MistralAI

This will help you getting started with Mistral chat models, accessed via their API. For detailed documentation of all ChatMistralAI features and configurations head to the API reference.

Overview

Integration details

Class	Package	Local	Serializable	JS support	Package downloads	Package latest
ChatMistralAI	langchain_mistralai	❌	beta	✅

Model features

Tool calling	Structured output	JSON mode	Image input	Audio input	Video input	Token-level streaming	Native async	Token usage	Logprobs
✅	✅	❌	❌	❌	❌	✅	✅	✅	❌

Setup

To access Mistral models you'll need to create a Mistral account, get an API key, and install the langchain-mistralai integration package.

Credentials

A valid API key is needed to communicate with the API. Once you've obtained an API key, store it in the MISTRAL_API_KEY environment variable:

import getpass
import os

if not os.getenv("__MODULE_NAME___API_KEY"):
    os.environ["__MODULE_NAME___API_KEY"] = getpass.getpass(
        "Enter your __ModuleName__ API key: "
    )

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")

Installation

The LangChain MistralAI integration lives in the langchain-mistralai package:

%pip install -qU langchain-mistralai

Instantiation

Now we can instantiate our model object and generate chat completions:

from langchain_mistralai.chat_models import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

API Reference:ChatMistralAI

Invocation

messages = [
    (
        "system",
        "You are a helpful assistant that translates English to French. Translate the user sentence.",
    ),
    ("human", "I love programming."),
]
ai_msg = llm.invoke(messages)
ai_msg

AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'prompt_tokens': 27, 'total_tokens': 36, 'completion_tokens': 9}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-d6196c33-9410-413b-b454-4ed0bec1f0c7-0', usage_metadata={'input_tokens': 27, 'output_tokens': 9, 'total_tokens': 36})

print(ai_msg.content)

J'adore la programmation.

Async

await llm.ainvoke(messages)

AIMessage(content="J'aime programmer.", response_metadata={'token_usage': {'prompt_tokens': 27, 'total_tokens': 34, 'completion_tokens': 7}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-1873888a-186f-49a8-ab81-24335bd3099b-0', usage_metadata={'input_tokens': 27, 'output_tokens': 7, 'total_tokens': 34})

Streaming

for chunk in llm.stream(messages):
    print(chunk.content, end="")

J'adore programmer.

Batch

llm.batch([messages])

[AIMessage(content="J'adore la programmation.", response_metadata={'token_usage': {'prompt_tokens': 27, 'total_tokens': 36, 'completion_tokens': 9}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-2aa2a189-c405-4cf5-bd31-e9025e4c8536-0', usage_metadata={'input_tokens': 27, 'output_tokens': 9, 'total_tokens': 36})]

Chaining

You can also easily combine with a prompt template for easy structuring of user input. We can do this using LCEL

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate(
    [
        (
            "system",
            "You are a helpful assistant that translates {input_language} to {output_language}.",
        ),
        ("human", "{input}"),
    ]
)

chain = prompt | llm
chain.invoke(
    {
        "input_language": "English",
        "output_language": "German",
        "input": "I love programming.",
    }
)

API Reference:ChatPromptTemplate

AIMessage(content='Ich liebe Programmieren.', response_metadata={'token_usage': {'prompt_tokens': 21, 'total_tokens': 28, 'completion_tokens': 7}, 'model': 'mistral-large-latest', 'finish_reason': 'stop'}, id='run-409ebc9a-b4a0-4734-ab6f-e11f6b4f808f-0', usage_metadata={'input_tokens': 21, 'output_tokens': 7, 'total_tokens': 28})

API reference

For detailed documentation of all ChatMistralAI features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html

Related

• Chat model conceptual guide
• Chat model how-to guides