Document Inputs

Document inputs let multimodal models analyze files together with text prompts. Common use cases include summarization, information extraction, question answering, and document comparison.

To explore available models, visit cortecs.ai and filter by the Document tag.

Supported document formats, file sizes, and page limits depend on the model and provider. The examples below send a PDF as Base64-encoded data.

OpenAI Chat Completions API

Python

import base64
from pathlib import Path

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cortecs.ai/v1",
    api_key="<API_KEY>",
)

pdf_base64 = base64.b64encode(
    Path("path/to/document.pdf").read_bytes()
).decode("utf-8")

completion = client.chat.completions.create(
    model="<MODEL_NAME>",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "file",
                    "file": {
                        "filename": "document.pdf",
                        "file_data": f"data:application/pdf;base64,{pdf_base64}",
                    },
                },
                {
                    "type": "text",
                    "text": "Summarize the key points in this document.",
                },
            ],
        }
    ],
)

print(completion.choices[0].message.content)

Node.js

import fs from "fs";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.cortecs.ai/v1",
  apiKey: "<API_KEY>"
});

const pdfBase64 = fs
  .readFileSync("path/to/document.pdf")
  .toString("base64");

const completion = await client.chat.completions.create({
  model: "<MODEL_NAME>",
  messages: [
    {
      role: "user",
      content: [
        {
          type: "file",
          file: {
            filename: "document.pdf",
            file_data: `data:application/pdf;base64,${pdfBase64}`
          }
        },
        {
          type: "text",
          text: "Summarize the key points in this document."
        }
      ]
    }
  ]
});

console.log(completion.choices[0].message.content);

Curl

pdf_base64=$(base64 < path/to/document.pdf | tr -d '\n')

curl 'https://api.cortecs.ai/v1/chat/completions' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  -d '{
    "model": "<MODEL_NAME>",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "file",
            "file": {
              "filename": "document.pdf",
              "file_data": "data:application/pdf;base64,'"${pdf_base64}"'"
            }
          },
          {
            "type": "text",
            "text": "Summarize the key points in this document."
          }
        ]
      }
    ]
  }'

OpenAI Responses API

Python

import base64
from pathlib import Path

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cortecs.ai/v1",
    api_key="<API_KEY>",
)

pdf_base64 = base64.b64encode(
    Path("path/to/document.pdf").read_bytes()
).decode("utf-8")

response = client.responses.create(
    model="<MODEL_NAME>",
    input=[
        {
            "role": "user",
            "content": [
                {
                    "type": "input_file",
                    "filename": "document.pdf",
                    "file_data": f"data:application/pdf;base64,{pdf_base64}",
                },
                {
                    "type": "input_text",
                    "text": "Summarize the key points in this document.",
                },
            ],
        }
    ],
)

print(response.output_text)

Node.js

import fs from "fs";
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.cortecs.ai/v1",
  apiKey: "<API_KEY>"
});

const pdfBase64 = fs
  .readFileSync("path/to/document.pdf")
  .toString("base64");

const response = await client.responses.create({
  model: "<MODEL_NAME>",
  input: [
    {
      role: "user",
      content: [
        {
          type: "input_file",
          filename: "document.pdf",
          file_data: `data:application/pdf;base64,${pdfBase64}`
        },
        {
          type: "input_text",
          text: "Summarize the key points in this document."
        }
      ]
    }
  ]
});

console.log(response.output_text);

Curl

pdf_base64=$(base64 < path/to/document.pdf | tr -d '\n')

curl 'https://api.cortecs.ai/v1/responses' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  -d '{
    "model": "<MODEL_NAME>",
    "input": [
      {
        "role": "user",
        "content": [
          {
            "type": "input_file",
            "filename": "document.pdf",
            "file_data": "data:application/pdf;base64,'"${pdf_base64}"'"
          },
          {
            "type": "input_text",
            "text": "Summarize the key points in this document."
          }
        ]
      }
    ]
  }'

Anthropic Messages API

Python

import base64
from pathlib import Path

from anthropic import Anthropic

client = Anthropic(
    base_url="https://api.cortecs.ai",
    api_key="<API_KEY>",
)

pdf_base64 = base64.b64encode(
    Path("path/to/document.pdf").read_bytes()
).decode("utf-8")

message = client.messages.create(
    model="<MODEL_NAME>",
    max_tokens=1024,
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document",
                    "source": {
                        "type": "base64",
                        "media_type": "application/pdf",
                        "data": pdf_base64,
                    },
                },
                {
                    "type": "text",
                    "text": "Summarize the key points in this document.",
                },
            ],
        }
    ],
)

print("\n".join(block.text for block in message.content if block.type == "text"))

Node.js

import fs from "fs";
import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://api.cortecs.ai",
  apiKey: "<API_KEY>"
});

const pdfBase64 = fs
  .readFileSync("path/to/document.pdf")
  .toString("base64");

const message = await client.messages.create({
  model: "<MODEL_NAME>",
  max_tokens: 1024,
  messages: [
    {
      role: "user",
      content: [
        {
          type: "document",
          source: {
            type: "base64",
            media_type: "application/pdf",
            data: pdfBase64
          }
        },
        {
          type: "text",
          text: "Summarize the key points in this document."
        }
      ]
    }
  ]
});

console.log(
  message.content
    .filter((block) => block.type === "text")
    .map((block) => block.text)
    .join("\n")
);

Curl

pdf_base64=$(base64 < path/to/document.pdf | tr -d '\n')

curl 'https://api.cortecs.ai/v1/messages' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer <API_KEY>' \
  -H 'anthropic-version: 2023-06-01' \
  -d '{
    "model": "<MODEL_NAME>",
    "max_tokens": 1024,
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "document",
            "source": {
              "type": "base64",
              "media_type": "application/pdf",
              "data": "'"${pdf_base64}"'"
            }
          },
          {
            "type": "text",
            "text": "Summarize the key points in this document."
          }
        ]
      }
    ]
  }'