it a great tool for content creation and problem-solving. Its open-source nature makes it an affordable option for developers, students, and businesses alike. As AI continues to grow, DeepSeek-V3 will prove to be a useful tool for anyone wanting to explore modern AI technology.

Frequently Asked Questions