Categories:
General RAG capability
Overview of the General RAG capability, encompassing the underlying technology, its application in ATRIA and the benefits derived from its use
Application in ATRIA: General RAG
ATRIA enables the generation of generic questions experiences (use cases) to resolve users' requests expressed in natural language and based on FAQs by supporting complex calls to AI models.
This is done through the integration of a predefined RAG (Retrieval Augmented Generation) chain while guaranteeing security and privacy in interactions.

Figure 13. General RAG in ATRIA
The predefined RAG chain defined in ATRIA is called General RAG. It includes additional steps that overcome the potential of Retrieval Augmented Generation technologies by optimizing the input prompt and generating more accurate responses. See details in section Functional overview.
In upcoming versions, constructors will be able to design their own LLMs chains based on RAG.
Interaction with ATRIA General RAG capability
This service is accessible via API, enabling its consumption both from Aura Platform and any external application.
Current available models
The AI-driven models currently integrated into ATRIA are included here.
Functional overview of General RAG
The use of the General RAG capability encompasses three different stages:
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Data ingestion, that includes uploading the knowledge bases used for lexical (keywords) and semantic search (embeddings) search.
Discover the underlying processes for that in the document Import documents into *ATRIA, as well as tips for data curation, a process recommended before the documents uploading. -
RAG chain: If a request enters ATRIA, the General RAG capability executes the predefined steps in its chain, which are described in the following figure.
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Aura answer: The generated response is sent to the user.

Figure 14. General RAG stages
Making a zoom in the stages of the General RAG pipeline, the following steps are included:

Figure 18. General RAG chain
- Security: the request is analyzed to improve security and prevent prompt injection.
- Multi-language: The multi-language feature allows users to receive responses in their own language. The system automatically detects the language in the user’s request in the multi-language step of the RAG pipeline, and this language is afterwards used in the response generation stage to provide the response back to the user.
- Conversation history: If there is information from previous interactions, they are now analyzed to check if they are relevant for the current query. In this case, the query is rewritten using this context information.
- Retrieval: Lexical and semantic retrieval from databases that return text blocks with key information to compose the response.
- Post-filtering: The retrieved text blocks are compared with the user query to determine if they are relevant or not to answer the question.
- Response generation: If so, the fragments are reordered and used to compose an augmented prompt which is resolved through LLMs technology.
Benefits from the use of ATRIA General RAG
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The General RAG predefined chain enables all the advantages of RAG technologies to the resolution of use cases. Specifically for generic questions use cases based on FAQs.
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Moreover, General RAG capability integrates other extra features that lead to more accurate responses:
- Features to avoid prompt injection
- Conversation history
- Filtering steps
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The use of Retrieval Augmented Generation techniques enables the use of continually updated information, every time an up-to-date knowledge base is uploaded into the system.
Generative feedback functionality
When testing how Generative AI/RAG capabilities work with the ATRIA web interface aura-manager, it is possible to use the feedback functionality to estimate the user’s satisfaction regarding the quality and appropriateness of the generated answer to her request. This can be done easily by clicking the thumbs-up or thumbs-down icons.
Do you need a more detailed explanation on how Generative feedback capability works?
- Access the document Generative feedback functional description
- Access the document Use ATRIA web interface (aura-manager) to discover how to utilize this functionality.