Docs:

Mon, 01 Jan 0001 00:00:00 +0000

NLP Apps capability

Overview of the NLP Apps capability, encompassing the underlying technology and its application in ATRIA

Introduction to NLP Apps technology

Within Natural language processing (NLP) technologies, NLP Apps refers to NLP pipelines (chains) that combine different technologies for language processing with several tools for combining them.

Currently, these technologies, both proprietary and third-party ones, are included in the Aura NLP component and can be categorized in the following groups:

Deterministic recognizers, such as Exact Match or Grammars.
Probabilistic recognizers, such as OpenAI embeddings: Semantic Search, that uses Azure OpenAI capabilities based on embeddings and Microsoft CLU.
Classifiers, from the traditional Domain Classifier to the embeddings-based Domain Classifier.
Entities recognition stages, Standard NER or Gazetteer NER, based on deterministic entity detection.
Other tools for executing different normalization tasks, connectors, adapters, etc.

The technical description of all the available NLP technologies in included in the document Components for NLP pipelines

Application of NLP Apps in ATRIA

ATRIA enables the generation of experiences (use cases) through the use of Aura cognitive capabilities as stand-alone NLP Apps for sending a request expressed in natural language and receiving back an accurate response without the need for a conversational bot.

Figure 6. NLP Apps in ATRIA

Interaction with NLP Apps in ATRIA

This service is accessible via API, enabling its consumption both from Aura Platform or any external application.

Functional overview

A simple overview of the process is provided below:

A user sends a request to ATRIA, indicating which specific NLP App she wants to use for the recognition of the request. These Apps are available in Aura NLP, a module of Aura Cognitive Services in charge of processing and understanding human natural language.
The NLP technologies in the pre-selected specific App resolves the use case and generates a response.
The response is sent back to the user.

Docs:

Mon, 01 Jan 0001 00:00:00 +0000

Semantic Search capability

Overview of the Semantic Search capability, encompassing the underlying technology and its application in ATRIA

Introduction to Semantic Search technology

Within Natural Language Processing technologies, Semantic Search goes beyond the traditional keyword-based search methods, as it delves into the intent and the meaning behind a query, interpreting the meaning of words and phrases.

This leads to the generation of more accurate and relevant search results that align closely with the user’s intent.

For this purpose, semantic search uses neural network embeddings: a representation of words or phrases in a continuous vector space that captures the semantic relationships between them. This information is crucial for semantic search to interpret the user’s intent accurately.

Figure 7. Semantic Search technology

Application of Semantic Search in ATRIA

Semantic Search is a specific NLP App, included in the NLP as a Service capability.

ATRIA benefits from the Semantic Search capability based on embeddings for the development of generic questions experiences (grounded in FAQs).
It allows achieving an accurate understanding of requests and the generation of highly reliable answers, fully aligned with the user's expectations.

Figure 8. Semantic Search in ATRIA

Interaction with Semantic Search in ATRIA

This service is accessible via API, enabling its consumption both from Aura Platform and any external application.

Semantic search technology is available in Aura through a specific Aura NLP stage: OpenAI embeddings.

Current available models

Semantic Search currently uses Azure OpenAI embeddings technology.

Check the version of the model here.

Functional overview

The use of this capability encompasses three different stages:

Preparation, for the creation of the use case knowledge bases with the required FAQs and associated answers, and the subsequent generation of embeddings with this information.
Identification, in which a user sends a request to ATRIA, selecting as the specific NLP App the one that includes the semantic search technology (OpenAI embeddings). This app recognizes the user’s request.
Answer generation: the best response to the user request is identified and sent back to the user.

Aura –

Docs:

NLP Apps capability

Introduction to NLP Apps technology

Application of NLP Apps in ATRIA

Interaction with NLP Apps in ATRIA

Functional overview

Docs:

Semantic Search capability

Introduction to Semantic Search technology

Application of Semantic Search in ATRIA

Interaction with Semantic Search in ATRIA

Current available models

Functional overview