Mastering Multi-Turn Conversations with the DeepSeek /chat/completions API

The DeepSeek /chat/completions API offers powerful capabilities for building conversational AI applications. This article will guide you through utilizing the API for multi-turn conversations, enabling you to create engaging and context-aware chat experiences.

Understanding the Stateless Nature of the DeepSeek API

It's crucial to understand that the DeepSeek /chat/completions API is stateless. This means that the server doesn't automatically retain any history of past interactions. To maintain context across multiple turns, you, as the user, are responsible for managing and providing the entire conversation history with each new request.

This might seem like extra work, but offers developers more control over the conversation flow and allows for greater flexibility in designing conversational agents.

Concatenating Context: The Key to Multi-Turn Conversations

The core technique for creating multi-turn conversations with the DeepSeek API lies in concatenating the conversation history. With each new turn, you'll append previous user inputs and model outputs to the message list sent to the API.

The following steps outline the process:

1. Initialize the Conversation: Start with a list containing the initial user message.
2. Send the First Request: Send the initial message to the DeepSeek /chat/completions API.
3. Append Model's Response: Add the model's response (typically accessed with response.choices[0].message) to the end of the message list. This preserves the context of the first turn.
4. Add Subsequent User Input: Add the new user question or statement to the end of the updated message list.
5. Send the Next Request: Submit the entire updated message list to the API for the next turn.
6. Repeat: Continue steps 3-5 for each subsequent turn, continually building on the conversation history.

Practical Implementation with Python

Let's illustrate this with a Python code example:

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.deepseek.com")

# Round 1
messages = [{"role": "user", "content": "What's the highest mountain in the world?"}]
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)
messages.append(response.choices[0].message)
print(f"Messages Round 1: {messages}")

# Round 2
messages.append({"role": "user", "content": "What is the second?"})
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)
messages.append(response.choices[0].message)
print(f"Messages Round 2: {messages}")

In the first round, the API receives only the initial question. In the second round, the API receives the original question, the model's answer from round one, and the new question. This process keeps repeating. Be mindful of the token limits of the DeepSeek models.

Analyzing the Message Structure

Let's break down the message structure in each round.

Round 1:

[
  {"role": "user", "content": "What's the highest mountain in the world?"}
]

Round 2:

[
  {"role": "user", "content": "What's the highest mountain in the world?"},
  {"role": "assistant", "content": "The highest mountain in the world is Mount Everest."},
  {"role": "user", "content": "What is the second?"}
]

As you can see, the second round's message list includes the entire history: the initial user question, the model's response, and the subsequent user question. This allows the DeepSeek model to understand the context and provide more relevant and coherent answers.

Beyond the Basics: Tips for Enhanced Conversations

• Manage Token Usage: Keep track of the total token count in your message history. Long conversations can exceed the API's token limits, leading to errors or truncated responses. Consider implementing strategies like summarization or context pruning to keep the message size manageable. See DeepSeek's documentation on Token & Token Usage.
• Utilize System Messages: Employ system messages at the beginning of the conversation to guide the model's behavior and define its persona. The Temperature Parameter can also be adjusted to affect the creativity of the model's responses.
• Error Handling: Implement robust error handling to gracefully manage potential issues with the API, such as rate limits or server errors. Refer to the Error Codes documentation for guidance.
• Explore other API features: Deepseek offers a wide variety of API features, such as Function Calling and JSON Output.

Conclusion

Mastering multi-turn conversations with the DeepSeek /chat/completions API involves effectively managing and concatenating the conversation context. By understanding the stateless nature of the API and implementing the techniques described in this guide, you can build powerful and engaging conversational AI applications. Remember to consult the official DeepSeek API Docs for the most up-to-date information and best practices.