Introduction to Traces

Learn how to use introduction to traces with W&B Weave

This is an interactive notebook. You can run it locally or use the links below:

• Open in Google Colab
• View source on GitHub

🔑 Prerequisites

Before you can begin tracing in Weave, complete the following prerequisites.

1. Install the W&B Weave SDK and log in with your API key.
2. Install the OpenAI SDK and log in with your API key.
3. Initialize your W&B project.

# Install dependancies and imports
!pip install wandb weave openai -q

import json
import os
from getpass import getpass

from openai import OpenAI

import weave

# 🔑 Setup your API keys
# Running this cell will prompt you for your API key with `getpass` and will not echo to the terminal.
#####
print("---")
print(
    "You can find your Weights and Biases API key here: https://wandb.ai/settings#api"
)
os.environ["WANDB_API_KEY"] = getpass("Enter your Weights and Biases API key: ")
print("---")
print("You can generate your OpenAI API key here: https://platform.openai.com/api-keys")
os.environ["OPENAI_API_KEY"] = getpass("Enter your OpenAI API key: ")
print("---")
#####

# 🏠 Enter your W&B project name
weave_client = weave.init("MY_PROJECT_NAME")  # 🐝 Your W&B project name

🐝 Run your first trace

The following code sample shows how to capture and visualize a trace in Weave using the @weave.op decorator. It defines a function called extract_fruit that sends a prompt to OpenAI’s GPT-4o to extract structured data (fruit, color, and flavor) from a sentence. By decorating the function with @weave.op, Weave automatically tracks the function execution, including inputs, outputs, and intermediate steps. When the function is called with a sample sentence, the full trace is saved and viewable in the Weave UI.

@weave.op()  # 🐝 Decorator to track requests
def extract_fruit(sentence: str) -> dict:
    client = OpenAI()
    system_prompt = (
        "Parse sentences into a JSON dict with keys: fruit, color and flavor."
    )
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": sentence},
        ],
        temperature=0.7,
        response_format={"type": "json_object"},
    )
    extracted = response.choices[0].message.content
    return json.loads(extracted)

sentence = "There are many fruits that were found on the recently discovered planet Goocrux. There are neoskizzles that grow there, which are purple and taste like candy."
extract_fruit(sentence)

🚀 Looking for more examples?

• Check out the Quickstart guide.
• Learn more about advanced tracing topics.
• Learn more about tracing in Weave