DeepSeek API effectively manages multi-round conversations by maintaining a context of previous interactions, allowing for coherent and contextually relevant responses across multiple exchanges. Hereâs how it operates:
Session Management
When initiating a conversation, the API creates a new chat session using the `new_chat()` method, which generates a unique session ID. This ID is crucial for tracking the conversation's context throughout its duration[1].Handling Multi-Round Conversations
To facilitate multi-round dialogues, DeepSeek API requires the user to manage conversation history manually. Each time a new message is sent to the API, the entire conversation history must be included in the request. This means that every user input and the corresponding assistant output should be appended to a list of messages that is sent with each API call. This approach ensures that the model has access to all prior exchanges, enabling it to generate responses that are informed by the entire dialogue context[3][5].Asynchronous Processing
The API supports asynchronous communication, allowing for efficient handling of responses. When sending messages, the API can process incoming data in chunks, which is particularly useful for long conversations or when waiting for responses from the server. This non-blocking behavior allows other tasks to continue running while waiting for replies, enhancing overall performance during multi-turn interactions[1].Example Implementation
In practice, users implement this by maintaining a list of messages and updating it with each interaction. For example:python
messages = [{"role": "user", "content": "What is the capital of France?"}]
response = client.chat.completions.create(model="deepseek-chat", messages=messages)
# Append assistant's response
messages.append({"role": "assistant", "content": response.content})
# Next user query
messages.append({"role": "user", "content": "And what about Germany?"})
response = client.chat.completions.create(model="deepseek-chat", messages=messages)
This code snippet illustrates how each round of conversation builds upon previous exchanges by appending new messages to the existing list[4][6].
Limitations
While DeepSeek supports multi-turn dialogues effectively, it does not inherently manage conversation history; this responsibility lies with the developer using the API. Therefore, developers need to implement logic to store and retrieve previous messages as needed[2][3].Citations:
[1] https://thinhdanggroup.github.io/blog-on-chat-deepseek-r1-api/
[2] https://huggingface.co/deepseek-ai/Janus-1.3B/discussions/9
[3] https://www.youtube.com/watch?v=_IKz7sDQeVg
[4] https://www.vellum.ai/blog/the-training-of-deepseek-r1-and-ways-to-use-it
[5] https://api-docs.deepseek.com
[6] https://www.aisharenet.com/en/deepseek-r1nenglixiang/
[7] https://www.deepseek.com
[8] https://www.creolestudios.com/deepseek-vs-chatgpt-cost-comparison/