How do I handle errors in multi-turn conversations with DeepSeek API

Understanding Error Types

DeepSeek API can encounter various errors, including HTTP errors, invalid request formats, authentication issues, and server errors. Each type of error requires a different approach to resolve.

Implementing Error Handling

1. HTTP Errors: These occur when the server returns a bad status code. You can catch these using `requests.exceptions.HTTPError`.

2. Invalid Request Formats: Ensure that your request body is correctly formatted according to the API documentation. If you encounter a `400 - Invalid Format` error, adjust your request format accordingly.

3. Authentication Issues: If you receive a `401 - Authentication Fails` error, verify that your API key is correct and properly set in your requests.

4. Server Errors: For `500 - Server Error` or `503 - Server Overloaded`, retry your request after a brief wait.

Example Code for Error Handling

Here's how you can modify your code to handle potential errors when conducting multi-turn conversations with the DeepSeek API:

python
import requests

# Initialize the conversation
conversation = [
    {"role": "user", "content": "Hi, can you help me with some science questions?"}
]

# Set API details
url = "https://api.deepseek.com/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

try:
    # First API call
    response = requests.post(
        url,
        headers=headers,
        json={"model": "deepseek-chat", "messages": conversation}
    )
    response.raise_for_status()  # Raises an exception for bad status codes
    
    # Extract assistant's reply
    assistant_reply = response.json()['choices']['message']['content']
    print(f"Assistant: {assistant_reply}")
    
    # Add the assistant's reply to the conversation
    conversation.append({"role": "assistant", "content": assistant_reply})
    
    # User's next message
    user_message = "Sure! What’s the speed of light?"
    conversation.append({"role": "user", "content": user_message})
    
    # Second API call
    response = requests.post(
        url,
        headers=headers,
        json={"model": "deepseek-chat", "messages": conversation}
    )
    response.raise_for_status()
    
    # Extract and print assistant's reply
    assistant_reply = response.json()['choices']['message']['content']
    print(f"Assistant: {assistant_reply}")
    
except requests.exceptions.HTTPError as err:
    print(f"HTTP error occurred: {err}")
except Exception as err:
    print(f"An error occurred: {err}")

Best Practices for Multi-Turn Conversations

- Maintain Context: Ensure that you pass the entire conversation history with each request to maintain context.
- Prompt Crafting: Craft your prompts carefully to get high-quality responses from the AI.
- Error Logging: Implement logging to track and analyze errors for future improvements.

By following these guidelines, you can effectively handle errors and build robust multi-turn conversation systems with the DeepSeek API.

Citations:
[1] https://froala.com/blog/general/deepseek-api-integration-guide/
[2] https://stackoverflow.com/questions/78362581/error-when-trying-to-do-multiturn-chat-with-gemini-pro
[3] https://www.together.ai/blog/fine-tuning-llms-for-multi-turn-conversations-a-technical-deep-dive
[4] https://api-docs.deepseek.com/guides/multi_round_chat
[5] https://api-docs.deepseek.com/quick_start/error_codes
[6] https://docs.ragas.io/en/stable/howtos/applications/evaluating_multi_turn_conversations/
[7] https://www.conversationdesigninstitute.com/blog/error-handling-tips-to-make-your-chatbot-more-helpful-natural-and-persuasive
[8] https://www.byteplus.com/en/topic/385345
[9] https://poly.ai/blog/multi-turn-conversations-what-are-they-and-why-do-they-matter-for-your-customers/