Log media

W&B Weave supports logging and displaying many content types, with dedicated functions for videos, images, audio clips, PDFs, CSV data, and HTML. This guide is for developers who want to capture media inputs and outputs in their Weave traces, so they can review and share them alongside the rest of the trace data. It provides basic and advanced examples for logging and displaying each supported media type:

Images
Video
Documents
Audio
HTML

Overview

Python
TypeScript

The examples in this guide use annotations. We recommend annotations because they’re the most direct way to start logging your media. For more advanced configurations, see the Content API section.To log media to Weave, add type annotations like Annotated[bytes, Content] or Annotated[str, Content] as input or return types in your ops. If you annotate path arguments with Annotated[str, Content], Weave automatically opens, detects, and displays the media within your trace.

The TypeScript SDK provides dedicated functions for logging media:

weave.weaveImage({ data: Buffer }) - for images (PNG format)
weave.weaveAudio({ data: Buffer }) - for audio (WAV format)

The TypeScript SDK supports logging images and audio. To log videos, documents, or HTML, use the Weave Python SDK instead.

The following sections provide examples of logging each type of media. Each section starts with a minimal example and then expands into more advanced scenarios.

Log images

The following examples demonstrate how to generate and log images to Weave’s UI.

Weave trace view showing a generated image of a cat wearing a pumpkin hat

Python
TypeScript

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:

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

weave.init('your-team-name/your-project-name')

# 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")

Weave logs the image and returns a link to the trace where you can view the image.

Advanced example: Generate an image with DALL-E and log it to Weave

The following example generates a picture of a cat and logs it to Weave:

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

client = openai.OpenAI()
weave.init("your-team-name/your-project-name")

@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")

Advanced example: Resize large images before logging

Resize images before logging them to reduce UI rendering cost and storage impact. 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('your-team-name/your-project-name')

# 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.Resampling.LANCZOS)
    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()

Weave logs the resized image and returns a link to the trace where you can view the image.

The following example logs an image using the weaveImage function:

import * as weave from 'weave';
import * as fs from 'fs';

async function main() {
    await weave.init('your-team-name/your-project-name');

    // Load and log an image using weaveImage
    const loadImage = weave.op(async function loadImage(path: string) {
        const data = fs.readFileSync(path);
        return weave.weaveImage({ data });
    });

    // Assuming you have a PNG image file
    await loadImage('sample_image.png');
}

main();

Weave logs the image and returns a link to the trace where you can view the image.

Advanced example: Log an image generated with the OpenAI DALL-E API

The following example logs an image generated with the OpenAI DALL-E API using the weaveImage function:

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

async function main() {
    await weave.init('your-team-name/your-project-name');
    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();

Log video

The following examples demonstrate how to generate and log videos to Weave’s UI.

Python
TypeScript

Log videos by annotating functions with Annotated[bytes, Content] types. Weave automatically handles mp4 videos. The following is a minimal example:

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

weave.init('your-team-name/your-project-name')

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")

Weave logs the video and returns a link to the trace where you can view the video.

Advanced example: Log a video within a video analysis project

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

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('your-team-name/your-project-name')

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)

Weave logs both the input video and the model’s analysis, so you can review them together in the trace.

This feature is not available in the Weave TypeScript SDK yet.

Log documents

The following examples generate and log documents to Weave’s UI.

Python
TypeScript

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, and xml file types. You can also log using file paths with Annotated[str, Content].The following example shows how to store copies of the input PDF and CSV files, and then stores the file contents returned by the function:

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('your-team-name/your-project-name')

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")

Advanced example: Log documents within a RAG system

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

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('your-team-name/your-project-name')

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?"
)

Weave logs both the source PDF and the generated answer, so you can audit the document that produced each response.

This feature is not available in the Weave TypeScript SDK yet.

Log audio

The following examples demonstrate how to log audio to Weave.

Weave trace view showing a logged audio file with an inline audio player

Python
TypeScript

Log audio to Weave by annotating functions with Annotated[bytes, Content] types, or by specifying the audio 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].The following code snippet generates a sine wave, records it, and then logs the audio to Weave:

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

weave.init('your-team-name/your-project-name')

# 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")

Advanced example: Generate and log AI-created audio

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

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

client = OpenAI()
weave.init("your-team-name/your-project-name")

@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")

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.

The following example shows how to log audio using streaming response from the OpenAI API:

import weave
from openai import OpenAI
import wave

weave.init("your-team-name/your-project-name")
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?")

The following example loads an existing audio file and logs it to Weave:

import * as weave from 'weave';
import * as fs from 'fs';

async function main() {
    await weave.init('your-team-name/your-project-name');

    // Load and log audio using weaveAudio
    const loadAudio = weave.op(async function loadAudio(path: string) {
        const data = fs.readFileSync(path);
        return weave.weaveAudio({ data });
    });

    // Assuming you have a WAV audio file
    await loadAudio('beep.wav');
}

main();

Advanced example: Generate audio using OpenAI’s TTS API and log it to Weave

The following example generates audio using OpenAI’s text-to-speech (TTS) API and logs it to Weave:

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

async function main() {
    await weave.init('your-team-name/your-project-name');
    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();

Try our cookbook for Audio Logging. The cookbook also includes an advanced example of a Real-Time Audio API-based assistant integrated with Weave.

Log HTML

The following examples demonstrate how to generate and log HTML to Weave’s UI.

Python
TypeScript

Log interactive HTML by annotating functions with Annotated[bytes, Content[Literal['html']]].The following example creates a minimal HTML page and logs it to Weave:

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

weave.init('your-team-name/your-project-name')

@weave.op
def create_simple_html() -> Annotated[bytes, 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()

Advanced example: Generate self-contained HTML pages using Serverless Inference and log them to Weave

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

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("your-team-name/your-project-name")
weave.publish(prompt_template, name="generate_prompt")

@weave.op
def generate_html(prompt: str, template: weave.StringPrompt) -> Annotated[bytes, 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 feature is not available in the Weave TypeScript SDK yet.

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

Contents API

The Content API handles media objects in Weave. Use it when you need more control than the previous annotation-based examples provide. For example, when you want to import content from base64 data, custom byte buffers, or text, or when you need to attach custom metadata. It lets you import content into Weave as base64 data, file paths, raw bytes, or text.

The Content API is only available in Python.

Usage

You can use the Content API in two primary ways: 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 the Content API’s runtime features in your code.

Type annotations

The Weave Content API is primarily designed for use 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 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

To take advantage of features such as the following:

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

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 might 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, see 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 (for example, `"jpg"`, `"mp3"`)
`mimetype`	`str`	MIME type (for example, `"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)

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"])

Get Started

Guides

Cookbooks

Reference

Details & Support

Overview

Log images

Advanced example: Generate an image with DALL-E and log it to Weave

Advanced example: Resize large images before logging

Advanced example: Log an image generated with the OpenAI DALL-E API

Log video

Advanced example: Log a video within a video analysis project

Log documents

Advanced example: Log documents within a RAG system

Log audio

Advanced example: Generate and log AI-created audio

Advanced example: Generate audio using OpenAI’s TTS API and log it to Weave

Log HTML

Advanced example: Generate self-contained HTML pages using Serverless Inference and log them to Weave

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

Custom metadata

​Overview

​Log images

​Advanced example: Generate an image with DALL-E and log it to Weave

​Advanced example: Resize large images before logging

​Advanced example: Log an image generated with the OpenAI DALL-E API

​Log video

​Advanced example: Log a video within a video analysis project

​Log documents

​Advanced example: Log documents within a RAG system

​Log audio

​Advanced example: Generate and log AI-created audio

​Advanced example: Generate audio using OpenAI’s TTS API and log it to Weave

​Log HTML

​Advanced example: Generate self-contained HTML pages using Serverless Inference and log them to Weave

​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

​Custom metadata

Overview

Log images

Advanced example: Generate an image with DALL-E and log it to Weave

Advanced example: Resize large images before logging

Advanced example: Log an image generated with the OpenAI DALL-E API

Log video

Advanced example: Log a video within a video analysis project

Log documents

Advanced example: Log documents within a RAG system

Log audio

Advanced example: Generate and log AI-created audio

Advanced example: Generate audio using OpenAI’s TTS API and log it to Weave

Log HTML

Advanced example: Generate self-contained HTML pages using Serverless Inference and log them to Weave

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

Custom metadata