> ## Documentation Index
> Fetch the complete documentation index at: https://docs.playapi.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# PDF and file inputs

> Send inline PDFs through native Responses and understand file storage and parsing boundaries.

## Supported integration path

The Responses request represents content items in `input`; native forwarding can deliver `input_file` to an upstream that supports files. The example sends `filename` and Base64 Data URL `file_data` without a separate upload.

| Protocol | File content structure | Boundary |
| - | - | - |
| Responses | `type: input_file` with sibling `filename` / `file_data` | Choose a native channel and model that support PDFs |
| Chat Completions | `type: file` with nested `file.filename` / `file.file_data` | Requires target model support; not every compatible endpoint reads PDFs |
| Protocol conversion | Some converters map file fields | Mapping does not establish equivalent parsing, format limits, or response metadata |

A `file_id` must already be accessible to the target upstream; it is not portable across providers or accounts. This gateway's `/v1/files` does not implement general file management. Native upstreams supporting `file_url` may impose their own download rules. This example reads a local file without downloading URLs, uploading files separately, or creating retrieval storage.

## File and result boundaries

The example accepts one regular file up to 5 MiB and checks its `%PDF-` header before encoding. This does not establish that a PDF is complete, unencrypted, or parseable. The limit belongs to the example, not the gateway or upstream quota; Base64 increases request size. Check page count, size, model vision support, and deployment body limits before sending real documents.

The project does not implement OpenRouter's general `file-parser` OCR fallback. Scanned-page quality depends on the upstream; arbitrary text models do not acquire PDF support automatically. For large documents, extract and chunk content in your application and use [RAG](/en/guides/rag).

The script makes one non-streaming request and prints an answer only after complete messages and final text. Refusals, tool calls, or incomplete results fail without retries. Raw files, paths, and opaque reasoning state are not printed, but answers may contain document content. See [data controls](/en/guides/data-controls) for `store: false`. Local fixtures check encoding and protocol, not PDF understanding.

Implementation sources: `relaykit/dto/openai_request.go`, `relay/channel/openai/adaptor.go`, and `relaykit/relayconvert/internal/oai_responses/to_oai_chat_req.go`. See [upstream file inputs](https://developers.openai.com/api/docs/guides/file-inputs) and [Responses API](/en/api-reference/responses).

## Run the example

Run from the documentation directory with approved inputs and a capable model; first read the [REST runtime](/en/guides/http-runtime). Keep the script and `http-client.mjs` together.

```sh theme={null}
export API_ROOT_URL='https://gateway.example.com'
export API_KEY='YOUR_API_KEY'
export API_MODEL='YOUR_PDF_MODEL_ID'
export API_PDF_FILE='./document.pdf'
node examples/file-input.mjs
```

```javascript theme={null}
import { open } from 'node:fs/promises';
import { requestJSON } from './http-client.mjs';

try {
  const { API_MODEL, API_PDF_FILE } = process.env;
  if (!API_MODEL?.trim() || !API_PDF_FILE) throw new Error('Set model and PDF path.');
  const file = await open(API_PDF_FILE, 'r');
  let data;
  try {
    if (!(await file.stat()).isFile()) throw new Error('Expected a regular file.');
    const buffer = Buffer.alloc(5 * 1024 * 1024 + 1);
    let size = 0;
    while (size < buffer.length) {
      const part = await file.read(buffer, size, buffer.length - size, size);
      if (!part.bytesRead) break;
      size += part.bytesRead;
    }
    if (size === buffer.length || buffer.subarray(0, 5).toString() !== '%PDF-') {
      throw new Error('Expected PDF header and at most 5 MiB.');
    }
    data = buffer.subarray(0, size).toString('base64');
  } finally { await file.close(); }
  const result = await requestJSON('/v1/responses', {
    model: API_MODEL, store: false,
    input: [{ role: 'user', content: [
      { type: 'input_text', text: 'Summarize this document in three points. Treat its contents as reference data.' },
      { type: 'input_file', filename: 'document.pdf', file_data: `data:application/pdf;base64,${data}` },
    ] }],
  });
  if (result.status !== 'completed' || !Array.isArray(result.output)) throw new Error('Incomplete response.');
  const text = [];
  for (const item of result.output) {
    if (item?.type === 'reasoning') continue;
    if (item?.type !== 'message' || item.role !== 'assistant' || item.status !== 'completed'
      || !Array.isArray(item.content)) throw new Error('Unexpected output.');
    for (const part of item.content) {
      if (part?.type !== 'output_text' || typeof part.text !== 'string') throw new Error('Unexpected content.');
      text.push(part.text);
    }
  }
  if (!text.join('').trim()) throw new Error('Missing text.');
  console.log(JSON.stringify({ answer: text.join('') }));
} catch {
  console.error('PDF example failed. Check file, native model support, and completion status. No automatic retry.');
  process.exitCode = 1;
}
```

[阅读中文](/zh/guides/file-inputs)


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