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ATRIA Model Gateway architecture and components
Development architecture and technical components of the atria-model-gateway
Technical foundations
atria-model-gateway is responsible for managing the communication with different AI models. This component receives a request from aura-gateway-api, together with other input data, and makes a call the corresponding AI models.
If the selected AI model is RAG, then atria-model-gateway calls the atria-rag-server, which is in charge of executing the RAG chain and making the corresponding calls to the LLM models and databases.
Functional components
The functional components of atria-model-gateway are described in the document LLM/LMM Experiences Builder
Architecture overview
The following diagram schematically shows the main technical components integrated into atria-model-gateway.
A brief description of these components is included below:
Access module
Module for the management of different profiles to access atria-model-gateway.
Context module
Module in charge of the storage of a conversation history in a cache (currently, Redis is used) over a period of time, grouped by session ID. These conversations are taken into account when calling the generative LLM models.
Model manager
Module that includes the available models and presets. It is in charge of receiving the info from aura-gateway-api and calling the corresponding model.
Models
Available AI models integrated into the atria-model-gateway.
Presets
Presets are configurable entities to define the specific model to work with and certain parameters associated to it: model Id, name, description, model parameters, etc.
Constructors can use the default presets or build new ones: Go to document ATRIA configuration.
When configuring an application, all the presets that can be used for this application must be previously defined.