> ## Documentation Index
> Fetch the complete documentation index at: https://docs-v1.latitude.so/llms.txt
> Use this file to discover all available pages before exploring further.

# IBM watsonx.ai

> Connect your IBM watsonx.ai-powered application to Latitude Telemetry for feature-level observability and evaluations.

<Note>
  This integration is only available in the **Python SDK**.
</Note>

## Overview

This guide shows you how to integrate **Latitude Telemetry** into an existing application that uses the official **IBM watsonx.ai SDK**.

After completing these steps:

* Every watsonx.ai call (e.g. `generate`, `generate_text`) can be captured as a log in Latitude.
* Logs are grouped under a **prompt**, identified by a `path`, inside a Latitude **project**.
* You can inspect inputs/outputs, measure latency, and debug watsonx.ai-powered features from the Latitude dashboard.

<Check>
  You'll keep calling watsonx.ai exactly as you do today — Telemetry simply
  observes and enriches those calls.
</Check>

***

## Requirements

Before you start, make sure you have:

* A **Latitude account** and **API key**
* A **Latitude project ID**
* A Python-based project that uses the **IBM watsonx.ai SDK** (`ibm-watsonx-ai`)

That's it — prompts do **not** need to be created ahead of time.

***

## Steps

<Steps>
  <Step title="Install requirements">
    Add the Latitude Telemetry package to your project:

    <CodeGroup>
      ```bash pip theme={null}
      pip install latitude-telemetry
      ```

      ```bash uv theme={null}
      uv add latitude-telemetry
      ```

      ```bash poetry theme={null}
      poetry add latitude-telemetry
      ```
    </CodeGroup>
  </Step>

  <Step title="Wrap your watsonx.ai-powered feature">
    Initialize Latitude Telemetry and wrap the code that calls watsonx.ai using <code>telemetry.capture</code>.

    You can use the `capture` method as a decorator (recommended) or as a context manager:

    ```python Using decorator (recommended) theme={null}
    import os
    from ibm_watsonx_ai.foundation_models import Model
    from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
    from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions

    telemetry = Telemetry(
        os.environ["LATITUDE_API_KEY"],
        TelemetryOptions(instrumentors=[Instrumentors.Watsonx]),
    )

    @telemetry.capture(
        project_id=123,  # The ID of your project in Latitude
        path="generate-support-reply",  # Add a path to identify this prompt in Latitude
    )
    def generate_support_reply(input: str) -> str:
        model = Model(
            model_id="ibm/granite-13b-chat-v2",
            credentials={"url": "https://us-south.ml.cloud.ibm.com", "apikey": os.environ["WATSONX_API_KEY"]},
            project_id=os.environ["WATSONX_PROJECT_ID"],
        )
        parameters = {
            GenParams.MAX_NEW_TOKENS: 100,
        }
        response = model.generate_text(prompt=input, params=parameters)
        return response
    ```

    ```python Using context manager theme={null}
    import os
    from ibm_watsonx_ai.foundation_models import Model
    from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
    from latitude_telemetry import Telemetry, Instrumentors, TelemetryOptions

    telemetry = Telemetry(
        os.environ["LATITUDE_API_KEY"],
        TelemetryOptions(instrumentors=[Instrumentors.Watsonx]),
    )

    def generate_support_reply(input: str) -> str:
        with telemetry.capture(
            project_id=123,  # The ID of your project in Latitude
            path="generate-support-reply",  # Add a path to identify this prompt in Latitude
        ):
            model = Model(
                model_id="ibm/granite-13b-chat-v2",
                credentials={"url": "https://us-south.ml.cloud.ibm.com", "apikey": os.environ["WATSONX_API_KEY"]},
                project_id=os.environ["WATSONX_PROJECT_ID"],
            )
            parameters = {
                GenParams.MAX_NEW_TOKENS: 100,
            }
            response = model.generate_text(prompt=input, params=parameters)
            return response
    ```

    <Info>
      The `path`:

      * Identifies the prompt in Latitude
      * Can be new or existing
      * Should not contain spaces or special characters (use letters, numbers, `- _ / .`)
    </Info>
  </Step>
</Steps>

***

## Seeing your logs in Latitude

Once your feature is wrapped, logs will appear automatically.

1. Open the **prompt** in your Latitude dashboard (identified by `path`)
2. Go to the **Traces** section
3. Each execution will show:
   * Input and output messages
   * Model and token usage
   * Latency and errors
   * One trace per feature invocation

Each watsonx.ai call appears as a child span under the captured prompt execution, giving you a full, end-to-end view of what happened.

***

## That's it

No changes to your watsonx.ai calls, no special return values, and no extra plumbing — just wrap the feature you want to observe.