Generate chat completions - Writer AI Studio

Chat Completion Endpoint

The chat completion endpoint allows you to create a conversation between a user and an AI-assisted chat model. This guide introduces the chat completion endpoint and shows how to create a multi-turn conversation with an LLM, where the conversation history is preserved so the model understands the context of the conversation.

You need an API key to access the Writer API. Get an API key by following the steps in the API quickstart. We recommend setting the API key as an environment variable in a .env file with the name WRITER_API_KEY.

Chat Completion Versus Text Generation

The chat completion endpoint is similar to the text generation endpoint, but it is designed to handle conversations between a user and an LLM. The chat completion endpoint can generate single messages, or create more complex conversations between a user and an LLM. The text generation endpoint is designed to generate a single text response based on a given prompt. Additionally, the chat completion endpoint offers tool calling, which you can use to access other LLMs, Knowledge Graphs, and custom functions.

Endpoint Overview

URL:POST https://api.writer.com/v1/chat

Using the /chat endpoint results in charges for model usage. See the pricing page for more information.

Example cURL Request

curl --location 'https://api.writer.com/v1/chat' \
--header 'Content-Type: application/json' \
--header "Authorization: Bearer $WRITER_API_KEY" \
--data '{
    "model": "palmyra-x5",
    "messages": [
        {
            "role": "user",
            "content": "You are an expert at writing concise product descriptions for an E-Commerce Retailer"
        },
        {
            "role": "assistant",
            "content": "Okay, great I can help write these descriptions. Do you have a specific product in mind?"
        },
        {
            "role": "user",
            "content": "Please write a one sentence product description for a cozy, stylish sweater suitable for both casual and formal occasions"
        }
    ]
}'

Example Python Code

from writerai import Writer

# Initialize the client. If you don't pass the `api_key` parameter,
# the client looks for the `WRITER_API_KEY` environment variable.
client = Writer()

chat_response = client.chat.chat(
  messages=[
    {
        "role": "user",
        "content": "You are an expert at writing concise product descriptions for an E-Commerce Retailer"
    },
    {
        "role": "assistant",
        "content": "Okay, great I can help write these descriptions. Do you have a specific product in mind?"
    },
    {
        "role": "user",
        "content": "Please write a one sentence product description for a cozy, stylish sweater suitable for both casual and formal occasions"
    }
  ],
  model="palmyra-x5"
)

print(chat_response.choices[0].message.content)

Example JavaScript Code

import { Writer } from 'writer-sdk';

// Initialize the Writer client. If you don't pass the `apiKey` parameter,
// the client looks for the `WRITER_API_KEY` environment variable.
const client = new Writer();

const chatResponse = await client.chat.chat({
  messages: [
    {
        role: "user",
        content: "You are an expert at writing concise product descriptions for an E-Commerce Retailer"
    },
    {
        role: "assistant",
        content: "Okay, great I can help write these descriptions. Do you have a specific product in mind?"
    },
    {
        role: "user",
        content: "Please write a one sentence product description for a cozy, stylish sweater suitable for both casual and formal occasions"
    }
  ],
  model: 'palmyra-x5'
});

console.log(chatResponse.choices[0].message.content);

Request Body

Below are the required and commonly used optional parameters for the text generation endpoint.

Response Parameters

Non-streaming Response

If you set the stream parameter to false, the response is delivered as a single JSON object. It contains several parameters describing the response, including the choices array, which contains the generated text.

Streaming Response

If you set the stream parameter to true, the response is delivered as server-sent events. The event contains several parameters. The content of the chunk is in the choices[0].delta.content parameter.

Sample Application

The following sample application uses the Python and JavaScript SDKs to create a command-line chatbot. The application asks the user for input, passes the conversation history to the LLM, and streams the response from the LLM. It loops until the user enters the message exit.

Best Practices

Follow these best practices to ensure that your chatbot behaves as expected:

• Use system messages: Including a system message can guide the behavior of the assistant, setting expectations for its tone and responsiveness.
• Maintain context: Ensure that all relevant parts of the conversation are included in the messages array to maintain context, as the model doesn’t retain memory of past interactions.
• Handle errors gracefully: Implement error handling for various HTTP status codes and API-specific errors such as rate limits or malformed requests.
• Manage conversational flow: Regularly review the conversation’s context and adjust it to keep interactions relevant and concise, especially under the model’s token limit.

Next Steps

Now that you’ve created a chatbot, learn how to add tool calling to your application to enhance the functionality with other LLMs, Knowledge Graphs, and custom functions.