2.7 Multi-Modal Input Recipe - Images & Text

In this recipe, we will explore how to use Genkit’s Multi-Modal Inputs feature to create a flow that processes both image and text inputs. This technique is particularly useful for applications that require understanding and generating content based on multiple data types.

In the first recipe we created a webpage summarizer that took in the url of a webpage and returned a summary of the content. You can find the recipe here: 2.5 Webpage Summarizer Recipe. The way we went about this was by downloading the webpage content and then passing it to the model as text input.

What if, instead of downloading the webpage content, we wanted to pass the url of the webpage to the model and have the model do the heavy lifting. Here is an example of how that would look like:

const webpageAnalyzerFlow = ai.defineFlow(
  {
    name: 'webpageAnalyzer',
    inputSchema: z.object({
      url: z.string().describe('A URL of the webpage to analyze'),
      query: z.string().describe('A question about the webpage'),
    }),
  },
  async (input, { sendChunk }) => {
    const { url, query } = input;
    const { response, stream } = ai.generateStream({
      system: `You are a webpage analysis assistant. Analyze the content of the provided webpage and answer the question based on its content.`,
      prompt: [
        {
          media: {
            contentType: 'text/html',
            url,
          },
        },
        {
          text: `Question: ${query}`,
        },
      ],
    });
    for await (const chunk of stream) {
      sendChunk(chunk);
    }
    const { text } = await response;
    return text;
  },
);

A few things to note about this approach:

• Our prompt is now an array of input objects, where each object can be either text or media input.
• In the prompt, we are passing the url of the webpage as media input to the model. We are also passing the question as text input to the model.

In this particular case, this would not be very efficient as the model would have to spend a lot of its compute resources on downloading the webpage content and then summarizing it. However, when working with media content such as images and videos, the models are very good at understanding the content of the media and generating relevant outputs, as compare to trying to convert the media content into text and then passing it to the model. In such cases, it would be more efficient to pass the media content directly to the model as input and have the model process it.

Genkit’s Multi-Modal Inputs feature allows us to do just that. We can pass both text and media content as input to the model and have the model process it to generate relevant outputs. This technique is particularly useful for applications that require understanding and generating content based on multiple data types, such as image captioning, visual question answering, and more.

Let’s see how we can implement this in Genkit!

Prerequisites & Setup

You should have your environment set up for Genkit development. Follow the Getting Started guide if you haven't done so already.

I have a Genkit Starter Template that you can use to quickly spin up a new project with everything configured. You can find it here.

Building a Simple Image Analyzer Flow

In this example, we will build a flow that takes an image as input and generates a caption for the image. We will use the gemini-2.5-flash model for this task, but feel free to experiment with other models that support multi-modal inputs.

We will start by defining the flow with an input schema that accepts an image and a question about the image. The image will be a base64 encoded string, and the question will be a simple text string.

There are different tools and libraries you can use to convert images to base64 strings, such as the sharp library in Node.js.

const imageAnalyzerFlow = ai.defineFlow(
  {
    name: 'imageAnalyzer',
    inputSchema: z.object({
      image: z.string().describe('A base64-encoded image to analyze'),
      query: z.string().describe('A question about the image'),
    }),
  },
  async (input, { sendChunk }) => {
    const { image, query } = input;
    const { response, stream } = ai.generateStream({
      system: `You are an image analysis assistant. Analyze the provided image and answer the question based on its content.`,
      prompt: [
        {
          media: {
            contentType: 'image/png',
            // This must be a base64 encoded string with the data URI scheme, e.g. "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."
            url: image,
          },
        },
        {
          text: `Question: ${query}`,
        },
      ],
    });
    for await (const chunk of stream) {
      sendChunk(chunk);
    }
    const { text } = await response;
    return text;
  },
);

Just like in the webpage analyzer example, our prompt is an array of input objects, where the first object is a media input containing the base64 encoded image, and the second object is a text input containing the question about the image.

Now that we have created a simple image analyzer flow, we can test it out by passing a base64 encoded image and a question about the image as input to the flow. Let’s work on creating a functioning example of this in the next section!

Building a Receipt Scanner App

For our recipe, we will build a simple receipt scanner app that takes an image of a receipt as input and extracts relevant information such as the total amount, date, and merchant name from the receipt. This is a common use case for multi-modal inputs, as it requires the model to understand the content of the image and extract specific information from it.

First, we will define our output schema for the receipt information we want to extract, using Zod:

// Schema for individual receipt line items
const receiptItemSchema = z.object({
  name: z.string().describe('The name or description of the item'),
  quantity: z
    .number()
    .optional()
    .describe('The quantity of the item purchased'),
  unitPrice: z.number().optional().describe('The price per unit of the item'),
  totalPrice: z.number().describe('The total price for this line item'),
});

// Schema for the complete receipt data
const receiptDataSchema = z.object({
  merchantName: z.string().describe('The name of the merchant or business'),
  merchantAddress: z
    .string()
    .optional()
    .describe('The address of the merchant'),
  date: z
    .string()
    .optional()
    .describe('The date of the transaction (in ISO format if possible)'),
  items: z
    .array(receiptItemSchema)
    .describe('Array of all line items on the receipt'),
  subtotal: z.number().optional().describe('The subtotal before tax and tip'),
  tax: z.number().optional().describe('The tax amount'),
  tip: z.number().optional().describe('The tip amount (if applicable)'),
  total: z.number().describe('The total amount paid'),
  currency: z
    .string()
    .default('USD')
    .describe('The currency code (e.g., USD, EUR, GBP)'),
  confidence: z
    .number()
    .min(0)
    .max(100)
    .describe('Confidence score for the extraction (0-100)'),
  notes: z
    .string()
    .optional()
    .describe('Any additional notes or observations about the receipt'),
});

// Schema for receipt analysis with validation
const receiptAnalysisSchema = z.object({
  receiptData: receiptDataSchema,
  warnings: z
    .array(z.string())
    .describe('Any warnings or discrepancies detected during validation'),
  calculationValid: z
    .boolean()
    .describe('Whether the line items and totals match'),
});

A few things to note about this schema:

• We have defined a schema for individual receipt line items, which includes the item name, quantity, unit price, and total price.
• We have defined a schema for the complete receipt data, which includes the merchant name, address, date, array of line items, subtotal, tax, tip, total, currency, confidence score, and any additional notes.
• We have also defined a schema for the overall receipt analysis, which includes the extracted receipt data, any warnings or discrepancies detected during validation, and a boolean indicating whether the line items and totals match.

With our output schema defined, we can now create a flow that takes an image of a receipt as input and uses Genkit’s multi-modal input capabilities to extract the relevant information based on the content of the image.

import { startFlowServer } from '@genkit-ai/express';
import { googleAI } from '@genkit-ai/google-genai';
import { genkit, z } from 'genkit';

const ai = genkit({
  plugins: [googleAI()],
  model: googleAI.model('gemini-2.5-flash'),
});

const receiptScannerFlow = ai.defineFlow(
  {
    name: 'receiptScanner',
    inputSchema: z.object({
      image: z
        .string()
        .describe(
          'A base64-encoded image of the receipt to scan (data:image/png;base64,...)',
        ),
      currency: z
        .string()
        .optional()
        .describe('Expected currency code (defaults to USD)'),
    }),
    outputSchema: receiptAnalysisSchema,
  },
  async (input) => {
    const { image, currency = 'USD' } = input;

    // Our logic for processing the receipt image and extracting information would go here.
  },
);

A few things to note about this flow definition:

• The input schema expects a base64-encoded image of the receipt and an optional currency code.
• The output schema is the receiptAnalysisSchema we defined earlier, which includes all the relevant information we want to extract from the receipt.
• Inside the flow function, we would implement the logic to process the receipt image using the multi-modal input capabilities of Genkit, and return the extracted information in the format defined by our output schema.

In the next section, we will implement the logic for processing the receipt image and extracting the relevant information using Genkit’s multi-modal input capabilities.

Here is a high-level overview of the steps we would take to implement the receipt scanning logic:

1. Pass the base64-encoded image to the model as a media input in the prompt. This allows the model to analyze the content of the image directly and let the model do the heavy lifting of extracting information from the image based on our prompt.
2. Using the model’s response, which will be structured as per our defined output schema, we can post-process the response, this may include things like:
   • Saving the extracted data to a database (if applicable)
   • Performing additional validation or calculations on the extracted data
   • Returning the extracted data in a structured format to the user

Here is a simplified example of how the flow function might look like:

const receiptScannerFlow = ai.defineFlow(
  {
    // ... (same as before)
  },
  async (input) => {
    const { image, currency = 'USD' } = input;

    // Extract structured data from the receipt image
    const { output } = await ai.generate({
      system: `You are a receipt scanning assistant. Analyze the provided receipt image and extract all relevant information. Be precise with numbers and calculations. If you cannot read a field clearly, mark it as optional and note your confidence level.`,
      prompt: [
        {
          media: {
            url: image,
          },
        },
        {
          text: `Extract all data from this receipt. Include:
- Merchant name and address
- Transaction date
- All line items with names, quantities, unit prices, and totals
- Subtotal, tax, tip (if present), and final total
- Use ${currency} as the currency
- Provide a confidence score (0-100) based on image quality and readability`,
        },
      ],
      output: {
        schema: receiptDataSchema,
      },
    });

    if (!output) {
      throw new Error(
        'Failed to extract receipt data from the image. The image may be too blurry or not contain a valid receipt.',
      );
    }

    // Validate calculations
    const warnings: string[] = [];
    let calculationValid = true;

    // data validation and consistency checks
    // Check if line items sum to subtotal (if we have all the data)
    if (output.items.length > 0 && output.subtotal !== undefined) {
      const calculatedSubtotal = output.items.reduce(
        (sum, item) => sum + item.totalPrice,
        0,
      );
      const subtotalDifference = Math.abs(calculatedSubtotal - output.subtotal);

      if (subtotalDifference > 0.02) {
        // Allow 2 cents tolerance for rounding
        warnings.push(
          `Calculated subtotal ($${calculatedSubtotal.toFixed(2)}) differs from receipt subtotal ($${output.subtotal.toFixed(2)}) by $${subtotalDifference.toFixed(2)}`,
        );
        calculationValid = false;
      }
    }

    // Check if subtotal + tax + tip = total (if we have all the data)
    if (output.subtotal !== undefined && output.total !== undefined) {
      const calculatedTotal =
        (output.subtotal || 0) + (output.tax || 0) + (output.tip || 0);
      const totalDifference = Math.abs(calculatedTotal - output.total);

      if (totalDifference > 0.02) {
        // Allow 2 cents tolerance for rounding
        warnings.push(
          `Calculated total ($${calculatedTotal.toFixed(2)}) differs from receipt total ($${output.total.toFixed(2)}) by $${totalDifference.toFixed(2)}`,
        );
        calculationValid = false;
      }
    }

    // Warn if confidence is low
    if (output.confidence < 70) {
      warnings.push(
        `Low confidence score (${output.confidence}%). The image may be blurry or partially obscured. Results may be inaccurate.`,
      );
    }

    // Warn if critical fields are missing
    if (!output.date) {
      warnings.push(
        'Transaction date could not be extracted from the receipt.',
      );
    }
    if (!output.merchantAddress) {
      warnings.push(
        'Merchant address could not be extracted from the receipt.',
      );
    }

    return {
      receiptData: output,
      warnings,
      calculationValid,
    };
  },
);

A few things to note about this implementation:

• When we get the response, we check if the totals and calculations are consistent, and we generate warnings if there are any discrepancies or if the confidence score is low.
• If the models confidence in the extracted data is low, we include a warning to indicate that the results may be inaccurate, this might involve flagging the receipt for manual review by a human. This can happen if the image is not clear, if the receipt is damaged, or if the model is unsure about certain fields.
• We also check for critical fields like the transaction date, ensuring that we provide warnings if such fields cannot be extracted, as they are often important for the user to know.
• Finally, we return the extracted receipt data along with any warnings and a boolean indicating whether the calculations are valid.

This flow can be further enhanced with additional features such as:

• Support for multiple languages and currencies
• Integration with a database to store extracted receipt data
• A user interface for uploading receipt images and displaying extracted data (we will cover this on the frontend recipes)

And finally, we can serve this flow using Genkit’s flow server and test it out with different receipt images to see how well it performs!

startFlowServer({
  flows: [receiptScannerFlow],
  port: 3000,
  cors: true,
});

This will start a server on http://localhost:3000 where you can send POST requests to the /receiptScanner endpoint with the appropriate input to test the receipt scanning functionality.

Here is a curl example of how to test the flow:

curl --request POST \
  --url http://localhost:3000/receiptScanner \
  --header 'content-type: application/json' \
  --data '{
  "data": {
    "image": "data:image/jpeg;base64,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"
  }
}'

Conclusion

In this recipe, we have explored how to utilize multi-modal inputs in GenKit to create a more interactive and engaging user experience. By combining text, images, and other media types, we can enhance the way users interact with our applications and provide richer content. This approach allows us to leverage the strengths of different input modalities, making our applications more versatile and accessible to a wider audience.

🚀 Get the Complete Code

This recipe includes a full GitHub repository with working code, setup instructions, and examples. Unlock access to build faster and support this cookbook!

Complete, tested code repositories

Copy-paste ready examples

Lifetime access with updates

Support future recipes & content

Unlock All Recipes - 50% Off Already purchased? Sign in

Happy coding!