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

# IBM watsonx

The nebuly platform provides full support for the IBM watsonx SDK. In this section,
we will show you how to easily monitor all the requests made to the IBM watsonx models.

First of all, let's perform a simple chat request using the IBM watsonx SDK, tracking
the interaction time start and time end:

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    import datetime
    from ibm_watsonx_ai import Credentials
    from ibm_watsonx_ai.foundation_models import Model
    from ibm_watsonx_ai.foundation_models.utils.enums import ModelTypes

    ibm_model_id = ModelTypes.GRANITE_20B_CODE_INSTRUCT
    time_start = datetime.datetime.utcnow().isoformat()
    model = Model(
        model_id=ibm_model_id,
        credentials=Credentials(
            api_key="<IAM_API_KEY>",
            url="<IBM_URL>"
        ),
        project_id="<IBM_PROJECT_ID>"
    )

    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who won the world series in 2020?"}
    ]
    generated_response = model.chat(messages=messages)
    assistant = generated_response['choices'][0]['message']['content']
    time_end = datetime.datetime.utcnow().isoformat()
    ```
  </Tab>
</Tabs>

Now, let's build the payload with all the useful information to be sent to the nebuly
platform. We are going to include all the conversation details, along with the
details of the model used that are used in the platform to compute and track the cost
of the interaction.

<Note>
  The costs are available only for the following IBM Foundation Granite models:

  * ibm/granite-13b-instruct-v2
  * ibm/granite-8b-japanese
  * ibm/granite-20b-multilingual
  * ibm/granite-3-2b-instruct
  * ibm/granite-3-8b-instruct
  * ibm/granite-guardian-3-2b
  * ibm/granite-guardian-3-8b
  * ibm/granite-3b-code-instruct
  * ibm/granite-8b-code-instruct
  * ibm/granite-20b-code-instruct
  * ibm/granite-34b-code-instruct

  You can send the traces also for other Granite and Third Party models, but the
  cost will not be computed.
</Note>

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    import uuid

    nebuly_traces = [  # Optional, it can be an empty list
        {
            "model": ibm_model_id.value,
            "messages": messages,
            "output": assistant,
            "input_tokens": generated_response['usage']['prompt_tokens'],  # Needed to compute the cost
            "output_tokens": generated_response['usage']['completion_tokens'],  # Needed to compute the cost
        }
    ]

    request_body = {
        "interaction": {
            "conversation_id": str(uuid.uuid4()),
            "input": messages[-1]['content'],
            "output": assistant,
            "time_start": time_start,
            "time_end": time_end,
            "end_user": "<USER_ID>"
        },
        "anonymize": False,
        "traces": nebuly_traces
    }
    ```
  </Tab>
</Tabs>

At this point, we have all the information needed to send the request to the nebuly platform.
The following code snippet shows how to easily send the request:

<Tabs>
  <Tab title="Python">
    ```python theme={null}
    import requests

    url = "https://backend.nebuly.com/event-ingestion/api/v2/events/trace_interaction"
    headers = {
        "Authorization": "Bearer <NEBULY_API_KEY>",
        "Content-Type": "application/json"
    }

    response = requests.post(url, json=request_body, headers=headers)
    ```
  </Tab>
</Tabs>

You can find a detailed explanation of the some of the specific parameters used in the
code snippets above:

<ParamField path="user_id" type="string" required>
  An id or username uniquely identifying the end-user. We recommend hashing their username or email address, in order to avoid sending us any identifying information.
</ParamField>

<ParamField path="conversation_id" type="string" required>
  A unique identifier for the conversation. It is used to group all the single interactions exchanged during the conversation.
</ParamField>

<ParamField path="anonymize" type="boolean">
  If set to `True`, a PII detection algorithm will be applied to the input and output messages to remove any personal information.
</ParamField>

You can find more details in the [API Reference](/tracking/api-reference/events/post-events-interaction-with-trace-v2) section.