Browse the docsOpenWorld

Image

POSThttps://labs-api.petpooja.com/v1/chat/completions

Send the image as a base64 data URI inside an image_url content part, alongside a text part describing the output you want. The text part is what makes this endpoint OpenWorld - ask for JSON with seller, buyer and line items, and that is what you get back.

Request body

Field	Type	Description
model	stringrequired	Must be `chitragupta`. Anything else returns `404 model_not_found`.
messages	arrayrequired	Roles `user` and `assistant` only. A `system` message returns `422 system_message_not_allowed` - the system prompt is owned server-side so behaviour stays consistent for every caller.
messages[].content[]	arrayrequired	Mix of `text` parts and `image_url` parts. Each `image_url.url` is a data URI: `data:image/jpeg;base64,…`. Up to 10 images per request.
max_tokens	integer	Optional. Defaults to `40000`, which is also the ceiling.

import base64, mimetypes
from openai import OpenAI

API_KEY  = "Your API Key"
BASE_URL = "https://labs-api.petpooja.com/v1/"
MODEL    = "chitragupta"

client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

IMAGE_PATH = "<IMAGE_PATH>"

mime = mimetypes.guess_type(IMAGE_PATH)[0] or "image/jpeg"
img_b64 = base64.b64encode(open(IMAGE_PATH, "rb").read()).decode()
image_data_uri = f"data:{mime};base64,{img_b64}"

response = client.chat.completions.create(
  model=MODEL,
  messages=[{
    "role": "user",
    "content": [
      {"type": "text", "text": ""},
      {"type": "image_url", "image_url": {"url": image_data_uri}},
    ],
  }],
)
print(response)

import fs from "node:fs";

const API_KEY = "Your API Key";
const BASE_URL = "https://labs-api.petpooja.com/v1/chat/completions";

const image = fs.readFileSync("<IMAGE_PATH>").toString("base64");

const res = await fetch(`${BASE_URL}`, {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Authorization: `Bearer ${API_KEY}`,
  },
  body: JSON.stringify({
    model: "chitragupta",
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Extract the invoice information as JSON." },
        { type: "image_url", image_url: { url: `data:image/jpeg;base64,${image}` } },
      ],
    }],
  }),
});

const data = await res.json();
console.log(data.choices[0].message.content);

package main

import (
  "encoding/base64"
  "fmt"
  "io"
  "net/http"
  "os"
  "strings"
)

const (
  key   = "Your API Key"
  url   = "https://labs-api.petpooja.com/v1/chat/completions"
  model = "chitragupta"
  path  = "<IMAGE_PATH>"
)

func main() {
  data, err := os.ReadFile(path)
  if err != nil {
    panic(err)
  }
  b64 := base64.StdEncoding.EncodeToString(data)

  body := fmt.Sprintf(`{"model":"%s","messages":[{"role":"user","content":[
{"type":"text","text":"Extract the invoice information from this image as JSON."},
{"type":"image_url","image_url":{"url":"data:image/jpeg;base64,%s"}}
]}]}`, model, b64)

  req, _ := http.NewRequest("POST", url, strings.NewReader(body))
  req.Header.Set("Authorization", "Bearer "+key)
  req.Header.Set("Content-Type", "application/json")

  res, err := http.DefaultClient.Do(req)
  if err != nil {
    panic(err)
  }
  defer res.Body.Close()
  out, _ := io.ReadAll(res.Body)

  fmt.Println(res.StatusCode)
  fmt.Println(string(out))
}

KEY="Your API Key"
BASE="https://labs-api.petpooja.com/v1/chat/completions"
IMAGE="<IMAGE_PATH>"
IMAGE_URI="data:image/jpeg;base64,$(base64 -w0 "$IMAGE")"
cat > /tmp/pp_chat_image.json <<EOF
{
  "model": "chitragupta",
  "max_tokens": 4000,
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Extract all visible text and details from this image."
        },
        {
          "type": "image_url",
          "image_url": {
            "url": "$IMAGE_URI",
            "detail": "high"
          }
        }
      ]
    }
  ]
}
EOF
curl -s -i "$BASE" \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/pp_chat_image.json

PDF

POSThttps://labs-api.petpooja.com/v1/chat/completions

Send the PDF as a base64 data URI inside a file content part. Every page is rendered to an image server-side and sent to the model in a single call, so the model can reason across pages - carry-forward totals, continuation tables, a summary on page 3 that refers to page 1.

Page budget

Rendered PDF pages and loose images share one budget of 10 per request. A 7-page PDF leaves room for 3 images. Exceed it and the request returns 413 page_limit_exceeded - split it, or use Default, which allows 25.

Request body

Field	Type	Description
model	stringrequired	Must be `chitragupta`.
file.file_data	stringrequired	Data URI of the PDF: `data:application/pdf;base64,…`.
file.filename	string	Optional label echoed into logs. Useful when you send more than one document.

import base64
from openai import OpenAI

API_KEY  = "Your API Key"
BASE_URL = "https://labs-api.petpooja.com/v1/"
MODEL    = "chitragupta"

client = OpenAI(api_key=API_KEY, base_url=BASE_URL)

PDF_PATH = "<PDF_PATH>"

pdf_b64 = base64.b64encode(open(PDF_PATH, "rb").read()).decode()
pdf_data_uri = f"data:application/pdf;base64,{pdf_b64}"

response = client.chat.completions.create(
  model=MODEL,
  messages=[{
    "role": "user",
    "content": [
      {"type": "text", "text": "Extract the invoice information from this document as JSON."},
      {"type": "file", "file": {"filename": "invoice.pdf", "file_data": pdf_data_uri}},
    ],
  }],
)

print(response)

import fs from "node:fs";

const apiKey = "Your API Key";
const baseURL = "https://labs-api.petpooja.com/v1/chat/completions";
const model = "chitragupta";
const pdfPath = "<PDF_PATH>";

async function main() {
  const b64 = fs.readFileSync(pdfPath).toString("base64");

  const body = {
    model,
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Extract information of last page from this document as JSON." },
        { type: "file", file: { filename: "invoice.pdf", file_data: `data:application/pdf;base64,${b64}` } }
      ]
    }]
  };

  const res = await fetch(baseURL, {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${apiKey}`,
      "Content-Type": "application/json"
    },
    body: JSON.stringify(body)
  });

  console.log("HTTP Status:", res.status);
  console.log(await res.text());
}

main();

package main

import (
  "encoding/base64"
  "fmt"
  "io"
  "net/http"
  "os"
  "strings"
)

const (
  apiKey  = "Your API Key"
  baseURL = "https://labs-api.petpooja.com/v1/chat/completions"
  model   = "chitragupta"
  pdfPath = "<PDF_PATH>"
)

func main() {
  data, err := os.ReadFile(pdfPath)
  if err != nil {
    panic(err)
  }
  b64 := base64.StdEncoding.EncodeToString(data)

  body := fmt.Sprintf(`{"model":"%s","messages":[{"role":"user","content":[
{"type":"text","text":"Extract information of last page from this document as JSON."},
{"type":"file","file":{"filename":"invoice.pdf","file_data":"data:application/pdf;base64,%s"}}
]}]}`, model, b64)

  req, _ := http.NewRequest("POST", baseURL, strings.NewReader(body))
  req.Header.Set("Authorization", "Bearer "+apiKey)
  req.Header.Set("Content-Type", "application/json")

  res, err := http.DefaultClient.Do(req)
  if err != nil {
    panic(err)
  }
  defer res.Body.Close()
  out, _ := io.ReadAll(res.Body)

  fmt.Println("HTTP Status:", res.StatusCode)
  fmt.Println(string(out))
}

KEY="Your API Key"
BASE="https://labs-api.petpooja.com/v1/chat/completions"
PDF="<PDF_PATH>"
PDF_URI="data:application/pdf;base64,$(base64 -w0 "$PDF")"
cat > /tmp/pp_chat_pdf.json <<EOF
{
  "model": "chitragupta",
  "max_tokens": 8000,
  "messages": [
    {
      "role": "user",
      "content": [
        {
          "type": "text",
          "text": "Extract all details from this document and return the extracted information."
        },
        {
          "type": "file",
          "file": {
            "file_data": "$PDF_URI",
            "filename": "document.pdf"
          }
        }
      ]
    }
  ]
}
EOF
curl -s -i "$BASE" \
  -H "Authorization: Bearer $KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/pp_chat_pdf.json