How to Stream DeepSeek API Response Using Server-Sent Events (SSE)

In the fast-paced world of artificial intelligence (AI), providing real-time responses from Large Language Models (LLMs) is essential for enhancing user experiences and improving application performance. A powerful way to achieve real-time streaming of LLM responses is through Server-Sent Events (SSE), a simple, efficient communication technology based on the HTTP protocol.

In this article, we’ll explore how SSE works, how it can be used to stream responses from LLMs like DeepSeek, and how Apidog’s SSE debugging tools can make testing and development more efficient.

What Are Server-Sent Events (SSE)?

Server-Sent Events (SSE) are a lightweight, real-time communication technology that establishes a one-way connection between the server and the client. Unlike WebSockets, which enable two-way communication, SSE is designed for scenarios where the server continuously pushes updates to the client without needing the client to request data repeatedly.

This is especially useful when streaming dynamic content, such as continuous responses from AI models. With SSE, developers and end users can witness the AI model’s responses unfold in real-time as the server sends each piece of data sequentially.

How SSE Works in LLM Streaming

In AI applications, especially with complex models like DeepSeek R1, responses often arrive in multiple fragments. SSE breaks the response into separate “events,” sending them one by one as the server generates each fragment. The client is updated immediately with each new piece of data, ensuring that the user always receives the most current information.

Benefits of Using SSE for AI Model Responses

• Real-Time Data Delivery: SSE ensures that updates are received instantly as they are generated, without delay.
• Efficient Communication: The server only sends updates when new data is available, reducing unnecessary data transmission and optimizing system performance.
• Simplified Client-Side Implementation: SSE requires minimal client-side logic, as the data is automatically received and displayed without needing complex code to manage continuous updates.

Setting up SSE Debugging with Apidog

To get started with SSE debugging in Apidog, ensure you’re using version 2.6.49 or higher. Apidog is an excellent tool for API testing and debugging, simplifying the process of working with SSE streams and AI models.

Step 1: Create a New Endpoint in Apidog

Start by creating a new HTTP project in Apidog. This will provide a workspace for testing and debugging your API requests. Once your project is set up, add a new endpoint and enter the URL of the AI model you’re using — in this case, DeepSeek R1.

Step 2: Send the Request

After configuring your endpoint, click the Send button to initiate the request. If the server’s response header includes Content-Type: text/event-stream, Apidog will recognize the data as being streamed via SSE. The tool will automatically parse and display the data as it arrives in real-time.

Step 3: View Real-Time Responses

The magic happens in Apidog’s Timeline View. As the DeepSeek model streams responses, the Timeline view updates dynamically with each fragment of the response. This real-time display lets you track the AI model’s thought process as it generates the response, giving valuable insight into how the model is reaching its conclusions.

Step 4: Viewing SSE Response in a Complete Reply

AI responses often come in multiple fragments. Apidog’s Auto-Merge feature addresses this by automatically combining these fragments into a unified, complete response. This saves you from the hassle of manually stitching together the pieces and ensures that you have a clear, complete view of the AI’s output.

Note: This feature is particularly useful when working with popular AI models like OpenAI, Gemini, and DeepSeek.

Visualizing the Thought Process of Reasoning Models

One of the standout features of Apidog’s SSE debugging is its ability to visualize the reasoning process behind responses, especially for models like DeepSeek R1. As the AI generates its output, Apidog provides a visual representation of how the model arrived at its conclusions. This allows you to debug and understand the model’s decision-making process in real time.

Supported Formats for Auto-Merge

Apidog can automatically recognize and merge responses from several popular AI model formats:

• OpenAI API Format
• Gemini API Format
• Claude API Format

If your response format matches any of these, Apidog will seamlessly merge the fragmented data into a complete reply, simplifying the debugging process.

Why Use Auto-Merge for LLM Debugging?

• Time Efficiency: Developers can avoid the tedious task of manually merging response fragments.
• Improved Debugging: A unified, complete response allows for a clearer analysis of the AI’s behavior.
• Enhanced Insight: Visualizing the model’s thought process adds an extra layer of understanding, particularly for complex models like DeepSeek R1.

Customizing SSE Debugging Rules in Apidog

In some cases, the built-in Auto-Merge feature might not work as expected, particularly when dealing with custom AI models or non-standard formats. Apidog allows you to customize the way responses are handled using JSONPath Extraction Rules or Post-Processor Scripts.

Configuring JSONPath Extraction Rules

If the SSE response is in JSON format but does not conform to the built-in recognition rules for formats like OpenAI, Claude or Gemini, you can configure JSONPath to extract the necessary content.

For example, consider the following raw SSE response:

To extract the content of the message.content field, you would configure JSONPath as follows: $.choices[0].message.content

This configuration will pull the content: Hi

By using JSONPath, you can customize how Apidog handles responses, ensuring that you always extract the correct data.

Using Post-Processor Scripts for Non-JSON SSE

For non-JSON responses, Apidog provides the ability to use Post-Processor Scripts to manipulate and extract data from the SSE stream. This allows you to write custom scripts that handle specific data formats that don’t conform to traditional JSON structures.

Best Practices for Streaming LLM Responses with SSE

When streaming LLM responses using SSE, there are several best practices to keep in mind to ensure smooth and efficient debugging:

• Handle Fragmentation Gracefully: Always anticipate that AI model responses may come in multiple fragments, and use the Auto-Merge feature to streamline this process.
• Test with Different AI Models: Use models like OpenAI, Gemini, and DeepSeek R1 to explore the behavior of different formats and ensure your setup can handle multiple response types.
• Use Timeline View for Debugging: Leverage Apidog’s Timeline view to get a real-time, step-by-step breakdown of how responses evolve, especially for complex AI models.
• Customize for Non-Standard Formats: If necessary, use JSONPath or Post-Processor Scripts to handle non-standard SSE formats or to fine-tune the data extraction process.

Conclusion: Enhancing LLM Streaming with SSE

Server-sent events provide a powerful mechanism for streaming real-time responses from AI models, particularly when dealing with large and complex LLMs. By using Apidog’s SSE debugging tools, including the Auto-Merge feature and enhanced visualization, developers can simplify the process of handling fragmented responses and gain deeper insights into the model’s behavior. Whether you're debugging responses from popular models like OpenAI or working with custom AI solutions, Apidog ensures that you can easily track, merge, and analyze SSE data in a way that’s efficient and insightful.