89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
# # IBM Watsonx AI Text Generation and Chat with AgentOps
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#
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# This notebook demonstrates how to use IBM Watsonx AI for basic text generation and chat completion tasks with AgentOps instrumentation.
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# ## Setup
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#
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# First, let's import the necessary libraries and initialize AgentOps:
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import agentops
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from ibm_watsonx_ai import Credentials
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from ibm_watsonx_ai.foundation_models import ModelInference
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from dotenv import load_dotenv
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import os
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# Load environment variables
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load_dotenv()
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os.environ["AGENTOPS_API_KEY"] = os.getenv("AGENTOPS_API_KEY", "your_api_key_here")
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# Initialize AgentOps
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agentops.init(trace_name="WatsonX Text Chat Example", tags=["watsonx-text-chat", "agentops-example"])
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# ## Initialize IBM Watsonx AI Credentials
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#
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# To use IBM Watsonx AI, you need to set up your credentials and project ID.
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# Initialize credentials - replace with your own API key
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# Best practice: Store API keys in environment variables
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# Ensure WATSONX_API_KEY is set in your .env file or environment
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os.environ["WATSONX_API_KEY"] = os.getenv("WATSONX_API_KEY", "your_watsonx_api_key_here")
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credentials = Credentials(
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url=os.getenv("WATSONX_URL", "https://eu-de.ml.cloud.ibm.com"),
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api_key=os.environ["WATSONX_API_KEY"],
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)
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# Project ID for your IBM Watsonx project
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project_id = os.getenv("WATSONX_PROJECT_ID", "your-project-id-here")
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# ## Text Generation
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#
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# Let's use IBM Watsonx AI to generate text based on a prompt:
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# Initialize text generation model
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gen_model = ModelInference(model_id="google/flan-ul2", credentials=credentials, project_id=project_id)
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# Generate text with a prompt
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prompt = "Write a short poem about artificial intelligence:"
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response = gen_model.generate_text(prompt)
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print(f"Generated Text:\\n{response}")
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# ## Chat Completion
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#
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# Now, let's use a different model for chat completion:
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# Initialize chat model
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chat_model = ModelInference(
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model_id="meta-llama/llama-3-3-70b-instruct", credentials=credentials, project_id=project_id
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)
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# Format messages for chat
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messages = [
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{"role": "system", "content": "You are a helpful AI assistant."},
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{"role": "user", "content": "What are the three laws of robotics?"},
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]
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# Get chat response
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chat_response = chat_model.chat(messages)
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print(f"Chat Response:\\n{chat_response['choices'][0]['message']['content']}")
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# ## Another Chat Example
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#
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# Let's try a different type of query:
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# New chat messages
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messages = [
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{"role": "system", "content": "You are an expert in machine learning."},
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{"role": "user", "content": "Explain the difference between supervised and unsupervised learning in simple terms."},
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]
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# Get chat response
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chat_response = chat_model.chat(messages)
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print(f"Chat Response:\\n{chat_response['choices'][0]['message']['content']}")
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# ## Clean Up
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#
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# Finally, let's close the persistent connection with the models:
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# Close connections
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gen_model.close_persistent_connection()
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chat_model.close_persistent_connection()
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# Let's check programmatically that spans were recorded in AgentOps
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print("\n" + "=" * 50)
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print("Now let's verify that our LLM calls were tracked properly...")
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try:
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agentops.validate_trace_spans(trace_context=None)
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print("\n✅ Success! All LLM spans were properly recorded in AgentOps.")
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except agentops.ValidationError as e:
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print(f"\n❌ Error validating spans: {e}")
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raise
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