Sending a Simple Query to DeepSeek with Ruby | CodeSignal Learn

Sending a Simple Query to DeepSeek with Ruby | CodeSignal Learn

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 gem, which allows us to interact with DeepSeek's API through the OpenAI client. To install this gem, you can use the following command in your terminal: Shell gem install openai If you are using the CodeSignal platform, this gem 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 environment variables. An environment variable is like a special note that your computer can read to find out important details, such as your DeepSeek API key, without having to write it directly in your code. This helps keep your key safe and secure. If you were setting this up on your own system, here's how you would do it: On macOS and Linux, open your terminal and use the export command to set the environment variables: Shell export OPENAI_API_KEY=your_deepseek_api_key_here export OPENAI_BASE_URL=https://api.deepseek.com For Windows, you can set the environment variables using the set command in the Command Prompt: Visual Basic set OPENAI_API_KEY=your_deepseek_api_key_here set OPENAI_BASE_URL=https://api.deepseek.com If you are using PowerShell, use the following command: PowerShell $env:OPENAI_API_KEY="your_deepseek_api_key_here" $env:OPENAI_BASE_URL="https://api.deepseek.com" These commands will set the environment variables for the current session. But remember, while using CodeSignal, you can skip these steps and jump straight into experimenting with DeepSeek models.

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Initializing the OpenAI Client for DeepSeek

Once the environment variables are set, you can initialize the OpenAI client in your script to use with DeepSeek. This is done by requiring the openai gem and then creating an instance of the client. The gem will automatically identify your API key and base URL from the environment variables: require "openai" # Initialize the OpenAI client client = OpenAI::Client.new Note: The above example won't work in the CodeSignal practice environment, because the environment uses a custom API wrapper. Therefore, you must explicitly provide the API key and base URL when creating the client, similar to the example below: client = OpenAI::Client.new(access_token: "deepseek_api_key", uri_base: "https://api.deepseek.com") By initializing the client in this manner, you ensure that your script is ready to authenticate requests to DeepSeek's API securely.

Sending Your First Query to DeepSeek

Now that your environment is set up and your API client is configured, it's time to send your first query to DeepSeek. We'll start by defining a simple prompt and then use the chat method to send this query to the AI model. Here's the code to accomplish this: # Define a simple query to test the AI's capabilities prompt = "Can you tell me an engineering joke?" # Create a tutoring session query request to get the AI's explanation response = client.chat(parameters: {model: "deepseek-ai/DeepSeek-V3", messages: [{ role: "user", content: prompt }]}) In this code, we define a query asking the AI to tell an engineering joke. The chat method of the OpenAI::Client is used to send a message to the DeepSeek model and receive a response. It takes a parameters hash to specify the details of the request: The model key specifies which AI model to use for generating the response. In this example, we use "deepseek-ai/DeepSeek-V3", which is DeepSeek's language model known for its advanced text understanding and generation capabilities. The messages key is an array of hashes, where each hash represents a message in the conversation. Each hash must include a :role, which indicates the role of the message sender, such as "user" for the person interacting with the AI, and :content, which contains the actual text of the message.

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 you can do that: # Extract the tutor's response from the API result reply = response.dig("choices", 0, "message", "content").strip # Display the query and the tutor's explanation puts "Query: #{prompt}" puts "Answer: #{reply}" Once the chat method is called, it returns a response hash. To extract the AI's reply, you need to access the "choices" array from the response, which contains possible responses generated by the AI. You then select the first choice with index 0 and retrieve the message content using ["message"]["content"] . The strip method 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: 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.

Example: Full Code Implementation

Let's look at the complete code example for sending a query to DeepSeek. This example includes all the steps we've discussed so far: require "openai" # Initialize the OpenAI client client = OpenAI::Client.new( access_token: "deepseek_api_key", uri_base: "https://api.deepseek.com" ) # Define a simple query to test the AI's capabilities prompt = "Can you tell me an engineering joke?" # Create a tutoring session query request to get the AI's explanation response = client.chat( parameters: { model: "deepseek-ai/DeepSeek-V3", messages: [ { role: "user", content: prompt } ] } ) # Extract the tutor's response from the API result reply = response.dig("choices", 0, "message", "content").strip # Display the query and the tutor's explanation puts "Query: #{prompt}" puts "Answer: #{reply}"

Ruby

# Define a simple query to test the AI's capabilities
prompt = "Can you tell me an engineering joke?"

# Create a tutoring session query request to get the AI's explanation
response = client.chat(
  parameters: {
    model: "deepseek-ai/DeepSeek-V3",
    messages: [
      { role: "user", content: prompt }
    ]
  }
)

Ruby

# Extract the tutor's response from the API result
reply = response.dig("choices", 0, "message", "content").strip

# Display the query and the tutor's explanation
puts "Query: #{prompt}"
puts "Answer: #{reply}"

Ruby

require "openai"

# Initialize the OpenAI client
client = OpenAI::Client.new(
  access_token: "deepseek_api_key",
  uri_base: "https://api.deepseek.com"
)

# Define a simple query to test the AI's capabilities
prompt = "Can you tell me an engineering joke?"

# Create a tutoring session query request to get the AI's explanation
response = client.chat(
  parameters: {
    model: "deepseek-ai/DeepSeek-V3",
    messages: [
      { role: "user", content: prompt }
    ]
  }
)

# Extract the tutor's response from the API result
reply = response.dig("choices", 0, "message", "content").strip

# Display the query and the tutor's explanation
puts "Query: #{prompt}"
puts "Answer: #{reply}"