Chat Completions

Learn how to use OpenAI's Core API endpoint to get responses from language models.

To use one of these models via the OpenAI API, you’ll send a request to the Chat Completions API containing the inputs and your API key, and receive a response containing the model’s output.

You can experiment with various models in the RockAPI Chat. If you’re not sure which model to use then try gpt-4o if you need high intelligence or gpt-4o-mini if you need the fastest speed and lowest cost.

Overview

The Chat Completions API supports text and image inputs, and can output text content (including code and JSON).

It accepts inputs via the messages parameter, which is an array of message objects.

Message roles

Each message object has a role (either system, user, or assistant) and content.

The system message is optional and can be used to set the behavior of the assistant
The user messages provide requests or comments for the assistant to respond to
Assistant messages store previous assistant responses, but can also be written by you to give examples of desired behavior (few-shot examples)

By default, the system message is "You are a helpful assistant". You can define instructions in the user message, but the instructions set in the system message are more effective. You can only set one system message per conversation.

Getting started

Chat models take a list of messages as input and return a model-generated message as output. Although the chat format is designed to make multi-turn conversations easy, it’s just as useful for single-turn tasks without any conversation.

An example Chat Completions API call looks like the following:

from openai import OpenAI

client = OpenAI(
    api_key = '$ROCKAPI_API_KEY',
    base_url = 'https://api.rockapi.ru/openai/v1'
)

response = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
    {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
    {"role": "user", "content": "Where was it played?"}
  ]
)

To learn more, you can view the full API reference documentation for the Chat API.

Including conversation history is important when user instructions refer to prior messages. In the example above, the user's final question of "Where was it played?" only makes sense in the context of the prior messages about the World Series of 2020. Because the models have no memory of past requests, all relevant information must be supplied as part of the conversation history in each request. If a conversation cannot fit within the model’s token limit, it will need to be shortened in some way.

To mimic the effect seen in ChatGPT where the text is returned iteratively, set the stream parameter to true.

from openai import OpenAI

client = OpenAI(
    api_key = '$ROCKAPI_API_KEY',
    base_url = 'https://api.rockapi.ru/openai/v1'
)

completion = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who won the world series in 2020?"},
    {"role": "assistant", "content": "The Los Angeles Dodgers won the World Series in 2020."},
    {"role": "user", "content": "Where was it played?"}
  ],
  stream=True
)

for chunk in completion:
    if not chunk.choices:
        continue

    print(chunk.choices[0].delta.content, end="")