Logging media

Weave supports logging and displaying video, images, and audio.

Video

Weave automatically logs videos using moviepy. This allows you to pass video inputs and outputs to traced functions, and Weave will automatically handle uploading and storing video data.

note

Video support is currently only available in Python.

For usage information, see Video Support.

Images

Logging type: PIL.Image.Image.

important

Base64-encoded image strings (e.g., data:image/jpeg;base64,...) are technically supported but discouraged. They can cause performance issues and should only be used if absolutely necessary (e.g., for integration with specific APIs).

The following example shows how to log an image generated via the OpenAI DALL-E API:

Python

import weave
from openai import OpenAI
import requests
from PIL import Image

weave.init('image-example')
client = OpenAI()

@weave.op
def generate_image(prompt: str) -> Image:
    response = client.images.generate(
        model="dall-e-3",
        prompt=prompt,
        size="1024x1024",
        quality="standard",
        n=1,
    )
    image_url = response.data[0].url
    image_response = requests.get(image_url, stream=True)
    image = Image.open(image_response.raw)

    # return a PIL.Image.Image object to be logged as an image
    return image

generate_image("a cat with a pumpkin hat")

TypeScript

import {OpenAI} from 'openai';
import * as weave from 'weave';

async function main() {
    const client = await weave.init('image-example');
    const openai = new OpenAI();

    const generateImage = weave.op(async (prompt: string) => {
        const response = await openai.images.generate({
            model: 'dall-e-3',
            prompt: prompt,
            size: '1024x1024',
            quality: 'standard',
            n: 1,
        });
        const imageUrl = response.data[0].url;
        const imgResponse = await fetch(imageUrl);
        const data = Buffer.from(await imgResponse.arrayBuffer());

        return weave.weaveImage({data});
    });

    generateImage('a cat with a pumpkin hat');
}

main();

This image is logged to Weave and automatically displayed in the UI.

Resize large images before logging

It can be helpful to resize images before logging to reduce UI rendering cost and storage impact. You can use postprocess_output in your @weave.op to resize an image.

from dataclasses import dataclass
from typing import Any
from PIL import Image
import weave

weave.init('image-resize-example')

# Custom output type
@dataclass
class ImageResult:
    label: str
    image: Image.Image

# Resize helper
def resize_image(image: Image.Image, max_size=(512, 512)) -> Image.Image:
    image = image.copy()
    image.thumbnail(max_size, Image.ANTIALIAS)
    return image

# Postprocess output to resize image before logging
def postprocess_output(output: ImageResult) -> ImageResult:
    resized = resize_image(output.image)
    return ImageResult(label=output.label, image=resized)

@weave.op(postprocess_output=postprocess_output)
def generate_large_image() -> ImageResult:
    # Create an example image to process (e.g., 2000x2000 red square)
    img = Image.new("RGB", (2000, 2000), color="red")
    return ImageResult(label="big red square", image=img)

generate_large_image()

Audio

Logging type: wave.Wave_read.

The following example shows how to log an audio file using OpenAI's speech generation API.

Python

import weave
from openai import OpenAI
import wave

weave.init("audio-example")
client = OpenAI()


@weave.op
def make_audio_file_streaming(text: str) -> wave.Wave_read:
    with client.audio.speech.with_streaming_response.create(
        model="tts-1",
        voice="alloy",
        input=text,
        response_format="wav",
    ) as res:
        res.stream_to_file("output.wav")

    # return a wave.Wave_read object to be logged as audio
    return wave.open("output.wav")

make_audio_file_streaming("Hello, how are you?")

TypeScript

import {OpenAI} from 'openai';
import * as weave from 'weave';

async function main() {
    await weave.init('audio-example');
    const openai = new OpenAI();

    const makeAudioFileStreaming = weave.op(async function audio(text: string) {
        const response = await openai.audio.speech.create({
            model: 'tts-1',
            voice: 'alloy',
            input: text,
            response_format: 'wav',
        });

        const chunks: Uint8Array[] = [];
        for await (const chunk of response.body) {
            chunks.push(chunk);
        }
        return weave.weaveAudio({data: Buffer.concat(chunks)});
    });

    await makeAudioFileStreaming('Hello, how are you?');
}

main();

This audio is logged to Weave and automatically displayed in the UI, along with an audio player. In the audio player, you can view and download the raw audio waveform.

tip

Try our cookbook for Audio Logging or

Open in Colab

. The cookbook also includes an advanced example of a Real Time Audio API based assistant integrated with Weave.