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Documentation Index

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Multimodal models can accept both text and non-text content — images, audio, video, and documents — as part of a single prompt. Genkit uses a unified Part type to represent all content, making it straightforward to mix text and media in the same message.

​
The MediaPart type

Non-text content is represented as a MediaPart: 
// Structure of a MediaPart
{
  media: {
    url: string;         // A URL or a base64 data URI
    contentType?: string; // MIME type, e.g. "image/png", "audio/mp3"
  }
}
A prompt is an array of Part objects. Each part is either a TextPart ({ text: string }) or a MediaPart.

​
Images

​
From a URL

Pass a publicly accessible image URL directly: 
import { genkit } from 'genkit';
import { googleAI } from '@genkit-ai/google-genai';

const ai = genkit({ plugins: [googleAI()], model: 'googleai/gemini-2.0-flash' });

const response = await ai.generate({
  prompt: [
    { media: { url: 'https://example.com/photo.jpg', contentType: 'image/jpeg' } },
    { text: 'Describe what you see in this image.' },
  ],
});

console.log(response.text);

​
From a base64-encoded buffer

For local files or dynamically loaded images, encode the content as a base64 data URI: 
import { readFileSync } from 'fs';

const imageData = readFileSync('./photo.jpg');
const base64Image = imageData.toString('base64');

const response = await ai.generate({
  prompt: [
    {
      media: {
        url: `data:image/jpeg;base64,${base64Image}`,
        contentType: 'image/jpeg',
      },
    },
    { text: 'What objects are in this image?' },
  ],
});

​
Shorthand for multipart arrays

You can also pass the parts array directly to ai.generate(): 
const response = await ai.generate([
  { media: { url: 'https://example.com/chart.png' } },
  { text: 'Summarize the trend shown in this chart.' },
]);

​
Python example

from genkit import Genkit
from genkit.plugins.google_genai import GoogleAI
import base64

ai = Genkit(plugins=[GoogleAI()])

# From a URL
response = await ai.generate(
    prompt=[
        {'media': {'url': 'https://example.com/photo.jpg', 'content_type': 'image/jpeg'}},
        {'text': 'Describe what you see in this image.'},
    ]
)
print(response.text)

# From a local file
with open('./photo.jpg', 'rb') as f:
    image_b64 = base64.b64encode(f.read()).decode()

response = await ai.generate(
    prompt=[
        {'media': {'url': f'data:image/jpeg;base64,{image_b64}', 'content_type': 'image/jpeg'}},
        {'text': 'What objects are visible?'},
    ]
)

​
Go example

package main

import (
    "context"
    "fmt"
    "log"
    "os"
    "encoding/base64"

    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    "github.com/firebase/genkit/go/plugins/googlegenai"
)

func main() {
    ctx := context.Background()
    g := genkit.Init(ctx,
        genkit.WithPlugins(&googlegenai.GoogleAI{}),
        genkit.WithDefaultModel("googleai/gemini-2.0-flash"),
    )

    // From a URL
    resp, err := genkit.Generate(ctx, g,
        ai.WithPromptParts(
            ai.NewMediaPart("image/jpeg", "https://example.com/photo.jpg"),
            ai.NewTextPart("Describe what you see in this image."),
        ),
    )
    if err != nil {
        log.Fatal(err)
    }
    fmt.Println(resp.Text())

    // From a local file
    data, _ := os.ReadFile("./photo.jpg")
    encoded := base64.StdEncoding.EncodeToString(data)

    resp, err = genkit.Generate(ctx, g,
        ai.WithPromptParts(
            ai.NewMediaPart("image/jpeg", "data:image/jpeg;base64,"+encoded),
            ai.NewTextPart("What objects are visible?"),
        ),
    )
    if err != nil {
        log.Fatal(err)
    }
    fmt.Println(resp.Text())
}

​
Audio input

Models that support audio input (such as Gemini 1.5 Pro and later) accept audio files using the same MediaPart structure: 
const audioData = readFileSync('./meeting.mp3');
const base64Audio = audioData.toString('base64');

const response = await ai.generate({
  model: 'googleai/gemini-2.0-flash',
  prompt: [
    {
      media: {
        url: `data:audio/mp3;base64,${base64Audio}`,
        contentType: 'audio/mp3',
      },
    },
    { text: 'Transcribe this audio and summarize the key points.' },
  ],
});

​
Video input

Video is supported via URL or base64, with the same approach. Large video files are best referenced by URL: 
const response = await ai.generate({
  model: 'googleai/gemini-2.0-flash',
  prompt: [
    {
      media: {
        url: 'https://storage.googleapis.com/my-bucket/demo.mp4',
        contentType: 'video/mp4',
      },
    },
    { text: 'Give me a chapter breakdown of this video.' },
  ],
});

​
PDF and document input

Gemini models can also process PDF documents: 
const pdfData = readFileSync('./report.pdf');
const base64Pdf = pdfData.toString('base64');

const response = await ai.generate({
  model: 'googleai/gemini-2.0-flash',
  prompt: [
    {
      media: {
        url: `data:application/pdf;base64,${base64Pdf}`,
        contentType: 'application/pdf',
      },
    },
    { text: 'Summarize the executive summary section of this report.' },
  ],
});

​
Model capabilities

Not all models support multimodal input. Each model exposes its capabilities in the ModelInfo.supports metadata: 
// Check if a model supports media input
const modelRef = googleAI.model('gemini-2.0-flash');
console.log(modelRef.info?.supports?.media); // true
The supports object includes:
FieldTypeDescription
mediabooleanCan process images, audio, video, documents.
multiturnbooleanCan process conversation history.
toolsbooleanCan call tools.
systemRolebooleanAccepts a system message.
outputstring[]Supported output types (e.g., ["text", "media"]).
contentTypestring[]Accepted MIME types for media input.
Multimodal support depends on the model. Gemini 1.5 and 2.x models support images, audio, video, and PDF input. Older or text-only models will return an error if media parts are included in the prompt. Check the plugin documentation for the specific model you are using.

​
Combining multimodal input with structured output

You can combine multimodal input with structured output to extract typed data from images: 
import { z } from 'genkit';

const ProductSchema = z.object({
  name: z.string(),
  price: z.number().optional(),
  brand: z.string().optional(),
  description: z.string(),
});

const response = await ai.generate({
  prompt: [
    { media: { url: 'https://example.com/product.jpg' } },
    { text: 'Extract the product details from this image.' },
  ],
  output: { schema: ProductSchema },
});

console.log(response.output); // { name: '...', price: 29.99, ... }

Structured output

Extract typed data from images or documents.

Google GenAI plugin

Gemini model capabilities and configuration.

Models

How model capability metadata works.

Streaming

Stream multimodal responses.

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