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Build e2e experiences

Build end-to-end experiences in ATRIA

“How to” workflows that schematically shows the orderly steps required to build end-to-end experiences using ATRIA capabilities

Introduction

In order to leverage the available ATRIA capabilities within a use case development, specific technical tasks must be performed by different Aura teams.

The current document provides a schematic overview of the end-to-end workflow and links to the corresponding technical guidelines tailored to each team responsible for specific aspects of the project.

Build experiences that call Generative AI

Build experiences that use General RAG

Build experiences that call NLP Apps

Build experiences that call Semantic Search

1 - Build experiences that call Generative AI

Build experiences that call Generative AI

Workflow with the main stages to build an end-to-end experience that calls an OpenAI GPT model

Introduction

Generative AI in Aura benefits from the auto-generative capabilities of Azure OpenAI GPT models for an accurate understanding of requests and the generation of highly reliable answers.

Steps in the process

a. Prerequisites: Install and enable

Enable ATRIA components in Aura installer

GES team

Check that the required components are enabled. If not:
Enable Generative components
Enable atria-model-gateway
Enable aura-manager (ATRIA web interface)

Publish aura-gateway-api in Kernel

GES team / Kernel DevOps Team

Is aura-gateway-api published in Kernel? If not:
Publish the aura-gateway-api API in Kernel as a prerequisite to call this API

Get a Kernel token

GES team

Check if your Kernel token has already expired. If so:
Get a valid Kernel two-legged token

}

b. Build experience

Configure, build and test your experience with Generative/RAG

Use case constructor

Guidelines for ATRIA uses cases constructors

2 - Build experiences that use General RAG

Build experiences that use General RAG

Workflow with the main stages to build an end-to-end experience that calls the General RAG model

Introduction

General RAG capability enables the implementation of RAG (Retrieval Augmented Generation) techniques to surpass the capabilities of LLMs in the development of generic questions use cases (based on FAQs).

Steps in the process

a. Prerequisites: Install and enable

Enable ATRIA components in Aura installer

GES team

Check that the required components are enabled. If not:
Enable RAG components
Enable atria-model-gateway
Enable atria-rag server
Enable aura-manager (ATRIA web interface)

Publish aura-gateway-api in Kernel

GES team / Kernel DevOps Team

Is aura-gateway-api published in Kernel? If not:
Publish the aura-gateway-api API in Kernel as a prerequisite to call this API

Get a Kernel token

GES team

Check if your Kernel token has already expired. If so:
Get a valid Kernel two-legged token

b. Build experience

Configure, build and test your experience with Generative/RAG

Use case constructor

Guidelines for ATRIA uses cases constructors

3 - Build experiences that call NLP apps

Build experiences that call NLP apps

Workflow with the main stages to build an end-to-end experience that calls NLP as a Service to use an NLP app

Introduction

Within NLP as a Service, the NLP Apps capability enables channels, services or skills to connect with Aura cognitive capabilities for sending a request expressed in natural language and receiving back an accurate response via API, without the need for a conversational bot.

Steps in the process

a. Prerequisites: Install and enable

1. Enable ATRIA components in Aura installer

GES team

Is Aura NLP deployed in your Aura system? If not:
Deploy Aura NLP
Enable NLP as a Service components

2. Publish aura-gateway-api in Kernel

GES team / Kernel DevOps Team

Is aura-gateway-api published in Kernel? If not:
Publish the aura-gateway-api API in Kernel as a prerequisite to call this API

3. Get a Kernel token

GES team

Check if your Kernel token has already expired. If so:
Get a valid Kernel two-legged token

b. Configure

4. Configure an application

Use case constructor

Configure an application to connect with aura-gateway-api

c. Build & test

5. Build the understanding model

Use case constructor

Generate and deploy the NLP recognition package for your use case

6. Make request to API

Use case constructor

Make a request to Aura NLP resolution API

4 - Build experiences that call Semantic Search

Build experiences that call Semantic Search

How to build an end-to-end experience that uses the Semantic Search stage (OpenAI embeddings recognizer), within NLP as a service

Introduction

Within [NLP as a Service], the Semantic Search capability enables the use of Azure OpenAI embeddings for the development of generic questions experiences (grounded in FAQs).

Steps in the process

a. Prequisites: Install and enable

1. Enable ATRIA components in Aura installer

GES team

Deploy Aura NLP
Enable NLP as a Service components

2. Publish aura-gateway-api in Kernel

GES team / Kernel DevOps Team

Is aura-gateway-api published in Kernel? If not:
Publish the aura-gateway-api API in Kernel as a prerequisite to call this API

3. Get a Kernel token

GES team

Check if your Kernel token has already expired. If so:
Get a valid Kernel two-legged token

b. Configure

4. Configure an application

Use case constructor

Configure an application to connect with aura-gateway-api

c. Build & test

5. Prepare the FAQ knowledge base

Content manager

Prepare the FAQ contents and answers used by the Semantic Search stage

6. Build the understanding model

Use case constructor

Generate and deploy the NLP recognition package for your use case
For the Semantic Search capability, the stage OpenAI embeddings is used

7. Make request to API

Use case constructor

Make a request to Aura NLP resolution API