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ChatMistralAI

This will help you getting started with Mistral chat models. For detailed documentation of all ChatMistralAI features and configurations head to the API reference. The ChatMistralAI class is built on top of the Mistral API. For a list of all the models supported by Mistral, check out this page.

Overviewโ€‹

Integration detailsโ€‹

ClassPackageLocalSerializableJS supportPackage downloadsPackage latest
ChatMistralAIlangchain_mistralaiโŒbetaโœ…PyPI - DownloadsPyPI - Version

Model featuresโ€‹

Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingNative asyncToken usageLogprobs
โœ…โœ…โœ…โŒโŒโŒโœ…โœ…โœ…โŒ

Setupโ€‹

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

Credentialsโ€‹

A valid API key is needed to communicate with the API. Once you've done this set the MISTRAL_API_KEY environment variable:

import getpass
import os

if "MISTRAL_API_KEY" not in os.environ:
os.environ["MISTRAL_API_KEY"] = getpass.getpass("Enter your Mistral 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["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"

Installationโ€‹

The LangChain Mistral integration lives in the langchain_mistralai package:

%pip install -qU langchain_mistralai

Instantiationโ€‹

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

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(
model="mistral-large-latest",
temperature=0,
max_retries=2,
# other params...
)
API Reference:ChatMistralAI

Invocationโ€‹

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='Sure, I\'d be happy to help you translate that sentence into French! The English sentence "I love programming" translates to "J\'aime programmer" in French. Let me know if you have any other questions or need further assistance!', response_metadata={'token_usage': {'prompt_tokens': 32, 'total_tokens': 84, 'completion_tokens': 52}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-64bac156-7160-4b68-b67e-4161f63e021f-0', usage_metadata={'input_tokens': 32, 'output_tokens': 52, 'total_tokens': 84})
print(ai_msg.content)
Sure, I'd be happy to help you translate that sentence into French! The English sentence "I love programming" translates to "J'aime programmer" in French. Let me know if you have any other questions or need further assistance!

Chainingโ€‹

We can chain our model with a prompt template like so:

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages(
[
(
"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 Programmierung. (German translation)', response_metadata={'token_usage': {'prompt_tokens': 26, 'total_tokens': 38, 'completion_tokens': 12}, 'model': 'mistral-small', 'finish_reason': 'stop'}, id='run-dfd4094f-e347-47b0-9056-8ebd7ea35fe7-0', usage_metadata={'input_tokens': 26, 'output_tokens': 12, 'total_tokens': 38})

API referenceโ€‹

Head to the API reference for detailed documentation of all attributes and methods.


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