- Cloudmersive Documentation

Node.JS Python C# Java PHP Objective-C Ruby Apex C/C++ cURL Swift JavaScript Go

Run this command to install the SDK:

npm install cloudmersive-documentai-api-client --save

Or add this snippet to your package.json:

  "dependencies": {
    "cloudmersive-documentai-api-client": "^1.3.1"
  }

var CloudmersiveDocumentaiApiClient = require('cloudmersive-documentai-api-client');
var defaultClient = CloudmersiveDocumentaiApiClient.ApiClient.instance;

// Configure API key authorization: Apikey
var Apikey = defaultClient.authentications['Apikey'];
Apikey.apiKey = 'YOUR API KEY';



var apiInstance = new CloudmersiveDocumentaiApiClient.RunBatchJobApi();

var opts = { 
  'recognitionMode': "recognitionMode_example", // String | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images
  'body': new CloudmersiveDocumentaiApiClient.AdvancedExtractFieldsRequest() // AdvancedExtractFieldsRequest | Input document and parameters
};

var callback = function(error, data, response) {
  if (error) {
    console.error(error);
  } else {
    console.log('API called successfully. Returned data: ' + data);
  }
};
apiInstance.extractFieldsFromDocumentAdvancedBatchJob(opts, callback);

Run this command to install the SDK:

pip install cloudmersive-documentai-api-client

from __future__ import print_function
import time
import cloudmersive_documentai_api_client
from cloudmersive_documentai_api_client.rest import ApiException
from pprint import pprint

# Configure API key authorization: Apikey
configuration = cloudmersive_documentai_api_client.Configuration()
configuration.api_key['Apikey'] = 'YOUR_API_KEY'



# create an instance of the API class
api_instance = cloudmersive_documentai_api_client.RunBatchJobApi(cloudmersive_documentai_api_client.ApiClient(configuration))
recognition_mode = 'recognition_mode_example' # str | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images (optional)
body = cloudmersive_documentai_api_client.AdvancedExtractFieldsRequest() # AdvancedExtractFieldsRequest | Input document and parameters (optional)

try:
    # Extract Field Values from a Document using Advanced AI as a Batch Job
    api_response = api_instance.extract_fields_from_document_advanced_batch_job(recognition_mode=recognition_mode, body=body)
    pprint(api_response)
except ApiException as e:
    print("Exception when calling RunBatchJobApi->extract_fields_from_document_advanced_batch_job: %s\n" % e)

.NET Framework | .NET Core

Run this command to install the SDK:

Install-Package Cloudmersive.APIClient.NET.DocumentAI -Version 3.0.4

using System;
using System.Diagnostics;
using Cloudmersive.APIClient.NET.DocumentAI.Api;
using Cloudmersive.APIClient.NET.DocumentAI.Client;
using Cloudmersive.APIClient.NET.DocumentAI.Model;

namespace Example
{
    public class DocumentAiDocumentBatchJobExtractFieldsAdvancedPostExample
    {
        public void main()
        {
            // Configure API key authorization: Apikey
            Configuration.Default.AddApiKey("Apikey", "YOUR_API_KEY");
            
            

            var apiInstance = new RunBatchJobApi();
            var recognitionMode = recognitionMode_example;  // string | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images (optional) 
            var body = new AdvancedExtractFieldsRequest(); // AdvancedExtractFieldsRequest |  (optional) 

            try
            {
                // Extract Field Values from a Document using Advanced AI as a Batch Job
                ExtractDocumentBatchJobResult result = apiInstance.DocumentAiDocumentBatchJobExtractFieldsAdvancedPost(recognitionMode, body);
                Debug.WriteLine(result);
            }
            catch (Exception e)
            {
                Debug.Print("Exception when calling RunBatchJobApi.DocumentAiDocumentBatchJobExtractFieldsAdvancedPost: " + e.Message );
            }
        }
    }
}

using System;
using System.Diagnostics;
using Cloudmersive.APIClient.NETCore.DocumentAI.Api;
using Cloudmersive.APIClient.NETCore.DocumentAI.Client;
using Cloudmersive.APIClient.NETCore.DocumentAI.Model;

namespace Example
{
    public class ExtractFieldsFromDocumentAdvancedBatchJobExample
    {
        public void main()
        {
            // Configure API key authorization: Apikey
            Configuration.Default.AddApiKey("Apikey", "YOUR_API_KEY");
            
            

            var apiInstance = new RunBatchJobApi();
            var recognitionMode = recognitionMode_example;  // string | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images (optional) 
            var body = new AdvancedExtractFieldsRequest(); // AdvancedExtractFieldsRequest | Input document and parameters (optional) 

            try
            {
                // Extract Field Values from a Document using Advanced AI as a Batch Job
                ExtractDocumentBatchJobResult result = apiInstance.ExtractFieldsFromDocumentAdvancedBatchJob(recognitionMode, body);
                Debug.WriteLine(result);
            }
            catch (Exception e)
            {
                Debug.Print("Exception when calling RunBatchJobApi.ExtractFieldsFromDocumentAdvancedBatchJob: " + e.Message );
            }
        }
    }
}

To install with Maven, add a reference to the repository in pom.xml:

And add a reference to the dependency in pom.xml:

To install with Gradle, add it in your root build.gradle at the end of repositories:

And add the dependency in build.gradle:

Run this command to install the SDK:

composer require cloudmersive/cloudmersive_documentai_api_client

<?php
require_once(__DIR__ . '/vendor/autoload.php');

// Configure API key authorization: Apikey
$config = Swagger\Client\Configuration::getDefaultConfiguration()->setApiKey('Apikey', 'YOUR_API_KEY');



$apiInstance = new Swagger\Client\Api\RunBatchJobApi(
    
    
    new GuzzleHttp\Client(),
    $config
);
$recognition_mode = "recognition_mode_example"; // string | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images
$body = new \Swagger\Client\Model\AdvancedExtractFieldsRequest(); // \Swagger\Client\Model\AdvancedExtractFieldsRequest | Input document and parameters

try {
    $result = $apiInstance->extractFieldsFromDocumentAdvancedBatchJob($recognition_mode, $body);
    print_r($result);
} catch (Exception $e) {
    echo 'Exception when calling RunBatchJobApi->extractFieldsFromDocumentAdvancedBatchJob: ', $e->getMessage(), PHP_EOL;
}
?>

Add the Objective-C client to your Podfile:

Add the Ruby client to your Gemfile:

gem 'cloudmersive-documentai-api-client', '~> 2.2'

# load the gem
require 'cloudmersive-documentai-api-client'
# setup authorization
CloudmersiveDocumentaiApiClient.configure do |config|
  # Configure API key authorization: Apikey
  config.api_key['Apikey'] = 'YOUR API KEY'
  # Uncomment the following line to set a prefix for the API key, e.g. 'Bearer' (defaults to nil)
  #config.api_key_prefix['Apikey'] = 'Bearer'
end

api_instance = CloudmersiveDocumentaiApiClient::RunBatchJobApi.new

opts = { 
  recognition_mode: 'recognition_mode_example', # String | Optional; Recognition mode - Advanced (default) provides the highest accuracy but slower speed, while Normal provides faster response but lower accuracy for low quality images
  body: CloudmersiveDocumentaiApiClient::AdvancedExtractFieldsRequest.new # AdvancedExtractFieldsRequest | Input document and parameters
}

begin
  #Extract Field Values from a Document using Advanced AI as a Batch Job
  result = api_instance.extract_fields_from_document_advanced_batch_job(opts)
  p result
rescue CloudmersiveDocumentaiApiClient::ApiError => e
  puts "Exception when calling RunBatchJobApi->extract_fields_from_document_advanced_batch_job: #{e}"
end

Download and copy the /client folder into your Apex project:

Download Apex Client

SwagRunBatchJobApi api = new SwagRunBatchJobApi();
SwagClient client = api.getClient();

// Configure API key authorization: Apikey
ApiKeyAuth Apikey = (ApiKeyAuth) client.getAuthentication('Apikey');
Apikey.setApiKey('YOUR API KEY');

Map<String, Object> params = new Map<String, Object>{
    'recognitionMode' => 'recognitionMode_example',
    'body' => SwagAdvancedExtractFieldsRequest.getExample()
};

try {
    // cross your fingers
    SwagExtractDocumentBatchJobResult result = api.extractFieldsFromDocumentAdvancedBatchJob(params);
    System.debug(result);
} catch (Swagger.ApiException e) {
    // ...handle your exceptions
}

Install libcurl in your C/C++ project:

libcurl/7.75.0

CURL *curl;
CURLcode res;
curl = curl_easy_init();
if(curl) {
     curl_easy_setopt(curl, CURLOPT_CUSTOMREQUEST, "POST");
     curl_easy_setopt(curl, CURLOPT_URL, "https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced");
     curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
     curl_easy_setopt(curl, CURLOPT_DEFAULT_PROTOCOL, "https");
     struct curl_slist *headers = NULL;
     headers = curl_slist_append(headers, "recognitionMode: <string>");
     headers = curl_slist_append(headers, "Content-Type: application/json");
     headers = curl_slist_append(headers, "Apikey: YOUR-API-KEY-HERE");
     curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
     const char *data = "{\n    \"InputFile\": \"<byte>\",\n    \"FieldsToExtract\": [\n        {\n            \"FieldName\": \"<string>\",\n            \"FieldOptional\": \"<boolean>\",\n            \"FieldDescription\": \"<string>\",\n            \"FieldExample\": \"<string>\"\n        },\n        {\n            \"FieldName\": \"<string>\",\n            \"FieldOptional\": \"<boolean>\",\n            \"FieldDescription\": \"<string>\",\n            \"FieldExample\": \"<string>\"\n        }\n    ],\n    \"MaximumPagesProcessed\": \"<integer>\",\n    \"Preprocessing\": \"<string>\",\n    \"ResultCrossCheck\": \"<string>\",\n    \"RotateImageDegrees\": \"<double>\"\n}";
     curl_easy_setopt(curl, CURLOPT_POSTFIELDS, data);
     res = curl_easy_perform(curl);
}
curl_easy_cleanup(curl);

curl --location --request POST 'https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced' \
--header 'recognitionMode: <string>' \
--header 'Content-Type: application/json' \
--header 'Apikey: YOUR-API-KEY-HERE' \
--data-raw '{
    "InputFile": "<byte>",
    "FieldsToExtract": [
        {
            "FieldName": "<string>",
            "FieldOptional": "<boolean>",
            "FieldDescription": "<string>",
            "FieldExample": "<string>"
        },
        {
            "FieldName": "<string>",
            "FieldOptional": "<boolean>",
            "FieldDescription": "<string>",
            "FieldExample": "<string>"
        }
    ],
    "MaximumPagesProcessed": "<integer>",
    "Preprocessing": "<string>",
    "ResultCrossCheck": "<string>",
    "RotateImageDegrees": "<double>"
}'

import Foundation
#if canImport(FoundationNetworking)
import FoundationNetworking
#endif

var semaphore = DispatchSemaphore (value: 0)

let parameters = "{\n    \"InputFile\": \"<byte>\",\n    \"FieldsToExtract\": [\n        {\n            \"FieldName\": \"<string>\",\n            \"FieldOptional\": \"<boolean>\",\n            \"FieldDescription\": \"<string>\",\n            \"FieldExample\": \"<string>\"\n        },\n        {\n            \"FieldName\": \"<string>\",\n            \"FieldOptional\": \"<boolean>\",\n            \"FieldDescription\": \"<string>\",\n            \"FieldExample\": \"<string>\"\n        }\n    ],\n    \"MaximumPagesProcessed\": \"<integer>\",\n    \"Preprocessing\": \"<string>\",\n    \"ResultCrossCheck\": \"<string>\",\n    \"RotateImageDegrees\": \"<double>\"\n}"
let postData = parameters.data(using: .utf8)

var request = URLRequest(url: URL(string: "https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced")!,timeoutInterval: Double.infinity)
request.addValue("<string>", forHTTPHeaderField: "recognitionMode")
request.addValue("application/json", forHTTPHeaderField: "Content-Type")
request.addValue("YOUR-API-KEY-HERE", forHTTPHeaderField: "Apikey")

request.httpMethod = "POST"
request.httpBody = postData

let task = URLSession.shared.dataTask(with: request) { data, response, error in 
     guard let data = data else {
          print(String(describing: error))
          semaphore.signal()
          return
     }
     print(String(data: data, encoding: .utf8)!)
     semaphore.signal()
}

task.resume()
semaphore.wait()

XHR | jQuery

This code snippet uses the built-in JavaScript XHR request capability

var data = JSON.stringify({
     "InputFile": "<byte>",
     "FieldsToExtract": [
          {
               "FieldName": "<string>",
               "FieldOptional": "<boolean>",
               "FieldDescription": "<string>",
               "FieldExample": "<string>"
          },
          {
               "FieldName": "<string>",
               "FieldOptional": "<boolean>",
               "FieldDescription": "<string>",
               "FieldExample": "<string>"
          }
     ],
     "MaximumPagesProcessed": "<integer>",
     "Preprocessing": "<string>",
     "ResultCrossCheck": "<string>",
     "RotateImageDegrees": "<double>"
});

var xhr = new XMLHttpRequest();
xhr.withCredentials = true;

xhr.addEventListener("readystatechange", function() {
     if(this.readyState === 4) {
          console.log(this.responseText);
     }
});

xhr.open("POST", "https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced");
xhr.setRequestHeader("recognitionMode", "<string>");
xhr.setRequestHeader("Content-Type", "application/json");
xhr.setRequestHeader("Apikey", "YOUR-API-KEY-HERE");

xhr.send(data);

var settings = {
     "url": "https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced",
     "method": "POST",
     "timeout": 0,
     "headers": {
          "recognitionMode": "<string>",
          "Content-Type": "application/json",
          "Apikey": "YOUR-API-KEY-HERE"
     },
     "data": JSON.stringify({
          "InputFile": "<byte>",
          "FieldsToExtract": [
               {
                    "FieldName": "<string>",
                    "FieldOptional": "<boolean>",
                    "FieldDescription": "<string>",
                    "FieldExample": "<string>"
               },
               {
                    "FieldName": "<string>",
                    "FieldOptional": "<boolean>",
                    "FieldDescription": "<string>",
                    "FieldExample": "<string>"
               }
          ],
          "MaximumPagesProcessed": "<integer>",
          "Preprocessing": "<string>",
          "ResultCrossCheck": "<string>",
          "RotateImageDegrees": "<double>"
     }),
};

$.ajax(settings).done(function (response) {
     console.log(response);
});

package main

import (
     "fmt"
     "strings"
     "net/http"
     "io/ioutil"
)

func main() {

     url := "https://api.cloudmersive.com/document-ai/document/batch-job/extract/fields/advanced"
     method := "POST"

     payload := strings.NewReader(`{
    "InputFile": "<byte>",
    "FieldsToExtract": [
        {
            "FieldName": "<string>",
            "FieldOptional": "<boolean>",
            "FieldDescription": "<string>",
            "FieldExample": "<string>"
        },
        {
            "FieldName": "<string>",
            "FieldOptional": "<boolean>",
            "FieldDescription": "<string>",
            "FieldExample": "<string>"
        }
    ],
    "MaximumPagesProcessed": "<integer>",
    "Preprocessing": "<string>",
    "ResultCrossCheck": "<string>",
    "RotateImageDegrees": "<double>"
}`)

     client := &http.Client {
     }
     req, err := http.NewRequest(method, url, payload)

     if err != nil {
          fmt.Println(err)
          return
     }
     req.Header.Add("recognitionMode", "<string>")
     req.Header.Add("Content-Type", "application/json")
     req.Header.Add("Apikey", "YOUR-API-KEY-HERE")

     res, err := client.Do(req)
     if err != nil {
          fmt.Println(err)
          return
     }
     defer res.Body.Close()

     body, err := ioutil.ReadAll(res.Body)
     if err != nil {
          fmt.Println(err)
          return
     }
     fmt.Println(string(body))
}

Walkthrough Video