> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Chat Completions > This guide explains migrating to the modern chat completions API for enhanced conversational experiences with OpenAI models. Unlock richer conversational experiences by switching from legacy completions endpoints to the modern `chat.completions.create` interface. This guide walks you through migrating your code, customizing your client, leveraging message roles, and fine-tuning parameters for GPT-3.5-Turbo, GPT-4, and beyond. *** ## Why Migrate from Legacy Endpoints OpenAI’s older `completions.create` and `Completion.create` endpoints stopped receiving updates as of July 2023. The new chat completions API supports structured conversations with roles, function calls, and more control over model behavior. Avoid using legacy calls: they no longer receive feature updates and may be removed in future releases. ### Legacy Usage Examples ```python theme={null} # Legacy OpenAI Python SDK (no longer recommended) from openai import OpenAI client = OpenAI() response = client.completions.create( model="gpt-3.5-turbo-instruct", prompt="Write a tagline for an ice cream shop." ) ``` ```python theme={null} # Legacy openai-python (also deprecated) import openai response = openai.Completion.create( model="gpt-3.5-turbo-instruct", prompt="Write a tagline for an ice cream shop." ) ``` *** ## Modern Chat Completions Switch to the chat API for richer, role-based interactions: ```python theme={null} from openai import OpenAI openai = OpenAI(api_key="sk-...") response = openai.chat.completions.create( model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Write a tagline for an ice cream shop."}] ) print(response.choices[0].message.content) ``` *** ## Naming Your OpenAI Client Feel free to name your client object whatever you like. Here’s a standard pattern: ```python theme={null} from openai import OpenAI openai = OpenAI(api_key="sk-...") def chat_comp(prompt): response = openai.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": prompt}], max_tokens=250 ) return response.choices[0].message.content print(chat_comp("How can I make more money?")) ``` Or with a custom client name: ```python theme={null} from openai import OpenAI RobotBestFriend = OpenAI(api_key="sk-...") def chat_comp(prompt): response = RobotBestFriend.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": prompt}], max_tokens=250 ) return response.choices[0].message.content print(chat_comp("How can I make more money?")) ``` Console output: ```plaintext theme={null} There are several strategies you can consider to increase your income. Here are some ideas: 1. **Negotiate Your Salary:** ... 2. **Acquire New Skills:** ... 3. **Side Gigs:** ... 4. **Investing:** ... 5. **Start a Business:** ... 6. **Monetize Hobbies:** ... ``` Never commit your `api_key` to public repositories. Use environment variables or secret management tools. *** ## Roles in Chat Messages The `messages` array defines each turn in a conversation. Supported roles: | Role | Purpose | Example | | --------- | --------------------------------------------------------- | -------------------------------------------------------------- | | system | Sets global context, tone, or behavior for the assistant. | `"You are a helpful assistant."` | | user | End user’s input or question. | `"What's the weather today?"` | | assistant | Model’s prior responses in a multi-turn conversation. | `"The weather is sunny and 75°F."` | | function | Allows the model to call a custom function you define. | `"function_call": {"name": "get_weather", "arguments": {...}}` | ### JavaScript Example ```javascript theme={null} const openai = new OpenAI({ apiKey: "sk-..." }); const response = await openai.chat.completions.create({ model: "gpt-4", messages: [ { role: "system", content: [ { type: "text", text: "You are a friendly coding tutor who uses southern idioms." } ] }, { role: "user", content: [ { type: "text", text: "Are semicolons optional in JavaScript?" } ] } ] }); console.log(response.choices[0].message.content); ``` *** ## Adding a System Prompt in Python Include both `system` and `user` messages to guide the model’s tone: ```python theme={null} from openai import OpenAI RobotBestFriend = OpenAI(api_key="sk-...") def chat_comp(prompt): response = RobotBestFriend.chat.completions.create( model="gpt-4", messages=[ {"role": "system", "content": "You are a southern belle."}, {"role": "user", "content": prompt} ], max_tokens=250 ) return response.choices[0].message.content print(chat_comp("What are some side hustles I can try?")) ``` Console output: ```bash theme={null} There are several ways to bring a little more income if you set your mind to it, sugar... ``` Place the `system` message before the `user` message to ensure the context is applied first. *** ## Exploring the `create` Signature The `chat.completions.create` method supports numerous parameters for fine-tuning: ```python theme={null} def create( *, model: str, messages: Iterable[ChatCompletionMessageParam], max_tokens: int | None = None, n: int | None = None, temperature: float | None = None, top_p: float | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, logit_bias: dict[str, int] | None = None, functions: Iterable[Function] | None = None, function_call: FunctionCall | None = None, stream: bool | None = None, ... ): ... ``` Experiment with these options for customized responses: * **temperature**: Controls randomness. * **top\_p**: Nucleus sampling probability. * **presence\_penalty** & **frequency\_penalty**: Encourage topic variety. * **functions** & **function\_call**: Invoke your own code. *** ## Links and References * [OpenAI Chat Completions API](https://platform.openai.com/docs/api-reference/chat) * [Kubernetes Basics](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/) * [Docker Hub](https://hub.docker.com/) * [Terraform Registry](https://registry.terraform.io/)