Mistral AI
The benefits of this integration is that you can see the Mistral AI API calls in a step in the UI, and you can explore them in the prompt playground.
You will also get the full generation details (prompt, completion, tokens per second…) in your Literal AI dashboard, if your project is using Literal AI.
To benefit from tracing, you need to add cl.instrument_mistralai()
after creating your Mistral AI client.
You shouldn’t configure this integration if you’re already using another integration like Haystack, Langchain or LlamaIndex. Both integrations would record the same generation and create duplicate steps in the UI.
Prerequisites
Before getting started, make sure you have the following:
- A working installation of Chainlit
- The Mistral AI python client package installed,
mistralai
- A Mistral AI API key
- Basic understanding of Python programming
Step 1: Create a Python file
Create a new Python file named app.py
in your project directory. This file will contain the main logic for your LLM application.
Step 2: Write the Application Logic
In app.py
, import the necessary packages and define one function to handle messages incoming from the UI.
Step 3: Fill the environment variables
Create a file named .env
in the same folder as your app.py
file. Add your Mistral AI API key in the MISTRAL_API_KEY
variable.
You can optionally add your Literal AI API key in the LITERAL_API_KEY
.
Step 4: Run the Application
To start your app, open a terminal and navigate to the directory containing app.py
. Then run the following command:
The -w
flag tells Chainlit to enable auto-reloading, so you don’t need to restart the server every time you make changes to your application. Your chatbot UI should now be accessible at http://localhost:8000.