Text generation

The Mistral models allows you to chat with a model that has been fine-tuned to follow instructions and respond to natual lanugage prompts. A prompt is the input that you provide to the Mistral model. It can come in various forms, such as asking a question, giving an instruction, or providing a few examples of the task you want the model to perform. Based on the prompt, the Mistral model generates a text output as a response.

The chat completion API accepts a list of chat messages as input and generates a response. This response is in the form of a new chat message with the role "assistant" as output.

python

No streaming

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage

api_key = os.environ["MISTRAL_API_KEY"]
model = "mistral-large-latest"

client = MistralClient(api_key=api_key)

messages = [
    ChatMessage(role="user", content="What is the best French cheese?")
]

# No streaming
chat_response = client.chat(
    model=model,
    messages=messages,
)

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

With streaming

from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage

api_key = os.environ["MISTRAL_API_KEY"]
model = "mistral-large-latest"

client = MistralClient(api_key=api_key)

messages = [
    ChatMessage(role="user", content="What is the best French cheese?")
]

# With streaming
stream_response = client.chat_stream(model=model, messages=messages)

for chunk in stream_response:
    print(chunk.choices[0].delta.content)

With async

from mistralai.async_client import MistralAsyncClient
from mistralai.models.chat_completion import ChatMessage

api_key = os.environ["MISTRAL_API_KEY"]
model = "mistral-large-latest"

client = MistralAsyncClient(api_key=api_key)

messages = [
    ChatMessage(role="user", content="What is the best French cheese?")
]

# With async
async_response = client.chat_stream(model=model, messages=messages)

async for chunk in async_response: 
    print(chunk.choices[0].delta.content)

javascript

No streaming

import MistralClient from '@mistralai/mistralai';

const apiKey = process.env.MISTRAL_API_KEY;

const client = new MistralClient(apiKey);

const chatResponse = await client.chat({
  model: 'mistral-large-latest',
  messages: [{role: 'user', content: 'What is the best French cheese?'}],
});

console.log('Chat:', chatResponse.choices[0].message.content);

With streaming

import MistralClient from '@mistralai/mistralai';

const apiKey = process.env.MISTRAL_API_KEY;

const client = new MistralClient(apiKey);

const chatStreamResponse = await client.chatStream({
  model: 'mistral-tiny',
  messages: [{role: 'user', content: 'What is the best French cheese?'}],
});

console.log('Chat Stream:');
for await (const chunk of chatStreamResponse) {
  if (chunk.choices[0].delta.content !== undefined) {
    const streamText = chunk.choices[0].delta.content;
    process.stdout.write(streamText);
  }
}

curl

curl --location "https://api.mistral.ai/v1/chat/completions" \
     --header 'Content-Type: application/json' \
     --header 'Accept: application/json' \
     --header "Authorization: Bearer $MISTRAL_API_KEY" \
     --data '{
    "model": "mistral-large-latest",
    "messages": [
     {
        "role": "user",
        "content": "What is the best French cheese?"
      }
    ]
  }'

Chat messages

Chat messages (messages) are a collection of prompts or messages, with each message having a specific role assigned to it, such as "system," "user," "assistant," or "tool."

• A system message is an optional message that sets the behavior and context for an AI assistant in a conversation, such as modifying its personality or providing specific instructions. A system message can include task instructions, personality traits, contextual information, creativity constraints, and other relevant guidelines to help the AI better understand and respond to the user's input. See the API reference for explanations on how to set up a custom system prompt.
• A user message is a message sent from the perspective of the human in a conversation with an AI assistant. It typically provides a request, question, or comment that the AI assistant should respond to. User prompts allow the human to initiate and guide the conversation, and they can be used to request information, ask for help, provide feedback, or engage in other types of interaction with the AI.
• An assistant message is a message sent by the AI assistant back to the user. It is usually meant to reply to a previous user message by following its instructions, but you can also find it at the beginning of a conversation, for example to greet the user.
• A tool message only appears in the context of function calling, it is used at the final response formulation step when the model has to format the tool call's output for the user. To learn more about function calling, see the guide.

When to use user prompt vs. system message then user message?

• You can either combine your system message and user message into a single user message or separate them into two distinct messages.
• We recommend you experiment with both ways to determine which one works better for your specific use case.

We also allow a convenient safe_prompt flag to force chat completion to be moderated against sensitive content (see Guardrailing).