Document Reading

LLMGateway supports sending documents (PDFs and other file types) to document-capable models using OpenAI's file content block format. The gateway forwards the document to the underlying provider so the model can read and reason over its contents.

Document-Capable Models

Document input is currently supported on Google Gemini models via Google AI Studio. You can find document-capable models on the models page with the document filter.

Sending a Document

Add a file content block to a user message. The file_data field must be a base64-encoded data URL that includes the document's MIME type.

curl -X POST "https://llm-gw.agenzo.com/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-flash",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Summarize this document."
          },
          {
            "type": "file",
            "file": {
              "filename": "report.pdf",
              "file_data": "data:application/pdf;base64,JVBERi0xLjQKJ..."
            }
          }
        ]
      }
    ]
  }'

Content Block Fields

• type: must be "file".
• file.filename (optional): original filename, shown in the playground and forwarded for context.
• file.file_data: base64-encoded data URL of the form data:<mime-type>;base64,<data>.

Supported File Types

The accepted MIME types depend on the target model. Gemini models commonly support:

• application/pdf
• text/plain
• text/html
• text/css
• text/javascript
• text/csv
• text/markdown
• text/xml

If the upstream provider rejects the MIME type, the gateway surfaces a 400 error including the unsupported MIME type and the provider it was sent to. To use a different file type, encode the file with the matching MIME type in the data URL prefix.

Encoding a File as a Data URL

Any tool that can produce base64 output works. For example, in a shell:

DATA=$(base64 -i report.pdf | tr -d '\n')
echo "data:application/pdf;base64,$DATA"

Or in JavaScript:

import { readFileSync } from "node:fs";

const buffer = readFileSync("report.pdf");
const fileData = `data:application/pdf;base64,${buffer.toString("base64")}`;

Then pass fileData as the file.file_data value in your request.

Multiple Documents

You can include multiple file blocks in a single message, optionally mixed with text and image content:

curl -X POST "https://llm-gw.agenzo.com/v1/chat/completions" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-2.5-pro",
    "messages": [
      {
        "role": "user",
        "content": [
          { "type": "text", "text": "Compare these two reports." },
          {
            "type": "file",
            "file": {
              "filename": "q1.pdf",
              "file_data": "data:application/pdf;base64,JVBERi0x..."
            }
          },
          {
            "type": "file",
            "file": {
              "filename": "q2.pdf",
              "file_data": "data:application/pdf;base64,JVBERi0x..."
            }
          }
        ]
      }
    ]
  }'

Error Handling

The gateway returns 400 for the following document-related errors:

• The selected model does not support document input.
• The file block is missing both file_data and file_id.
• file_data is not a valid base64 data URL.
• The upstream provider rejects the document's MIME type for the selected model.