Quickstart

Installation

Weave requires Python 3.9+.

pip install weave

Track inputs & outputs of functions

• Import weave
• Call weave.init('project-name') to start logging
• Add the @weave.op() decorator to the functions you want to track

In this example, we're using openai so you will need to add an openai API key.

import weave
import json
from openai import OpenAI

@weave.op()
def extract_fruit(sentence: str) -> dict:
    client = OpenAI()

    response = client.chat.completions.create(
    model="gpt-3.5-turbo-1106",
    messages=[
        {
            "role": "system",
            "content": "You will be provided with unstructured data, and your task is to parse it one JSON dictionary with fruit, color and flavor as keys."
        },
        {
            "role": "user",
            "content": sentence
        }
        ],
        temperature=0.7,
        response_format={ "type": "json_object" }
    )
    extracted = response.choices[0].message.content
    return json.loads(extracted)

weave.init('intro-example')
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)

Now, every time you call this function, weave will automatically capture the input & output data and log any changes to the code. Run this application and your console will output a link to view it within W&B.

note

Calls made with the openai library are automatically tracked with weave but you can add other LLMs easily by wrapping them with @weave.op()

What's next?

• Follow the Build an Evaluation pipeline tutorial to start iteratively improving your applications.