Configure the Client

Reference documentation on preparing and configuring the Mindee client.

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Requirements

Before proceeding you'll need to have one of the official Mindee client libraries installed.

You'll also need to use one of your API Keys and one or several Models configured.

Overview

Before sending any files to the Mindee servers for processing, you'll need to initialize your client and set inference options.

These settings will determine how your files are sent, including any extra options, and how you want to process the result.

Initialize the Mindee Client

This should be the first step in your code. It will determine which organization is used to make the calls.

You should reuse the same client instance for all calls of the same organization.

The client instance is thread-safe where applicable.

Python

First import the needed classes:

from mindee import ClientV2, InferenceParameters

For the API key, you can pass it directly to the client. This is useful for quick testing.

api_key = "MY_API_KEY"

mindee_client = ClientV2(api_key)

Instead of passing the key directly, you can also set the following environment variable:

MINDEE_V2_API_KEY

This is recommended for production use. In this way there is no need to pass the api_key when initializing the client.

mindee_client = ClientV2()

Node.js

First import the needed classes. We recommend using TypeScript.

const mindee = require("mindee");
// for TS or modules:
// import * as mindee from "mindee";

For the API key, you can pass it directly to the client. This is useful for quick testing.

const apiKey = "MY_API_KEY";

const mindeeClient = new mindee.ClientV2({ apiKey: apiKey });

Instead of passing the key directly, you can also set the following environment variable:

MINDEE_V2_API_KEY

This is recommended for production use. In this way there is no need to pass the apiKey argument when initializing the client.

const mindeeClient = new mindee.ClientV2();

PHP

First import the needed classes:

use Mindee\ClientV2;
use Mindee\Input\InferenceParameters;
use Mindee\Error\MindeeException;

For the API key, you can pass it directly to the client. This is useful for quick testing.

$apiKey = "MY_API_KEY";

$mindeeClient = new ClientV2($apiKey);

Instead of passing the key directly, you can also set the following environment variable:

MINDEE_V2_API_KEY

This is recommended for production use. In this way there is no need to pass the $apiKey when initializing the client.

$mindeeClient = new ClientV2($apiKey);

Java

First import the needed classes:

import com.mindee.MindeeClientV2;
import com.mindee.InferenceParameters;

For the API key, you can pass it directly to the client. This is useful for quick testing.

String apiKey = "MY_API_KEY";

MindeeClientV2 mindeeClient = new MindeeClientV2(apiKey);

Instead of passing the key directly, you can also set the following environment variable:

MINDEE_V2_API_KEY

This is recommended for production use. In this way there is no need to pass the apiKey argument when initializing the client.

MindeeClientV2 mindeeClient = new MindeeClientV2();

.NET

First add the required namespaces.

using Mindee;
using Mindee.Input;

For the API key, you can pass it directly to the client. This is useful for quick testing.

string apiKey = "MY_API_KEY";

MindeeClientV2 mindeeClient = new MindeeClientV2(apiKey);

Instead of passing the key directly, you can also set the following environment variable:

MindeeV2__ApiKey

This is recommended for production use. In this way there is no need to pass the apiKey argument when initializing the client.

MindeeClientV2 mindeeClient = new MindeeClientV2();

Set Inference Parameters

Inference parameters control:

• which model to use

• server-side processing options

• how the results will be returned to you

Processing Options

These are mostly the same options as present in the Web API.

Python

Only the model_id is required.

inference_params = InferenceParameters(
    # ID of the model, required.
    model_id="MY_MODEL_ID",
    
    # Options:

    # If set to `True`, will enable Retrieval-Augmented Generation.
    rag=True,

    # Use an alias to link the file to your own DB.
    # If empty, no alias will be used.
    alias="MY_ALIAS",
)

Node.js

Only the modelId is required.

const inferenceParams = {
  // ID of the model, required.
  modelId: "MY_MODEL_ID",

  // Options:

  // If set to `true`, will enable Retrieval-Augmented Generation.
  rag: false,

  // Use an alias to link the file to your own DB.
  // If empty, no alias will be used.
  alias: "MY_ALIAS"
};

PHP

Only the modelId is required.

$inferenceParams = new InferenceParameters(
    // ID of the model, required.
    modelId: "MY_MODEL_ID",
    
    // Options:

    // If set to `true`, will enable Retrieval-Augmented Generation.
    rag: false,
    
    // Use an alias to link the file to your own DB.
    // If not set, no alias will be used.
    alias: "MY_ALIAS",
);

Java

Only the modelId is required.

InferenceParameters params = InferenceParameters
    // ID of the model, required.
    .builder("MY_MODEL_ID")

    // options

    // If set to `true`, will enable Retrieval-Augmented Generation.
    .rag(false)

    // Use an alias to link the file to your own DB.
    // If empty, no alias will be used.
    .alias("MY_ALIAS")

    // complete the builder
    .build();

.NET

Only the modelId is required.

var inferenceParams = new InferenceParameters(
    // ID of the model, required.
    modelId: "MY_MODEL_ID"

    // Options:

    // If set to `true`, will enable Retrieval-Augmented Generation.
    , rag: false

    // Use an alias to link the file to your own DB.
    // If empty, no alias will be used.
    , alias: "MY_ALIAS"
);

Polling Configuration

The client library will POST the request for you, and then automatically poll the API.

Python

When polling you really only need to set the model_id .

inference_params = InferenceParameters(model_id="MY_MODEL_ID")

You can also set the various polling parameters. However, we do not recommend setting this option unless you are encountering timeout problems.

from mindee import PollingOptions

inference_params = InferenceParameters(
    model_id="MY_MODEL_ID",
    
    # Set only if having timeout issues.
    polling_options=PollingOptions(
        # Initial delay before the first polling attempt.
        initial_delay_sec=3,
        # Delay between each polling attempt.
        delay_sec=1.5,
        # Total number of polling attempts.
        max_retries=80,
    ),
    # ... any other options ...
)

Node.js

When polling you really only need to set the modelId .

const inferenceParams = {modelId: "MY_MODEL_ID"};

You can also set the various polling parameters. However, we do not recommend setting this option unless you are encountering timeout problems.

const inferenceParams = {
  // ID of the model, required.
  modelId: "MY_MODEL_ID",
  
  // Set only if having timeout issues.
  pollingOptions: {
    // Initial delay before the first polling attempt.
    initialDelaySec: 3.0,
    // Delay between each polling attempt.
    delaySec: 1.5,
    // Total number of polling attempts.
    maxRetries: 80
  }
  // ... any other options ...
}

PHP

When polling you really only need to set the modelId .

$inferenceParams = new InferenceParameters(modelId: "MY_MODEL_ID");

You can also set the various polling parameters. However, we do not recommend setting this option unless you are encountering timeout problems.

use Mindee\Input\PollingOptions;

$inferenceParams = new InferenceParameters(
    // ID of the model, required.
    modelId: "MY_MODEL_ID",
    
    // Set only if having timeout issues.
    pollingOptions: new PollingOptions(
        // Initial delay before the first polling attempt.
        initialDelaySec: 3.0,
        // Delay between each polling attempt.
        delaySec: 1.5,
        // Total number of polling attempts.
        maxRetries: 80,
    ),
    // ... any other options ...
);

Java

When polling you really only need to set the modelId .

InferenceParameters params = InferenceParameters
        .builder("MY_MODEL_ID")
        .build();

You can also set the various polling parameters. However, we do not recommend setting this option unless you are encountering timeout problems.

InferenceParameters params = InferenceParameters
        .builder("MY_MODEL_ID")
        .pollingOptions(
            AsyncPollingOptions.builder()
                // Initial delay before the first polling attempt.
                .initialDelaySec(3.0)
                // Delay between each polling attempt.
                .intervalSec(1.5)
                // Total number of polling attempts.
                .maxRetries(80)
                // complete the builder
                .build()
        )
    
        // ... any other options ...
    
        .build();

.NET

When polling you really only need to set the modelId .

var inferenceParams = new InferenceParameters(modelId: "MY_MODEL_ID");

You can also set the various polling parameters. However, we do not recommend setting this option unless you are encountering timeout problems.

var inferenceParams = new InferenceParameters(
    modelId: "MY_MODEL_ID"
    
    // Set only if having timeout issues.
    , pollingOptions: new AsyncPollingOptions(
        // Initial delay before the first polling attempt.
        initialDelaySec: 3.0,
        // Delay between each polling attempt.
        intervalSec: 1.5,
        // Total number of polling attempts.
        maxRetries: 80
    )
    // ... any other options ...
);

Webhook Configuration

The client library will POST the request to your Web server, as configured by your webhook endpoint.

For more information on webhooks, take a look at the Webhooks page.

When using a webhook, you'll need to set the model ID and the webhook ID(s) to use.

Python

inference_params = InferenceParameters(
    model_id="MY_MODEL_ID",
    webhook_ids=["ENDPOINT_1_UUID"],
    
    # ... any other options ...
)

Node.js

const inferenceParams = {
  modelId: "MY_MODEL_ID",
  webhookIds: ["ENDPOINT_1_UUID"],

  // ... any other options ...
};

PHP

$inferenceParams = new InferenceParameters(
    modelId: "MY_MODEL_ID",
    webhooksIds: array("ENDPOINT_1_UUID"),

    // ... any other options ...
);

Java

InferenceParameters params = InferenceParameters
    .builder("MY_MODEL_ID")
    .webhookIds(new String[]{"ENDPOINT_1_UUID"})
    
    // ... any other options ...
    
    .build();

.NET

var inferenceParams = new InferenceParameters(
    modelId: "MY_MODEL_ID"
    , webhookIds: new List<string>{ "ENDPOINT_1_UUID" }
    
    // ... any other options ...
);

You can specify any number of webhook endpoint IDs, each will be sent the payload.

Next Steps

Now that everything is ready to, it's time to send your files to the Mindee servers.

If you're sending a local file, head on over to the Load and Adjust a File page for details on the next step.

If you're sending an URL, head on over to the Send a File or URL section.