Skip to main content

Tracing a GenAI App (IDE)

This quickstart helps you integrate your GenAI app with MLflow Tracing if you use a local IDE as your development environment. If you use a Databricks Notebook, please use the Databricks Notebook quickstart instead.

What you'll achieve​

By the end of this tutorial, you will have:

  • A MLflow Experiment for your GenAI app
  • Your local development environment connected to MLflow
  • A simple GenAI application instrumented with MLflow Tracing
  • A trace from that app in your MLflow Experiment

trace

Prerequisites​

  • Databricks Workspace: Access to a Databricks workspace.

Step 1: Install MLflow​

When working in your local IDE, you need to install MLflow with Databricks connectivity.

pip install --upgrade "mlflow[databricks]>=3.1" openai

Step 2: Create a new MLflow Experiment​

An MLflow Experiment is the container for your GenAI application. Learn more about the Experiment and what it contains in the data model section.

  1. Open your Databricks workspace
  2. Go to Experiments in the left sidebar under Machine Learning
  3. At the top of the Experiments page, click on New GenAI Experiment

Experiment Creation

Step 3: Connect your environment to MLflow​

note

This quickstart describes using a Databricks Personal Access Token. MLflow also works with the other Databricks-supported authentication methods.

  1. Click Generate API Key

  2. Copy and run the generated code in your terminal.

    Bash
    export DATABRICKS_TOKEN=<databricks-personal-access-token>
    export DATABRICKS_HOST=https://<workspace-name>.cloud.databricks.com
    export MLFLOW_TRACKING_URI=databricks
    export MLFLOW_EXPERIMENT_ID=<experiment-id>

Step 4: Create and instrument your application​

tip

Databricks provided out of the box access to popular frontier and open source foundational LLMs. To run this quickstart, you can:

  1. Use the Databricks hosted LLMs
  2. Directly use your own API key from an LLM provider
  3. Create an external model to enable governed access to your LLM provider's API keys

The below example quickstart uses the OpenAI SDK to connect to a Databricks hosted LLM. If you want to use your own OpenAI key, update the client = OpenAI(...) line.

If you prefer to use one of the other 20+ LLM SDKs (Anthropic, Bedrock, etc) or GenAI authoring frameworks (LangGraph, etc) that MLflow supports, follow the instructions in the MLflow Experiment UI in the previous step.

  1. Create a Python file named app.py in your project directory.

    Here, we use the @mlflow.trace decorator that makes it easy to trace any Python application combined with the OpenAI automatic instrumentation to captures the details of the call to the OpenAI SDK.

    The code snippet below uses Anthropic's Claude Sonnet LLM. You can select another LLM from the list of supported foundational models.

    Python
    import mlflow
    from openai import OpenAI

    # Enable MLflow's autologging to instrument your application with Tracing
    mlflow.openai.autolog()

    # Connect to a Databricks LLM via OpenAI using the same credentials as MLflow
    # Alternatively, you can use your own OpenAI credentials here
    mlflow_creds = mlflow.utils.databricks_utils.get_databricks_host_creds()
    client = OpenAI(
        api_key=mlflow_creds.token,
        base_url=f"{mlflow_creds.host}/serving-endpoints"
    )

    # Use the trace decorator to capture the application's entry point
    @mlflow.trace
    def my_app(input: str):
      # This call is automatically instrumented by `mlflow.openai.autolog()`
      response = client.chat.completions.create(
        model="databricks-claude-sonnet-4",  # This example uses a Databricks hosted LLM - you can replace this with any AI Gateway or Model Serving endpoint. If you provide your own OpenAI credentials, replace with a valid OpenAI model e.g., gpt-4o, etc.
        messages=[
          {
            "role": "system",
            "content": "You are a helpful assistant.",
          },
          {
            "role": "user",
            "content": input,
          },
        ],
      )
      return response.choices[0].message.content

    result = my_app(input="What is MLflow?")
    print(result)

  2. Run the application

    Bash
    python app.py

Step 5: View the Trace in MLflow​

  1. Navigate back to the MLflow Experiment UI
  2. You will now see the generated trace in the Traces tab
  3. Click on the trace to view its details

Trace Details

Understanding the Trace​

The trace you've just created shows:

  • Root Span: Represents the inputs to the my_app(...) function
    • Child Span: Represents the OpenAI completion request
  • Attributes: Contains metadata like model name, token counts, and timing information
  • Inputs: The messages sent to the model
  • Outputs: The response received from the model

This simple trace already provides valuable insights into your application's behavior, such as:

  • What was asked
  • What response was generated
  • How long the request took
  • How many tokens were used (affecting cost)
tip

For more complex applications like RAG systems or multi-step agents, MLflow Tracing provides even more value by revealing the inner workings of each component and step.

Next steps​

Continue your journey with these recommended actions and tutorials.

Reference guides​

Explore detailed documentation for concepts and features mentioned in this guide.

Last updated on