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The Responses API accepts text, images, audio, and files in a single request. Use structured input with content arrays to combine modalities.

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Supported input types

TypeContent typeFormatsDelivery
Textinput_textPlain textInline
Imageinput_imageJPEG, PNG, WebP, GIF, TIFFURL, base64 data URI, or file_id
Audioinput_audioWAV, MP3Base64
Documentinput_filePDF, DOCX, XLSX, PPTX, RTF, TXT, CSVURL, base64 data URI, or file_id
Videoinput_fileMP4Base64 data URI or file_id

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Image input

Send an image for the model to describe, analyze, or answer questions about. Provide the image as a URL, a base64 data URI, or a previously uploaded file_id.

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Image via URL

import { SDK } from '@meetkai/mka1';

const mka1 = new SDK({
  bearerAuth: `Bearer ${YOUR_API_KEY}`,
});

const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'Describe what you see in this image.' },
        {
          type: 'input_image',
          image_url: 'https://upload.wikimedia.org/wikipedia/commons/thumb/3/3a/Cat03.jpg/1200px-Cat03.jpg',
        },
      ],
    },
  ],
}, { headers: { 'X-On-Behalf-Of': '<end-user-id>' } });

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Image via base64

Encode the image as a data URI with the appropriate MIME type. 
import { readFileSync } from 'fs';

const imageBase64 = readFileSync('photo.jpg').toString('base64');

const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'What is in this photo?' },
        {
          type: 'input_image',
          image_url: `data:image/jpeg;base64,${imageBase64}`,
        },
      ],
    },
  ],
});

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Image via file_id

Upload an image with the Files API first, then reference it by ID. 
// Upload the image
const uploadResult = await mka1.llm.files.create({
  file: { fileName: 'photo.jpg', content: imageBuffer },
  purpose: 'assistants',
});

// Use the file_id in a response
const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'Describe this image.' },
        { type: 'input_image', file_id: uploadResult.id },
      ],
    },
  ],
});

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Audio input

Send audio for the model to process. The audio is automatically transcribed and the model responds to the spoken content. Supported formats: WAV and MP3 (max 25 MB). 
import { readFileSync } from 'fs';

const audioBase64 = readFileSync('recording.wav').toString('base64');

const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        {
          type: 'input_audio',
          input_audio: {
            data: audioBase64,
            format: 'wav',
          },
        },
      ],
    },
  ],
}, { headers: { 'X-On-Behalf-Of': '<end-user-id>' } });

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Document input

Send documents for the model to read and reason over. PDF and scanned documents are automatically processed with OCR — no extra configuration needed.

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Document via URL

const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'Summarize this document in three bullet points.' },
        {
          type: 'input_file',
          file_url: 'https://example.com/report.pdf',
          filename: 'report.pdf',
        },
      ],
    },
  ],
});

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Document via base64

Encode the file as a data URI. Include the MIME type so the API can route it to the correct processor. 
import { readFileSync } from 'fs';

const pdfBase64 = readFileSync('contract.pdf').toString('base64');

const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'What are the key terms in this contract?' },
        {
          type: 'input_file',
          file_data: `data:application/pdf;base64,${pdfBase64}`,
          filename: 'contract.pdf',
        },
      ],
    },
  ],
});

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Scanned documents and OCR

Scanned PDFs and images of documents are processed automatically. The API uses OCR to extract text from:
  • Scanned PDF pages (converted to images at 150 DPI, then OCR’d)
  • Photos of documents (JPEG, PNG, TIFF)
  • Office files (DOCX, XLSX, PPTX — converted to PDF first, then OCR’d)
Multi-page documents are processed in parallel. The extracted text is returned as Markdown and passed to the model for reasoning. No special parameters are needed — just send the file as input_file and the pipeline handles detection, conversion, and OCR.

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Supported document formats

FormatMIME typeProcessing
PDFapplication/pdfOCR per page at 150 DPI
JPEG / PNG / TIFF / WebP / GIFimage/*Direct OCR
Word (.doc, .docx)application/msword, application/vnd.openxmlformats-officedocument.wordprocessingml.documentConvert to PDF, then OCR
Excel (.xls, .xlsx)application/vnd.ms-excel, application/vnd.openxmlformats-officedocument.spreadsheetml.sheetConvert to PDF, then OCR
PowerPoint (.ppt, .pptx)application/vnd.ms-powerpoint, application/vnd.openxmlformats-officedocument.presentationml.presentationConvert to PDF, then OCR
RTFapplication/rtfConvert to PDF, then OCR
Plain text / CSVtext/plain, text/csvRead directly
Size limit: 30 MB per file.

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Mixed input

Combine multiple content types in a single message. The model sees all inputs together and can reason across them. 
const result = await mka1.llm.responses.create({
  model: 'meetkai:functionary-swahili-large',
  input: [
    {
      type: 'message',
      role: 'user',
      content: [
        { type: 'input_text', text: 'Compare the chart in the image with the data in the spreadsheet. Are the numbers consistent?' },
        {
          type: 'input_image',
          image_url: 'https://example.com/chart.png',
        },
        {
          type: 'input_file',
          file_url: 'https://example.com/data.xlsx',
          filename: 'data.xlsx',
        },
      ],
    },
  ],
});

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Next steps