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Observability with Mistral AI and MLflow

EvaluationMlflow+1

This is an example for leveraging MLflow's auto tracing capabilities for Mistral AI.

More information about MLflow Tracing is available here.

Getting Started#

Install mistralai and mlflow (current versions as of 4-Feb-2025)

!pip install mistralai==1.5.0
!pip install mlflow==2.20.1

!pip install mistralai==1.5.0
!pip install mlflow==2.20.1

Code#

import os

from mistralai.client import Mistral

import mlflow

# Turn on auto tracing for Mistral AI by calling mlflow.mistral.autolog()
mlflow.mistral.autolog()

# Configure your API key.
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

# Use the chat complete method to create new chat.
chat_response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        },
    ],
)
print(chat_response.choices[0].message)

import os

from mistralai.client import Mistral

import mlflow

# Turn on auto tracing for Mistral AI by calling mlflow.mistral.autolog()
mlflow.mistral.autolog()

# Configure your API key.
client = Mistral(api_key=os.environ["MISTRAL_API_KEY"])

# Use the chat complete method to create new chat.
chat_response = client.chat.complete(
    model="mistral-small-latest",
    messages=[
        {
            "role": "user",
            "content": "Who is the best French painter? Answer in one short sentence.",
        },
    ],
)
print(chat_response.choices[0].message)

Tracing#

To see the MLflow tracing, open the MLflow UI in the same directory and the same virtualenv where you run this notebook.

Launch the UI#

Open a terminal and run this command:

mlflow ui

Screenshot of launching the MLflow UI via command line

View the traces in the browser#

Open your browser and connect to the MLflow UI port (default: http://localhost:5000)

GIF animation of browsing a trace in the MLflow UI