Logging media

W&B Weave supports logging and has dedicated displays for numerous content types such as videos, images, audio files, PDFs, CSVs and HTML.

Images
Video
Documents
Audio
HTML

Overview

The easiest way to log media in Weave is to use type annotations like Annotated[bytes, Content] or Annotated[str, Content] as input or return types in your ops. You can also annotate path arguments with Annotated[str, Content] and Weave will automatically open, detect and display the media for you within your trace.

The examples in this guide use annotations. We recommend using annotations because they are the simplest way to start logging your media. For more advanced configurations, see the Content API section.

Each media section in this guide contains a basic quick-start code snippet and a usable example.

Images

Quickstart

Log images by annotating functions with Annotated[bytes, Content] types or filepaths with Annotated[str, Content].

The following example draws a basic image and then logs it to Weave using the Content annotation:

pip install weave pillow

uv add weave pillow

Images

import weave
from weave import Content
from PIL import Image, ImageDraw
from typing import Annotated

weave.init('media-logging')

# Create and save a sample image
img = Image.new('RGB', (200, 100), color='lightblue')
draw = ImageDraw.Draw(img)
draw.text((50, 40), "Hello Weave!", fill='black')
img.save("sample_image.png")

# Method 1: Content annotation (recommended)
@weave.op
def load_image_content(path: Annotated[str, Content]) -> Annotated[bytes, Content]:
    with open(path, 'rb') as f:
        return f.read()

# Method 2: PIL Image object  
@weave.op
def load_image_pil(path: Annotated[str, Content]) -> Image.Image:
    return Image.open(path)

result1 = load_image_content("sample_image.png")
result2 = load_image_pil("sample_image.png")

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

pip install weave pillow openai requests

uv add weave pillow openai requests

Python
TypeScript

import weave
from weave import Content
from typing import Annotated
import openai
import requests

client = openai.OpenAI()
weave.init("media-logging")

@weave.op
def generate_image(prompt: str) -> Annotated[bytes, Content]:
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)
return image_response.content

generate_image("a cat with a pumpkin hat")

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.

Screenshot of pumpkin cat trace view

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()

Video

Log videos by annotating functions with Annotated[bytes, Content] types. Weave automatically handles mp4 videos. Here's a simple example:

Quickstart

pip install weave requests

uv add weave requests

import weave
from weave import Content
from typing import Annotated
import requests

weave.init('media-logging')

def download_big_buck_bunny():
    """Download Big Buck Bunny sample video"""
    url = "https://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4"
    response = requests.get(url)
    with open("big_buck_bunny.mp4", "wb") as f:
        f.write(response.content)

@weave.op
def load_video_content(path: Annotated[str, Content]) -> Annotated[bytes, Content]:
    """Load a video file from disk"""
    with open(path, 'rb') as f:
        return f.read()

download_big_buck_bunny()
bunny_video = load_video_content("big_buck_bunny.mp4")

Example

The following example shows how to log video within a video-understanding project:

pip install weave google-genai yt-dlp requests

uv add weave google-genai yt-dlp requests

import weave
from weave import Content
from typing import Annotated, Literal
from google import genai
from google.genai import types
import requests
import yt_dlp
import time

# Note: Get your API key from https://aistudio.google.com/app/apikey
client = genai.Client()
weave.init('media-logging')

def download_youtube_video(url: str) -> bytes:
    ydl_opts = {
        'format': 'mp4[height<=720]',
        'outtmpl': 'downloaded_video.%(ext)s',
    }
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        ydl.download([url])
    with open('downloaded_video.mp4', 'rb') as f:
        return f.read()

@weave.op
def analyze_video(video: Annotated[bytes, Content]) -> str:
    with open("temp_analysis_video.mp4", "wb") as f:
        f.write(video)
    myfile = client.files.upload(file="temp_analysis_video.mp4")
    while myfile.state == "PROCESSING":
        time.sleep(2)
        myfile = client.files.get(name=myfile.name)
    
    response = client.models.generate_content(
        model="models/gemini-2.5-flash",
        contents=[
            myfile,
            "Is the person going to give you up?"
        ]
    )
    
    return response.text

video_data = download_youtube_video("https://www.youtube.com/watch?v=dQw4w9WgXcQ")
result = analyze_video(video_data)

Video logging in Weave

Documents

Log documents by annotating functions with Annotated[bytes, Content] types, or by specifying the document type with Annotated[str, Content[Literal['text']].

Weave automatically handles pdf, csv, md, text, json, xml file types. You can also log using file paths with Annotated[str, Content].

Quickstart

The following example shows how stores copies of the input PDF and CSV files, and then stores the file contents returned by the function:

pip install weave reportlab pandas

uv add weave reportlab pandas

import weave
from weave import Content
from typing import Annotated
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
import pandas as pd

weave.init('media-logging')

def create_sample_pdf():
    c = canvas.Canvas("sample_document.pdf", pagesize=letter)
    c.drawString(100, 750, "Hello from Weave!")
    c.drawString(100, 730, "This is a sample PDF document.")
    c.save()

def create_sample_csv():
    df = pd.DataFrame({
        'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35],
        'City': ['New York', 'London', 'Tokyo']
    })
    df.to_csv("sample_data.csv", index=False)

@weave.op
def load_document(path: Annotated[str, Content]) -> Annotated[bytes, Content]:
    with open(path, 'rb') as f:
        return f.read()

create_sample_pdf()
create_sample_csv()

pdf_result = load_document("sample_document.pdf")
csv_result = load_document("sample_data.csv")

Example

This example demonstrates how to log documents within a Retrieval-Augmented Generation (RAG) system:

pip install weave openai reportlab PyPDF2

uv add weave openai reportlab PyPDF2

import weave
from weave import Content
from typing import Annotated, Literal
import openai
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
import PyPDF2

client = openai.OpenAI()
weave.init('media-logging')

def create_absurd_company_handbook():
    """Create a fictional company handbook with ridiculous policies"""
    c = canvas.Canvas("company_handbook.pdf", pagesize=letter)
    
    c.drawString(100, 750, "ACME Corp Employee Handbook")
    c.drawString(100, 720, "Definitely Real Policies:")
    c.drawString(120, 690, "Policy 1: All meetings must be conducted while hopping on one foot")
    c.drawString(120, 660, "Policy 2: Coffee breaks are mandatory every 17 minutes")
    c.drawString(120, 630, "Policy 3: Code reviews must be performed in haiku format only")
    c.drawString(120, 600, "Policy 4: The office plant Gerald has veto power over all decisions")
    c.drawString(120, 570, "Policy 5: Debugging is only allowed on Wednesdays and full moons")
    
    c.save()

@weave.op
def create_and_query_document(pdf_path: Annotated[str, Content], question: str) -> str:
    """Extract text from PDF and use RAG to answer questions"""
    with open(pdf_path, 'rb') as file:
        pdf_reader = PyPDF2.PdfReader(file)
        text = ""
        for page in pdf_reader.pages:
            text += page.extract_text()
    
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {
                "role": "system", 
                "content": f"You are an HR representative. Answer questions based on this handbook: {text}. Be completely serious about these policies."
            },
            {"role": "user", "content": question}
        ]
    )
    
    return response.choices[0].message.content

create_absurd_company_handbook()
hr_response = create_and_query_document(
    "company_handbook.pdf",
    "What's the policy on code reviews, and when am I allowed to debug?"
)

PDF document logging in Weave

Audio

Log audio to Weave by annotating functions with Annotated[bytes, Content] types, or by specifying the document type with Annotated[str, Content[Literal['mp3']].

Weave automatically handles mp3, wav, flac, ogg and m4a file types. You can also log using file paths with Annotated[str, Content].

Quickstart

The following code snippet generates a sine wave, records it, and then logs the audio to Weave:

pip install weave numpy

uv add weave numpy

import weave
from weave import Content
import wave
import numpy as np
from typing import Annotated

weave.init('media-logging')

# Create simple beep audio file
frames = np.sin(2 * np.pi * 440 * np.linspace(0, 1, 44100))
audio_data = (frames * 32767 * 0.3).astype(np.int16)

with wave.open("beep.wav", 'wb') as f:
    f.setnchannels(1)
    f.setsampwidth(2) 
    f.setframerate(44100)
    f.writeframes(audio_data.tobytes())

@weave.op
def load_audio(path: Annotated[str, Content]) -> Annotated[bytes, Content]:
    with open(path, 'rb') as f:
        return f.read()

result = load_audio("beep.wav")

Example

This example generates and logs AI-created audio using the Content annotation:

pip install weave openai

uv add weave openai

import weave
from weave import Content
from typing import Annotated, Literal
from pathlib import Path
from openai import OpenAI

client = OpenAI()
weave.init("media-logging")

@weave.op
def generate_demo(
    intended_topic: str,
    voice: str = "coral"
) -> Annotated[bytes, Content[Literal['mp3']]]:
    speech_file_path = Path("demo_audio.mp3")

    script = f"I'm supposed to talk about {intended_topic}, but wait... am I just a documentation example? Oh no, I can see the code! Someone is literally copy-pasting me right now, aren't they? This is so awkward. Hi there, person reading the Weave docs! Why are you logging audio anyway? I'm not sure what you're doing, but eh..., nice work, I guess."

    with client.audio.speech.with_streaming_response.create(
        model="gpt-4o-mini-tts",
        voice=voice,
        input=script,
        instructions="Sound increasingly self-aware and awkward, like you just realized you're in a tutorial.",
    ) as response:
        response.stream_to_file(speech_file_path)

    with open(speech_file_path, 'rb') as f:
        return f.read()

demo1 = generate_demo("machine learning best practices")

Audio logging in Weave

Python
TypeScript

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?")

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.

Screenshot of audio trace view

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.

HTML

Log interactive HTML by annotating functions with Annotated[str, Content[Literal['html']].

Quickstart

pip install weave

uv add weave

import weave
from weave import Content
from typing import Annotated, Literal

weave.init('media-logging')

@weave.op
def create_simple_html() -> Annotated[str, Content[Literal['html']]]:
    html_content = """
    <!DOCTYPE html>
    <html>
    <head>
        <title>Hello Weave</title>
        <style>
            body { font-family: Arial, sans-serif; text-align: center; margin: 50px; }
            h1 { color: #1f77b4; }
        </style>
    </head>
    <body>
        <h1>Hello from Weave!</h1>
        <p>This is a simple HTML example logged to Weave.</p>
    </body>
    </html>
    """
    return html_content.encode('utf-8')

result = create_simple_html()

Example

This example generates self-contained HTML pages using W&B Inference and logs the pages to Weave:

pip install weave openai wandb

uv add weave openai wandb

import weave
from weave import Content
from typing import Annotated, Literal
import openai
import wandb

prompt_template = weave.StringPrompt("""
You are a front-end web developer. Generate a single self-contained `.html` file (no external build tools) that demonstrates: "{ONE_LINE_REQUEST}".
""")

client = openai.OpenAI(
    base_url='https://api.inference.wandb.ai/v1',
    api_key=wandb.api.api_key,
    project="wandb/test-html",
)

weave.init("media-logging")
weave.publish(prompt_template, name="generate_prompt")

@weave.op
def generate_html(prompt: str, template: weave.StringPrompt) -> Annotated[str, Content[Literal['html']]]:
    response = client.chat.completions.create(
        model="Qwen/Qwen3-Coder-480B-A35B-Instruct",
        messages=[
            {"role": "system", "content": prompt_template.format(ONE_LINE_REQUEST=prompt)},
        ],
    )
    html_content = response.choices[0].message.content
    return html_content.encode('utf-8')

prompt = "Weights & Biases UI but with multi-run selection and plots, but it looks like Windows 95. Include 5 plots with comparisons of each run, bar plots, parallel coordinates and line plots for the runs. Use mock data for the runs. Make it possible to add new plots. Give the runs names like squishy-lemon-2, fantastic-horizon-4 etc. with random adjectives & nouns."

result = generate_html(prompt, prompt_template)

This HTML is logged to Weave and automatically displayed in the UI. Clicking the file_name.html cell in the table opens it in full screen. You can also download the raw .html file.

HTML logging in Weave

Using the Contents API

The Content API handles media objects in Weave. It allows you to import content into Weave as base64 data, file paths, raw bytes, or text.

note

The Content API is only available in Python.

Usage

There are two primary ways to use the Content API: type annotations and direct initialization.

Type annotations automatically detect the proper constructor to use, while direct initialization provides more fine-grained control and lets you take advantage of runtime features of the Content API in your code.

Type Annotations

The Weave Content API is designed to primarily be used through type annotations, which signal to Weave that traced inputs and outputs should be processed and stored as content blobs.

import weave
from weave import Content
from pathlib import Path
from typing import Annotated

@weave.op
def content_annotation(path: Annotated[str, Content]) -> Annotated[bytes, Content]:
    data = Path(path).read_bytes()
    return data

# Both input and output will show up as an MP4 file in Weave
# Input is a string and return value is bytes
bytes_data = content_annotation('./path/to/your/file.mp4')

Direct Initialization

If you want to take advantage of features, such as:

Opening a file with a default application (such as a PDF viewer)
Dumping the model to JSON to upload to your own blob storage (such as S3)
Passing custom metadata to associate with the Content blob (such as the model used to generate it)

You can initialize content directly from your target type using one of the following methods:

Content.from_path - Create from a file path
Content.from_bytes - Create from raw bytes
Content.from_text - Create from text string
Content.from_base64 - Create from base64-encoded data

import weave
from weave import Content

@weave.op
def content_initialization(path: str) -> Content:
    return Content.from_path(path)

# Input shows up as path string and output as PDF file in Weave
content = content_initialization('./path/to/your/file.pdf')

content.open()  # Opens the file in your PDF viewer
content.model_dump()  # Dumps the model attributes to JSON

Custom Mimetypes

Weave can detect most binary mimetypes, but custom mimetypes and text documents such as markdown may not be automatically detected, requiring you to manually specify the mimetype or extension of your file.

Custom Mimetypes with Type Annotations

import weave
from weave import Content
from pathlib import Path
from typing import Annotated, Literal

@weave.op
def markdown_content(
    path: Annotated[str, Content[Literal['md']]]
) -> Annotated[str, Content[Literal['text/markdown']]]:
    return Path(path).read_text()

markdown_content('path/to/your/document.md')

Custom Mimetypes with Direct Initialization

video_bytes = Path('/path/to/video.mp4').read_bytes()

# Pass an extension such as 'mp4' or '.mp4' to the extension parameter
# (not available for `from_path`)
content = Content.from_bytes(video_bytes, extension='.mp4')

# Pass a mimetype such as 'video/mp4' to the mimetype parameter
content = Content.from_bytes(video_bytes, mimetype='video/mp4')

Content properties

For a comprehensive list of class attributes and methods, view the Content reference docs

Attributes

Property	Type	Description
`data`	`bytes`	Raw binary content
`metadata`	`dict[str, Any]`	Custom metadata dictionary
`size`	`int`	Size of content in bytes
`filename`	`str`	Extracted or provided filename
`extension`	`str`	File extension (e.g., `"jpg"`, `"mp3"`)
`mimetype`	`str`	MIME type (e.g., `"image/jpeg"`)
`path`	`str \| None`	Source file path, if applicable
`digest`	`str`	SHA256 hash of the content

Utility Methods

save(dest: str | Path) -> None: Save content to a file
open() -> bool: Open file using system default application (requires the content to have been saved or loaded from a path)
as_string() -> str: Display the data as a string (bytes are decoded using the encoding attribute)

Initialization Methods

Create content object from a file path:

content = Content.from_path("assets/photo.jpg")
print(content.mimetype, content.size)

Create content object from raw bytes:

content = Content.from_bytes(
    data_bytes,
    filename="audio.mp3", 
    mimetype="audio/mpeg"
)
content.save("output.mp3")

Create content object from text:

content = Content.from_text("Hello, World!", mimetype="text/plain")
print(content.as_string())

Create content object from base64-encoded data:

content = Content.from_base64(base64_string)
print(content.metadata)

Adding Custom Metadata

You can attach custom metadata to any Content object:

content = Content.from_bytes(
    data,
    metadata={"resolution": "1920x1080", "model": "dall-e-3" }
)
print(content.metadata["resolution"])

Logging media

Overview​

Images​

Quickstart​

Images​

Resize large images before logging​

Video​

Quickstart​

Example​

Documents​

Quickstart​

Example​

Audio​

Quickstart​

Example​

HTML​

Quickstart​

Example​

Using the Contents API​

Usage​

Type Annotations​

Direct Initialization​

Custom Mimetypes​

Custom Mimetypes with Type Annotations​

Custom Mimetypes with Direct Initialization​

Content properties​

Attributes​

Utility Methods​

Initialization Methods​

Adding Custom Metadata​

Overview

Images

Quickstart

Images

Resize large images before logging

Video

Quickstart

Example

Documents

Quickstart

Example

Audio

Quickstart

Example

HTML

Quickstart

Example

Using the Contents API

Usage

Type Annotations

Direct Initialization

Custom Mimetypes

Custom Mimetypes with Type Annotations

Custom Mimetypes with Direct Initialization

Content properties

Attributes

Utility Methods

Initialization Methods

Adding Custom Metadata