Tracing Mistral

MLflow Tracing ensures observability for your interactions with Mistral AI models. When Mistral auto-tracing is enabled by calling the mlflow.mistral.autolog() function, usage of the Mistral SDK will automatically record generated traces during interactive development.

Note that only synchronous calls to the Text Generation API are supported, and that asynchronous API and streaming methods are not traced.

Example Usage

import os

from mistralai 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)

Token usage

MLflow >= 3.2.0 supports token usage tracking for Mistral. The token usage for each LLM call will be logged in the mlflow.chat.tokenUsage attribute. The total token usage throughout the trace will be available in the token_usage field of the trace info object.

import json
import mlflow

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.",
        },
    ],
)

# Get the trace object just created
last_trace_id = mlflow.get_last_active_trace_id()
trace = mlflow.get_trace(trace_id=last_trace_id)

# Print the token usage
total_usage = trace.info.token_usage
print("== Total token usage: ==")
print(f"  Input tokens: {total_usage['input_tokens']}")
print(f"  Output tokens: {total_usage['output_tokens']}")
print(f"  Total tokens: {total_usage['total_tokens']}")

# Print the token usage for each LLM call
print("\n== Detailed usage for each LLM call: ==")
for span in trace.data.spans:
    if usage := span.get_attribute("mlflow.chat.tokenUsage"):
        print(f"{span.name}:")
        print(f"  Input tokens: {usage['input_tokens']}")
        print(f"  Output tokens: {usage['output_tokens']}")
        print(f"  Total tokens: {usage['total_tokens']}")

== Total token usage: ==
  Input tokens: 16
  Output tokens: 25
  Total tokens: 41

== Detailed usage for each LLM call: ==
Chat.complete:
  Input tokens: 16
  Output tokens: 25
  Total tokens: 41

Disable auto-tracing

Auto tracing for Mistral can be disabled globally by calling mlflow.mistral.autolog(disable=True) or mlflow.autolog(disable=True).