Project Links

Meta

Author: Shawn Lewis, Tim Sweeney, Nick Peneranda, Jeff Raubitschek, Jamie Rasmussen, Griffin Tarpenning, Josiah Lee, Andrew Truong, Niall McQuay

Maintainer: W&B

Requires Python: >=3.10

Classifiers

Development Status

4 - Beta

Environment

Web Environment

Framework

Flask
Jupyter

Intended Audience

Developers
Science/Research

License

OSI Approved :: Apache Software License

Operating System

OS Independent

Programming Language

Python

Topic

Database :: Front-Ends
Office/Business :: Financial :: Spreadsheet
Scientific/Engineering :: Information Analysis
Scientific/Engineering :: Visualization
Software Development :: Libraries :: Application Frameworks
Software Development :: Widget Sets

Weave by Weights & Biases

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases.

You can use Weave to:

Log and debug language model inputs, outputs, and traces
Build rigorous, apples-to-apples evaluations for language model use cases
Organize all the information generated across the LLM workflow, from experimentation to evaluations to production

Our goal is to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

Documentation

Our documentation site can be found here.

Prerequisites

Python 3.10 or higher
A Weights & Biases account (free tier available)

Quick Start

Install Weave:
```
pip install weave
```

Import and initialize:

import weave
weave.init("my-project-name")

Trace your functions:

@weave.op
def my_function():
    # Your tracked code!
    pass

Usage

Tracing

You can trace any function using weave.op - from api calls to OpenAI, Anthropic, Google AI Studio etc to generation calls from Hugging Face and other open source models to any other validation functions or data transformations in your code you'd like to keep track of.

Decorate all the functions you want to trace, this will generate a trace tree of the inputs and outputs of all your functions:

import weave
weave.init("weave-example")

@weave.op
def sum_nine(value_one: int):
    return value_one + 9

@weave.op
def multiply_two(value_two: int):
    return value_two * 2

@weave.op
def main():
    output = sum_nine(3)
    final_output = multiply_two(output)
    return final_output

main()

Fuller Example

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)

Contributing

Interested in pulling back the hood or contributing? Awesome, before you dive in, here's what you need to know.

We're in the process of 🧹 cleaning up 🧹. This codebase contains a large amount code for the "Weave engine" and "Weave boards", which we've put on pause as we focus on Tracing and Evaluations.

The Weave Tracing code is mostly in: weave/trace and weave/trace_server.

The Weave Evaluations code is mostly in weave/flow.