OpenAI Chat Completions

Create chat completions using OpenAI-compatible API format with support for multiple AI models

Overview

chat/completions is the most common API endpoint for LLMs, which takes a conversation list composed of multiple messages as input to get model responses. This endpoint follows the OpenAI Chat Completions API format, making it easy to integrate with existing OpenAI-compatible code.

Important Notes

Model Differences

Different model providers may support different request parameters and return different response fields. We strongly recommend consulting the model catalog for complete parameter lists and usage instructions for each model.

Response Pass-through Principle

WisGate typically does not modify model responses outside of reverse format, ensuring you receive response content consistent with the original API provider.

Streaming Support

WisGate supports Server-Sent Events (SSE) for streaming responses. Set "stream": true in your request to enable real-time streaming, which is useful for chat applications.

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Auto-Generated Documentation

The request parameters and response format are automatically generated from the OpenAPI specification. All parameters, their types, descriptions, defaults, and examples are pulled directly from openapi.json. Scroll down to see the interactive API reference.

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FAQ

How to handle rate limits?

When encountering 429 Too Many Requests, we recommend implementing exponential backoff retry:

Python

import time
import random

def chat_with_retry(messages, max_retries=3):
    for i in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="gpt-4",
                messages=messages
            )
            return response
        except RateLimitError:
            if i < max_retries - 1:
                wait_time = (2 ** i) + random.random()
                time.sleep(wait_time)
            else:
                raise

How to maintain conversation context?

Include the complete conversation history in the messages array:

conversation_history = [
    {"role": "system", "content": "You are a helpful assistant"},
    {"role": "user", "content": "What is Python?"},
    {"role": "assistant", "content": "Python is a programming language..."},
    {"role": "user", "content": "What are its advantages?"}
]

response = client.chat.completions.create(
    model="gpt-4",
    messages=conversation_history
)

What does finish_reason mean?

Value	Meaning
stop	Natural completion
length	Reached max_tokens limit
content_filter	Triggered content filter
function_call	Model called a function

How to control costs?

1. Use max_tokens to limit output length
2. Choose appropriate models (e.g., GPT-3.5 Turbo is more economical than GPT-4)
3. Streamline prompts, avoid redundant context
4. Monitor token consumption in the usage field of responses

How to use streaming?

Enable streaming by setting stream: true:

Python

stream = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

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