IBM watsonx

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()

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
}

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)