Track and monitor MistralAI model calls with Weave’s automatic tracing for chat completions, function calling, and model interactions.
Weave automatically tracks and logs LLM calls made through the MistralAI Python library. This guide shows you how to capture traces for MistralAI calls, wrap calls in your own ops for reproducibility, and organize experiments using the Model class.
Weave supports the Mistral v1.0 SDK. For details on upgrading from v0 to v1, see the migration guide.
It’s important to store traces of LLM applications in a central database, both during development and in production. You use these traces for debugging, and as a dataset that helps you improve your application.Weave automatically captures traces for mistralai. To use the library as usual, start by calling weave.init():
import weaveweave.init("cheese_recommender")# then use mistralai library as usualimport osfrom mistralai import Mistralapi_key = os.environ["MISTRAL_API_KEY"]model = "mistral-large-latest"client = Mistral(api_key=api_key)messages = [ { "role": "user", "content": "What is the best French cheese?", },]chat_response = client.chat.complete( model=model, messages=messages,)
Weave now tracks and logs all LLM calls made through the MistralAI library. You can view the traces in the Weave web interface.
Once basic tracing works, you can wrap your MistralAI calls in Weave Ops to make them reproducible across experiments. Weave ops make results reproducible by automatically versioning code as you experiment, and they capture their inputs and outputs. Create a function decorated with @weave.op() that calls into client.chat.complete(), and Weave tracks the inputs and outputs for you. The following example shows how to do this for a cheese recommender:
@weave.op()def cheese_recommender(region:str, model:str) -> str: "Recommend the best cheese in a given region" messages = [ { "role": "user", "content": f"What is the best cheese in {region}?", }, ] chat_response = client.chat.complete( model=model, messages=messages, ) return chat_response.choices[0].message.contentcheese_recommender(region="France", model="mistral-large-latest")cheese_recommender(region="Spain", model="mistral-large-latest")cheese_recommender(region="Netherlands", model="mistral-large-latest")
Organizing experimentation is difficult with many moving pieces. By using the Model class, you can capture and organize the experimental details of your app like your system prompt or the model you’re using. This helps organize and compare different iterations of your app.Besides versioning code and capturing inputs and outputs, Models capture structured parameters that control your application’s behavior, making it easy to find what parameters worked best. You can also use Weave Models with serve, and Evaluations.In the following example, you can experiment with model and country. Every time you change one of these, you get a new version of CheeseRecommender.
import weavefrom mistralai import Mistralweave.init("mistralai_project")class CheeseRecommender(weave.Model): # Change to `weave.Model` model: str temperature: float @weave.op() def predict(self, region:str) -> str: # Change to `predict` "Recommend the best cheese in a given region" client = Mistral(api_key=api_key) messages = [ { "role": "user", "content": f"What is the best cheese in {region}?", }, ] chat_response = client.chat.complete( model=model, messages=messages, temperature=self.temperature ) return chat_response.choices[0].message.contentcheese_model = CheeseRecommender( model="mistral-medium-latest", temperature=0.0 )result = cheese_model.predict(region="France")print(result)
