View Notebook on Github

​
IBM Watsonx.ai Text Generation and Chat with AgentOps

This notebook demonstrates how to use IBM Watsonx.ai for basic text generation and chat completion tasks with AgentOps instrumentation.

​
Installation

Install the required packages:

pip install agentops ibm-watsonx-ai python-dotenv

​
Setup

First, let’s import the necessary libraries and initialize AgentOps:

import agentops
from ibm_watsonx_ai import Credentials
from ibm_watsonx_ai.foundation_models import ModelInference
from dotenv import load_dotenv
import os

# Load environment variables
load_dotenv()
os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_agentops_api_key_here")

# Initialize AgentOps
agentops.init(tags=["watsonx-text-chat", "agentops-example"])

​
Initialize IBM Watsonx.ai Credentials

To use IBM Watsonx.ai, you need to set up your credentials and project ID.

# Initialize credentials - replace with your own API key
# Best practice: Store API keys in environment variables
# Ensure WATSONX_API_KEY, WATSONX_URL, and WATSONX_PROJECT_ID are set in your .env file or environment
os.environ["WATSONX_API_KEY"] = os.getenv("WATSONX_API_KEY", "your_watsonx_api_key_here")
os.environ["WATSONX_URL"] = os.getenv("WATSONX_URL", "https://eu-de.ml.cloud.ibm.com") # Example URL, ensure it's correct for your region
os.environ["WATSONX_PROJECT_ID"] = os.getenv("WATSONX_PROJECT_ID", "your-project-id-here")


credentials = Credentials(
    url=os.environ["WATSONX_URL"],
    api_key=os.environ["WATSONX_API_KEY"],
)

# Project ID for your IBM Watsonx project
project_id = os.environ["WATSONX_PROJECT_ID"]

​
Text Generation

Let’s use IBM Watsonx.ai to generate text based on a prompt:

# Initialize text generation model
gen_model = ModelInference(model_id="google/flan-ul2", credentials=credentials, project_id=project_id)

# Generate text with a prompt
prompt = "Write a short poem about artificial intelligence:"
response = gen_model.generate_text(prompt)
print(f"Generated Text:\n{response}")

​
Chat Completion

Now, let’s use a different model for chat completion:

# Initialize chat model
chat_model = ModelInference(
    model_id="meta-llama/llama-3-8b-instruct", # Using the model ID from the MDX as it might be more current/available
    credentials=credentials, 
    project_id=project_id
)

# Format messages for chat
messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "What are the three laws of robotics?"},
]

# Get chat response
chat_response = chat_model.chat(messages)
# Accessing response based on typical ibm-watsonx-ai SDK structure
print(f"Chat Response:\n{chat_response['results'][0]['generated_text']}")

​
Another Chat Example

Let’s try a different type of query:

# New chat messages
messages = [
    {"role": "system", "content": "You are an expert in machine learning."},
    {"role": "user", "content": "Explain the difference between supervised and unsupervised learning in simple terms."},
]

# Get chat response
chat_response = chat_model.chat(messages)
print(f"Chat Response:\n{chat_response['results'][0]['generated_text']}")

​
Clean Up

Finally, let’s close the persistent connection with the models if they were established and end the AgentOps session.

# Close connections if persistent connections were used.
# This is good practice if the SDK version/usage implies persistent connections.
try:
    gen_model.close_persistent_connection()
    chat_model.close_persistent_connection()
except AttributeError:
    # Handle cases where this method might not exist (e.g. newer SDK versions or stateless calls)
    print("Note: close_persistent_connection not available or needed for one or more models.")
    pass

agentops.end_session("Success") # Manually end session