Sending a Simple Query to DeepSeek Using JavaScript

Sending a Simple Query to DeepSeek

Welcome to the first lesson of our course on creating a personal tutor with DeepSeek! DeepSeek is an advanced AI platform that offers state-of-the-art language models capable of understanding and generating human-like text. Its unique features include high accuracy in language comprehension, the ability to generate contextually relevant responses, and a robust API that supports seamless integration into various applications. These capabilities make DeepSeek an excellent choice for building AI tutors, as it can provide personalized and insightful interactions with users. In this lesson, we will explore the basics of interacting with DeepSeek's AI models through the OpenAI client . Our goal is to send a simple query to DeepSeek's language model and receive a response. This foundational step will set the stage for more complex interactions in future lessons. By understanding how to communicate with DeepSeek, you'll be equipped to harness its full potential in creating a personal tutor that can adapt to individual learning needs.

Setting Up Your Environment

Before we can send a query to DeepSeek, we need to set up our development environment. This involves installing the necessary tools and libraries. For this course, you will need the openai library, which allows us to interact with DeepSeek's API through the OpenAI client. To install this library, you can use the following command in your terminal: Shell npm install openai If you are using the CodeSignal platform, this library is pre-installed, so you can focus on writing and running your code without worrying about installation.

Setting the DeepSeek API Key as an Environment Variable

In this course, you'll be using the CodeSignal coding environment, where we've already set up everything you need to start working with DeepSeek models. This means you don't need to worry about setting up an API key or configuring environment variables — it's all taken care of for you. However, it's still useful to understand how this process works in case you want to set it up on your own computer in the future. To work with DeepSeek models outside of CodeSignal, you need to obtain an API key from their service. This API key is essential for accessing DeepSeek's services and making requests to their API. To keep your API key secure, you can use a .env file along with the dotenv library. This helps keep your key safe and secure by not writing it directly in your code. Here's how you can set it up: Install the dotenv package: Shell npm install dotenv Create a .env file in your project directory and add your API key and base URL: OPENAI_API_KEY=your_deepseek_api_key_here OPENAI_BASE_URL=https://api.deepseek.com Load the environment variables in your JavaScript file: JavaScript require('dotenv').config(); These steps will ensure your API key is securely managed and accessible in your application.

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Extracting and Displaying the AI's Reply

After sending the query to DeepSeek, the next step is to extract the AI's reply from the API response and display it. Here's how we can complete our function to process the response:

JavaScript

async function sendQuery() {
  const prompt = "Can you tell me an engineering joke?";

  try {
    const response = await client.chat.completions.create({
      model: "deepseek-ai/DeepSeek-V3",
      messages: [{ role: "user", content: prompt }],
    });

    const reply = response.choices[0].message.content.trim();
    console.log("Query:", prompt);
    console.log("Answer:", reply);
  } catch (error) {
    console.error("Error sending query:", error);
  }
}

async function sendQuery() {
  const prompt = "Can you tell me an engineering joke?";

  try {
    const response = await client.chat.completions.create({
      model: "deepseek-ai/DeepSeek-V3",
      messages: [{ role: "user", content: prompt }],
    });

    const reply = response.choices[0].message.content.trim();
    console.log("Query:", prompt);
    console.log("Answer:", reply);
  } catch (error) {
    console.error("Error sending query:", error);
  }
}

The chat.completions.create method returns a response object. To extract the AI's reply, you need to access the choices array from the response object, which contains possible responses generated by the AI. You then select the first choice with choices[0] and retrieve the message content using message.content.trim(), which removes any extra spaces or newlines.

Finally, we print both the query and the AI's reply to see the interaction. This helps verify that the message was successfully sent and received. When you run this code, you should see an output similar to the following:

text

Query: Can you tell me an engineering joke?
Answer: Why don't engineers use self-driving cars? They don't trust anything they didn't over-engineer themselves!

Query: Can you tell me an engineering joke?
Answer: Why don't engineers use self-driving cars? They don't trust anything they didn't over-engineer themselves!

This output demonstrates a successful interaction with the AI, where it responds to the user's query with an engineering joke.