IBM WatsonX Integration

Lunary has partnered with IBM to provide a seamless integration for monitoring WatsonX calls in your Python app.

Our Python SDK includes automatic integration with IBM WatsonX’s foundation models using Lunary.

1. Setup the SDK

   First, ensure you have installed the IBM WatsonX SDK and Lunary. Set your environment variables for IBM authentication.

   pip install ibm-watsonx-ai lunary
   

   Configure your environment variables:

   • IBM_API_KEY: your IBM API key
   • IBM_PROJECT_ID: your IBM project id
2. Monitor IBM WatsonX calls

   Wrap your WatsonX model instance with Lunary’s monitor method to automatically track your calls.

   import os
   from ibm_watsonx_ai import Credentials
   from ibm_watsonx_ai.foundation_models import ModelInference
   import lunary
   
   model = ModelInference(
       model_id="meta-llama/llama-3-1-8b-instruct",
       credentials=Credentials(
           api_key=os.environ.get("IBM_API_KEY"),
           url="https://us-south.ml.cloud.ibm.com"
       ),
       project_id=os.environ.get("IBM_PROJECT_ID")
   )
   
   lunary.monitor(model)
   
   messages = [
       {"role": "system", "content": "You are a helpful assistant."},
       {"role": "user", "content": "Who won the world series in 2020?"}
   ]
   
   response = model.chat(messages=messages)
   
3. Tag requests and identify users

   Optionally, pass extra parameters to track details such as tags and user information by including additional arguments to the chat call.

   response = model.chat(messages=messages, tags=["baseball"], user_id="1234", user_props={"name": "Alice"})