Message Types and Session History

Welcome back! In the previous lessons, you learned how to send a simple query to DeepSeek's language model and explored various model parameters to customize the AI's responses. Now, we will delve into the concept of message types and the importance of maintaining session history . These elements are crucial for creating dynamic and context-aware interactions with the AI, allowing your personal tutor to engage in more meaningful educational conversations.

Understanding Message Types

Before we dive into building and managing session history, it's important to understand the concept of message types and how a tutoring session history is structured. In a tutor-student interaction, messages are typically categorized by roles: system, user, and assistant . While we'll explore system prompts more thoroughly in a later lesson, remember that these primary roles help define the flow of dialogue and ensure the AI understands who is speaking at any given time. DeepSeek expects the session history to be formatted as an array of hashes, where each hash represents a message with two key-value pairs: role and content . Here's an example of what a simple tutoring session might look like: JSON [{"role": "user", "content": "Can you explain the theory of relativity?"}, {"role": "assistant", "content": "Einstein's theory of relativity consists of two parts: special relativity and general relativity..."}, {"role": "user", "content": "What practical applications does it have?"}, {"role": "assistant", "content": "The theory of relativity has several practical applications including GPS systems, particle accelerators, and understanding astronomical phenomena."}] In this example, the session history consists of alternating messages between the user (the student) and the assistant (the AI tutor). Each message is stored with its respective role, providing context for the AI to generate appropriate educational responses. Understanding this structure is key to effectively managing tutoring sessions and ensuring that the AI can provide coherent and contextually relevant explanations.

Creating a Method to Handle Tutoring Sessions

To manage tutoring sessions effectively, we will create a method called send_query. This method will send queries to the AI and receive explanations, allowing us to handle multiple interactions seamlessly. Here's how the method is structured in Ruby: Rubyrequire "openai" # Initialize the DeepSeek client client = OpenAI::Client.new( access_token: "deepseek_api_key", uri_base: "https://api.deepseek.com" ) # Method to send a tutoring query and receive an explanation def send_query(client, messages) response = client.chat( parameters: { model: "deepseek-ai/DeepSeek-V3", messages: messages } ) response.dig("choices", 0, "message", "content").strip end In this method, we use the chat method of the client to send an array of messages to the AI. The messages parameter contains the session history, which provides context for the AI's response. The method returns the AI's explanation, which is extracted from the API result and stripped of any leading or trailing whitespace.

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Building and Managing Session History

Maintaining a session history is crucial for providing context to the AI tutor. This allows the AI to generate explanations that are relevant to the ongoing educational dialogue.

If we don't maintain session history, the AI will lack the context of previous interactions, leading to responses that may not be coherent or relevant to the current conversation. That is, it would treat each query as an isolated request, which could result in repetitive or disconnected explanations. Moreover, using an array of hashes to store session history offers several advantages. Using an array of hashes allows us to include both the role and content in a structured way, compared to a simple array of strings, which lacks role attribution and conversational context. The hash format ensures that the AI can accurately interpret who is speaking and maintain the flow of dialogue. It also makes it easier to manage and update the session history, as each message is self-contained with its role and content, providing a clear and organized way to track the conversation.

Let's see how we can build and manage session history in Ruby:

Ruby

# Start a session with an initial query
session = [
  { "role" => "user", "content" => "What was the most notable achievement of Albert Einstein?" }
]

# Get first response
reply = send_query(client, session)
puts "Answer: #{reply}"

# Start a session with an initial query
session = [
  { "role" => "user", "content" => "What was the most notable achievement of Albert Einstein?" }
]

# Get first response
reply = send_query(client, session)
puts "Answer: #{reply}"

After sending the initial query, the AI responds with information about Einstein's achievements, showcasing its ability to provide educational content:

text

Answer: Albert Einstein's most notable achievement is generally considered to be his theory of relativity, particularly the general theory of relativity published in 1915...

Answer: Albert Einstein's most notable achievement is generally considered to be his theory of relativity, particularly the general theory of relativity published in 1915...

In this example, we start a tutoring session with an initial query from the user. The send_query method is used to get the AI's explanation, which is then printed.

Ruby

# Add the response to session history
session << { "role" => "assistant", "content" => reply }

# Add a follow-up query
session << { "role" => "user", "content" => "Can you provide three more examples?" }

# Get explanation with tutoring context
follow_up_reply = send_query(client, session)
puts "Follow-up: #{follow_up_reply}"

# Add the response to session history
session << { "role" => "assistant", "content" => reply }

# Add a follow-up query
session << { "role" => "user", "content" => "Can you provide three more examples?" }

# Get explanation with tutoring context
follow_up_reply = send_query(client, session)
puts "Follow-up: #{follow_up_reply}"

With the session history maintained, the AI provides a contextually relevant follow-up explanation, listing additional achievements of Einstein:

text

Follow-up: Here are three more notable achievements of Albert Einstein:

1. Photoelectric Effect: In 1905, Einstein explained the photoelectric effect, demonstrating that light consists of particles called photons...

2. Einstein's Theory of Brownian Motion: Also in 1905, Einstein provided mathematical evidence for the existence of atoms...

3. Mass-Energy Equivalence: Einstein formulated the equation E=mc²...

Follow-up: Here are three more notable achievements of Albert Einstein:

1. Photoelectric Effect: In 1905, Einstein explained the photoelectric effect, demonstrating that light consists of particles called photons...

2. Einstein's Theory of Brownian Motion: Also in 1905, Einstein provided mathematical evidence for the existence of atoms...

3. Mass-Energy Equivalence: Einstein formulated the equation E=mc²...

By maintaining this history, we provide context for subsequent interactions, allowing the AI to generate more coherent and relevant educational explanations.

Note, that instead of manually adding messages to the session array, you can also manage session history using a dedicated function. This approach helps keep your code organized and makes it easier to handle more complex interactions. In the practical part, we will work on building such a function to automate the process of updating the session history.