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Introduction

Introduction

Kodexa is a versatile and comprehensive platform designed for data processing, management, and machine learning. It excels in handling a broad spectrum of unstructured data and intelligent document processing challenges with its highly extensible capabilities.

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Data Storage Framework

The platform organizes data storage across various locations within its deployment infrastructure:

  1. Object Storage: This serves as the repository for binary data and native files.
  2. Database: Used for storing metadata, configuration details, and structured data.
  3. Search Indexing: An optional feature for additional metadata, configuration, and structured data indexing.
  4. Analytics Datamart: Another optional element that holds structured data, audit logs, and extraction metadata.

Efficient Data Movement

Data enters the platform via a secure REST API. Within the core framework, data is seamlessly transferred between the platform instance, Object Storage, and the Database. Depending on the deployment configuration, data may also be replicated to search indexes and/or an analytics datamart for enhanced analytic reporting.

Extension Packs, integral for additional functionalities, can be deployed within the Kubernetes cluster or via cloud-native serverless architectures, maintaining communication exclusively with the REST API.

Network Configuration and Access

Typically, the platform is deployed within a Kubernetes environment, enabling ingress. All network access is predominantly confined to a private network, with options to integrate into an enterprise network using standard cloud capabilities.

Secure Authentication Protocols

Deployments commonly utilize either an OAuth provider or an internal security system for authentication. User sessions are managed via JWT tokens, generated upon successful authentication and then integrated into the user experience.

Robust Encryption Standards

The platform is equipped to support encryption for both in-transit and at-rest data, ensuring a high level of security and data protection throughout its deployment.

Resource-Driven Design

The Kodexa platform allows for sharable “resources” to be defined, these resources are the building blocks of AI-driven document automation.

Metadata Classes

The Kodexa platform uses a hierarchy of metadata classes to represent various components and configurations:

Action

Represents a specific action in the system. Actions are discrete operations that can be performed within the Kodexa platform, such as processing documents, triggering workflows, or executing custom logic.

AssistantDefinition

Defines an AI assistant's capabilities. This class encapsulates the configuration, behavior, and functionality of AI assistants used in the platform for various tasks such as document analysis, question answering, or task automation.

CredentialDefinition

Defines credential types and their properties. This class is used to specify different types of authentication and authorization credentials used across the platform, ensuring secure access to various resources and services.

Dashboard

Represents a dashboard configuration. Dashboards provide a visual interface for users to monitor, analyze, and interact with data and processes within the Kodexa platform.

DataForm

Defines structure for data input forms. This class is used to create and manage forms for data entry, ensuring consistent and structured data collection across the platform.

ExtensionPack

Represents a package of platform extensions. Extension packs allow for the addition of new functionality, integrations, or customizations to the Kodexa platform, enhancing its capabilities and adaptability.

GuidanceSet

Defines a set of guidance rules or instructions. Guidance sets provide structured information to guide users or automated processes through complex tasks or decision-making scenarios.

ModelRuntime

Represents a runtime environment for models. This class defines the configuration and requirements for executing machine learning or AI models within the Kodexa platform, ensuring proper resource allocation and execution.

Pipeline

Defines a sequence of processing steps. Pipelines orchestrate the flow of data and operations, allowing for complex, multi-stage processing of documents or data within the platform.

ProjectTemplate

Represents a template for creating projects. Project templates provide predefined structures, configurations, and resources to streamline the creation of new projects within the Kodexa platform.

Prompt

Defines a prompt template for AI interactions. This class is used to create structured prompts for AI models, ensuring consistent and effective communication between users and AI assistants.

RuleSet

Represents a set of business or processing rules. Rule sets define logical conditions and actions to be applied to data or processes, enabling dynamic and configurable behavior within the platform.

Store

Represents a data store configuration. This class defines the properties and settings for various data storage solutions used within the Kodexa platform, ensuring proper data management and access.

Taxonomy

Defines a hierarchical classification system. Taxonomies provide a structured way to categorize and organize information within the platform, facilitating efficient data retrieval and analysis.