Categories:
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:
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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.
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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.
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Answer generation: the best response to the user request is identified and sent back to the user.