| description | Reference documentation on preparing and configuring the Mindee client. |
|---|---|
| icon | wrench |
{% include "../../.gitbook/includes/this-is-reference-documenta....md" %}
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.
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.
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.
{% tabs %} {% tab title="Python" %} First import the needed classes:
from mindee import (
ClientV2,
InferenceParameters,
InferenceResponse,
PathInput, # for loading files from disk
)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(){% endtab %}
{% tab title="Node.js" %} First import the needed classes. We recommend using ES Modules.
import * as mindee from "mindee";
// If you're on CommonJS:
// const mindee = require("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.Client({ 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.Client();Advanced Usage
internally, undici is used for making HTTP calls, and when the client is initialized getGlobalDispatcher is called.
This is perfectly fine for the vast majority of cases:
If you set a custom Agent as the global dispatcher, Mindee will use it.
In the rare case where you need a specific dispatcher for Mindee, you can set it as follows:
import { Agent, interceptors } from "undici";
// Init the custom Dispatcher
// REPLACE THE LINE BELOW WITH YOUR CODE - this example will NOT work!
const myDispatcher = new Agent().compose( ... );
const mindeeClient = new mindee.Client({
// Don't set if using an environment variable
apiKey: apiKey,
// Activates verbose logging - disable in production!
debug: true,
// Pass the custom dispatcher to the Mindee client
dispatcher: myDispatcher,
});{% endtab %}
{% tab title="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);{% endtab %}
{% tab title="Ruby" %} First import the Mindee package:
require 'mindee'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 = Mindee::ClientV2.new(api_key: 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 = Mindee::ClientV2.new(){% endtab %}
{% tab title="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();{% endtab %}
{% tab title=".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();{% endtab %} {% endtabs %}
Product parameters control:
- which model to use
- server-side processing options
- how the results will be returned to you
You can set an alias for linking the sent file to your own system.
For example, you could use an internal PO number, or your database ID.
Aliases are not unique in our database, you can use the same alias multiple times.
{% tabs %}
{% tab title="Python" %}
Only the model_id is required.
inference_params = InferenceParameters(
# ID of the model, required.
model_id="MY_MODEL_ID",
# Use an alias to link the file to your own DB.
# If empty, no alias will be used.
alias="MY_ALIAS",
# ... any other options ...
){% endtab %}
{% tab title="Node.js" %}
Only the modelId is required.
const productParams = {
// ID of the model, required.
modelId: "MY_MODEL_ID",
// Use an alias to link the file to your own DB.
// If empty, no alias will be used.
alias: "MY_ALIAS",
// ... any other options ...
};{% endtab %}
{% tab title="PHP" %}
Only the modelId is required.
$inferenceParams = new InferenceParameters(
// ID of the model, required.
"MY_MODEL_ID",
// Use an alias to link the file to your own DB.
// If empty, no alias will be used.
alias: "MY_ALIAS",
// ... any other options ...
);{% endtab %}
{% tab title="Ruby" %}
Only the model_id is required.
inference_params = {
# ID of the model, required.
model_id: 'MY_MODEL_ID',
# Use an alias to link the file to your own DB.
# If empty, no alias will be used.
file_alias: 'MY_ALIAS',
# ... any other options ...
}{% endtab %}
{% tab title="Java" %}
Only the modelId is required.
InferenceParameters params = InferenceParameters
// ID of the model, required.
.builder("MY_MODEL_ID")
// Use an alias to link the file to your own DB.
// If empty, no alias will be used.
.alias("MY_ALIAS")
// ... any other options ...
// complete the builder
.build();{% endtab %}
{% tab title=".NET" %}
Only the modelId is required.
var inferenceParams = new InferenceParameters(
// ID of the model, required.
modelId: "MY_MODEL_ID"
// Use an alias to link the file to your own DB.
// If empty, no alias will be used.
, alias: "MY_ALIAS"
// ... any other options ...
);{% endtab %} {% endtabs %}
Enable or disable optional-features.
{% hint style="warning" icon="money-check-dollar-pen" %} Enabling a feature not in your plan will result in a Payment Required error (HTTP 402).
Check the #feature-comparison section for more information. {% endhint %}
The default activation states for Optional Features are set on the platform.
Any values set here will override the defaults.
Leave empty or null to use the default platform values.
For example: if the Polygon feature is enabled on the platform, and polygon is explicitly set to false in the parameters ⇒ the Polygon feature will not be enabled for the API call.
{% tabs %}
{% tab title="Python" %}
Only the model_id is required.
inference_params = InferenceParameters(
# ID of the model, required.
model_id="MY_MODEL_ID",
# Optional Features: set to `True` or `False` to override defaults
# Enhance extraction accuracy with Retrieval-Augmented Generation.
rag=None,
# Extract the full text content from the document as strings.
raw_text=None,
# Calculate bounding box polygons for all fields.
polygon=None,
# Boost the precision and accuracy of all extractions.
# Calculate confidence scores for all fields.
confidence=None,
# ... any other options ...
){% endtab %}
{% tab title="Node.js" %}
Only the modelId is required.
const productParams = {
// ID of the model, required.
modelId: "MY_MODEL_ID",
// Optional Features: set to `true` or `false` to override defaults
// Enhance extraction accuracy with Retrieval-Augmented Generation.
rag: undefined,
// Extract the full text content from the document as strings.
rawText: undefined,
// Calculate bounding box polygons for all fields.
polygon: undefined,
// Boost the precision and accuracy of all extractions.
// Calculate confidence scores for all fields.
confidence: undefined,
// ... any other options ...
};{% endtab %}
{% tab title="PHP" %}
Only the modelId is required.
$inferenceParams = new InferenceParameters(
// ID of the model, required.
"MY_MODEL_ID",
// Optional Features: set to `true` or `false` to override defaults
// Enhance extraction accuracy with Retrieval-Augmented Generation.
rag: null,
// Extract the full text content from the document as strings.
rawText: null,
// Calculate bounding box polygons for all fields.
polygon: null,
// Boost the precision and accuracy of all extractions.
// Calculate confidence scores for all fields.
confidence: null,
// ... any other options ...
);{% endtab %}
{% tab title="Ruby" %}
Only the model_id is required.
inference_params = {
# ID of the model, required.
model_id: 'MY_MODEL_ID',
# Options: set to `true` or `false` to override defaults
# Enhance extraction accuracy with Retrieval-Augmented Generation.
rag: nil,
# Extract the full text content from the document as strings.
raw_text: nil,
# Calculate bounding box polygons for all fields.
polygon: nil,
# Boost the precision and accuracy of all extractions.
# Calculate confidence scores for all fields.
confidence: nil,
# ... any other options ...
}{% endtab %}
{% tab title="Java" %}
Only the modelId is required.
InferenceParameters params = InferenceParameters
// ID of the model, required.
.builder("MY_MODEL_ID")
// Optional Features: set to `true` or `false` to override defaults
// Enhance extraction accuracy with Retrieval-Augmented Generation.
.rag(null)
// Extract the full text content from the document as strings.
.rawText(null)
// Calculate bounding box polygons for all fields.
.polygon(null)
// Boost the precision and accuracy of all extractions.
// Calculate confidence scores for all fields.
.confidence(null)
// ... any other options ...
// complete the builder
.build();{% endtab %}
{% tab title=".NET" %}
Only the modelId is required.
var inferenceParams = new InferenceParameters(
// ID of the model, required.
modelId: "MY_MODEL_ID"
// Optional Features: set to `true` or `false` to override defaults
// Enhance extraction accuracy with Retrieval-Augmented Generation.
, rag: null
// Extract the full text content from the document as strings.
, rawText: null
// Calculate bounding box polygons for all fields.
, polygon: null
// Boost the precision and accuracy of all extractions.
// Calculate confidence scores for all fields.
, confidence: null
// ... any other options ...
);{% endtab %} {% endtabs %}
These options allow changing how the model performs an inference on a per-call basis.
As such they can only be used via API.
{% hint style="info" %} These advanced features are not generally meant for improving model accuracy.
Instead, make sure the Data Schema has been properly optimized. {% endhint %}
Give additional guidelines to the model to help it better process a specific document.
This is a free-form text format.
Allows changing the Data Schema on a per-call basis: directly modify the Data Schema: add, remove, or change fields.
The typical use case is when the data needing to be extracted change based on internal business logic.
To download the JSON string appropriate for your model:
-
Go to your model's page
-
On the left-hand menu, click on "General Settings"
-
Scroll down to the "Actions" section
-
Click on the "Download Data Schema" button:
The Data Schema can be passed as a JSON string or by instantiating the appropriate classes.
If passed as a JSON string, it will be validated in the client before being sent to the server.
{% tabs %}
{% tab title="Python" %}
Only the model_id is required.
inference_params = InferenceParameters(
# ID of the model, required.
model_id="MY_MODEL_ID",
# Text Context
text_context="this is an invoice.",
# Data Schema
data_schema="{ ... JSON DATA ... }",
# ... any other options ...
){% endtab %}
{% tab title="Node.js" %}
Only the modelId is required.
const productParams = {
// ID of the model, required.
modelId: "MY_MODEL_ID",
// Text Context
textContext: "this is an invoice.",
// Data Schema
dataSchema: "{ ... JSON DATA ... }",
// ... any other options ...
};{% endtab %}
{% tab title="PHP" %}
Only the modelId is required.
$inferenceParams = new InferenceParameters(
// ID of the model, required.
"MY_MODEL_ID",
// Text Context
textContext: "this is an invoice.",
// Data Schema
dataSchema: "{ ... JSON DATA ... }",
// ... any other options ...
);{% endtab %}
{% tab title="Ruby" %}
Only the model_id is required.
inference_params = {
# ID of the model, required.
model_id: 'MY_MODEL_ID',
# Text Context
text_context: "this is an invoice.",
# Data Schema
data_schema: "{ ... JSON DATA ... }",
# ... any other options ...
}{% endtab %}
{% tab title="Java" %}
Only the modelId is required.
InferenceParameters params = InferenceParameters
// ID of the model, required.
.builder("MY_MODEL_ID")
// Text Context
.textContext("this is an invoice.")
// Data Schema
.dataSchema("{ ... JSON DATA ... }")
// ... any other options ...
// complete the builder
.build();{% endtab %}
{% tab title=".NET" %}
Only the modelId is required.
var inferenceParams = new InferenceParameters(
// ID of the model, required.
modelId: "MY_MODEL_ID"
// Text Context
, textContext: "this is an invoice."
// Data Schema
, dataSchema: "{ ... JSON DATA ... }"
// ... any other options ...
);{% endtab %} {% endtabs %}
Now that everything is ready, it's time to send your files to the Mindee servers.
For details on the next steps, to process:
- a local file or raw data ⇒ load-and-adjust-a-file.md
- a remote file (URL) ⇒ load-an-url.md