weave

import os import weave from openai import OpenAI # Ensure you have your Weights & Biases API key set as an environment variable # os.environ['WANDB_API_KEY'] = 'YOUR_WANDB_API_KEY' # Ensure you have your OpenAI API key set as an environment variable # os.environ['OPENAI_API_KEY'] = 'YOUR_OPENAI_API_KEY' # Initialize Weave with your project name # This will create a new project or connect to an existing one in W&B weave.init(project_name=os.environ.get('WANDB_PROJECT_NAME', 'my-llm-project')) client = OpenAI() @weave.op() def extract_dinos(sentence: str) -> dict: """Extracts dinosaur names and diets from a sentence using OpenAI.""" response = client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": "In JSON format extract a list of `dinosaurs`, with their `name`, their `common_name`, and whether its `diet` is a herbivore or carnivore."}, {"role": "user", "content": sentence} ], response_model=dict # Placeholder if a Pydantic model is not used here ) return response.choices[0].message.content if response.choices else {} @weave.op() def main(): sentence = "The mighty Tyrannosaurus Rex (T-Rex), a carnivore, hunted the herbivorous Triceratops." dinosaur_info = extract_dinos(sentence) print("Extracted Dinosaur Info:", dinosaur_info) if __name__ == "__main__": main()