PDF Inputs

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import OpenAI from 'openai';
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const openai = new OpenAI({
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  baseURL: 'https://api.staging.tram.ai.vn/v1',
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  apiKey: '<TRAM_AI_API_KEY>',
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});
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const result = await openai.chat.completions.create({
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  model: 'anthropic/claude-sonnet-4',
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  messages: [
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    {
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      role: 'user',
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      content: [
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        {
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          type: 'text',
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          text: 'What are the main points in this document?',
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        },
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        {
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          type: 'file',
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          file: {
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            filename: 'document.pdf',
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            fileData: 'https://bitcoin.org/bitcoin.pdf',
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          },
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        },
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      ],
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    },
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  ],
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  // Optional: Configure PDF processing engine
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  plugins: [
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    {
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      id: 'file-parser',
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      pdf: {
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        engine: 'mistral-ocr',
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      },
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    },
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  ],
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  stream: false,
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});
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console.log(result);

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import requests
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import json
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import base64
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from pathlib import Path
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def encode_pdf_to_base64(pdf_path):
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    with open(pdf_path, "rb") as pdf_file:
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        return base64.b64encode(pdf_file.read()).decode('utf-8')
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url = "https://api.staging.tram.ai.vn/v1/chat/completions"
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headers = {
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    "Authorization": f"Bearer {API_KEY_REF}",
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    "Content-Type": "application/json"
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}
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# Read and encode the PDF
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pdf_path = "path/to/your/document.pdf"
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base64_pdf = encode_pdf_to_base64(pdf_path)
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data_url = f"data:application/pdf;base64,{base64_pdf}"
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messages = [
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    {
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        "role": "user",
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        "content": [
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            {
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                "type": "text",
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                "text": "What are the main points in this document?"
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            },
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            {
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                "type": "file",
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                "file": {
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                    "filename": "document.pdf",
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                    "file_data": data_url
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                }
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            },
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        ]
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    }
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]
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# Optional: Configure PDF processing engine
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# PDF parsing will still work even if the plugin is not explicitly set
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plugins = [
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    {
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        "id": "file-parser",
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        "pdf": {
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            "engine": "cloudflare-ai"  # defaults to "mistral-ocr". See Pricing above
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        }
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    }
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]
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payload = {
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    "model": "google/gemma-3-27b-it",
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    "messages": messages,
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    "plugins": plugins
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}
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response = requests.post(url, headers=headers, json=payload)
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print(response.json())

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import requests
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import json
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import base64
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from pathlib import Path
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# First, encode and send the PDF
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def encode_pdf_to_base64(pdf_path):
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    with open(pdf_path, "rb") as pdf_file:
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        return base64.b64encode(pdf_file.read()).decode('utf-8')
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url = "https://api.staging.tram.ai.vn/v1/chat/completions"
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headers = {
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    "Authorization": f"Bearer {API_KEY_REF}",
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    "Content-Type": "application/json"
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}
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# Read and encode the PDF
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pdf_path = "path/to/your/document.pdf"
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base64_pdf = encode_pdf_to_base64(pdf_path)
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data_url = f"data:application/pdf;base64,{base64_pdf}"
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# Initial request with the PDF
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messages = [
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    {
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        "role": "user",
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        "content": [
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            {
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                "type": "text",
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                "text": "What are the main points in this document?"
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            },
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            {
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                "type": "file",
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                "file": {
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                    "filename": "document.pdf",
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                    "file_data": data_url
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                }
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            },
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        ]
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    }
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]
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payload = {
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    "model": "google/gemma-3-27b-it",
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    "messages": messages
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}
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response = requests.post(url, headers=headers, json=payload)
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response_data = response.json()
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# Store the annotations from the response
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file_annotations = None
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if response_data.get("choices") and len(response_data["choices"]) > 0:
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    if "annotations" in response_data["choices"][0]["message"]:
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        file_annotations = response_data["choices"][0]["message"]["annotations"]
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# Follow-up request using the annotations (without sending the PDF again)
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if file_annotations:
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    follow_up_messages = [
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        {
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            "role": "user",
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            "content": [
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                {
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                    "type": "text",
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                    "text": "What are the main points in this document?"
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                },
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                {
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                    "type": "file",
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                    "file": {
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                        "filename": "document.pdf",
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                        "file_data": data_url
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                    }
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                }
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            ]
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        },
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        {
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            "role": "assistant",
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            "content": "The document contains information about...",
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            "annotations": file_annotations
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        },
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        {
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            "role": "user",
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            "content": "Can you elaborate on the second point?"
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        }
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    ]
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    follow_up_payload = {
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        "model": "google/gemma-3-27b-it",
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        "messages": follow_up_messages
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    }
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    follow_up_response = requests.post(url, headers=headers, json=follow_up_payload)
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    print(follow_up_response.json())