Aura –

Docs:

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

NLP as a Service operational workflow

ATRIA technical operational flow corresponding to the operation of the NLP as a Service capability

Operational flowchart

The sequence diagram of the process executed by the NLP Apps capability is shown below:

@startuml
title NLP resolution API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
Application -> Kernel: Create two-legged token with scope aura-ai-services:nlp-messaging:write
Note right of Kernel: this token needs refreshing
Kernel -> Application: Response two-legged token
Application -> Kernel: Request to aura-aiservices/nlp/query with token
Kernel -> AuraGatewayApi: Request to aiservices/nlp/query with token-info header
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AuraNLPApp: Request recognition
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 200 and message
Kernel -> Application: response 200 and message
@enduml

NLP Apps operational sequence diagram

Docs:

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

Agent AI operational workflow

ATRIA technical operational flow corresponding to the operation of the Agents AI capability

Operational flowchart

The sequence diagram of the process executed by the agents AI capability is shown below:

@startuml
title Agent API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
participant AgentsManager #0796f5
participant MongoDeviceRecommender #11f5cf
participant AtriaModelGateway #f58e11
participant AzureOpenAI #9476e7
Application -> Kernel: Create two-legged token with scope aura-ai-services:agents-messaging:write
Note right of Kernel: this token needs refreshing
Kernel -> Application: Response two-legged token
Application -> Kernel: Request to aura-aiservices/agents/messages with token and correlatorId (x-correlator)
Kernel -> AuraGatewayApi: Request to aiservices/agents/messages with token-info header and correlatorId
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AgentsManager: Send Request to an agent
AgentsManager -> AgentsManager: Check if the context exists && retrieves it
AgentsManager -> AgentsManager: routing to the agents
AgentsManager -> MongoDeviceRecommender: Send Request to an agent with the context
MongoDeviceRecommender -> MongoDeviceRecommender: process the Request
group Agents process
  loop XX times
    MongoDeviceRecommender -> AtriaModelGateway: Send prompt to atria-model-gateway
    AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
    AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
    AtriaModelGateway -> MongoDeviceRecommender: Response prompt
    MongoDeviceRecommender -> MongoDeviceRecommender: Analyze the response
  end
end
MongoDeviceRecommender -> AgentsManager: response 200 and message
AgentsManager -> AgentsManager: Store the context
AgentsManager --> AuraGatewayApi: response 200 and message
AuraGatewayApi -> AuraGatewayApi: process response
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 200 and message
Kernel -> Application: response 200 and message
@enduml

Agents AI operational sequence diagram

Docs:

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

Generative AI operational workflow

ATRIA technical operational flow corresponding to the operation of the Generative AI capability

Operational flowchart

The sequence diagram of the process executed by the Generative AI capability is shown below:

@startuml
title Generative API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
participant AtriaModelGateway #f58e11
participant AzureOpenAI #9476e7
Application -> Kernel: Create two-legged token with scope aura-aiservices:messaging:write
Note right of Kernel: this token needs refreshing
Kernel -> Application: Response two-legged token
Application -> Kernel: Request to aura-aiservices/generative/prompts with token and correlatorId (x-correlator)
Kernel -> AuraGatewayApi: Request to aiservices/generative/prompts with token-info header and correlatorId
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AuraGatewayApi: Generate prompt
AuraGatewayApi -> AtriaModelGateway: Send prompt to atria-model-gateway
AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
AtriaModelGateway -> AuraGatewayApi: Response prompt
AuraGatewayApi -> AuraGatewayApi: process atria-model-gateway response
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 200 and message with session_id
Kernel -> Application: response 200 and message with session_id
@enduml

Generative AI operational sequence diagram

Docs:

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

ATRIA General RAG operational workflow

ATRIA technical operational flow corresponding to the operation of the RAG capability, specifically to the so-named General RAG predefined chain.

Flow diagram

Calls to the atria-rag-server component (AtriaRAG in the sequence diagram) executes the predefined RAG chain General RAG.


@startuml
title RAG API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
participant AtriaModelGateway #f58e11
participant AtriaRAG #f5de11
participant AzureOpenAI #9476e7

Application -> Kernel: Create two-legged token with scope aura-aiservices:messaging:write
Note right of Kernel: this token needs refreshing
Kernel -> Application: Response two-legged token
Application -> Kernel: Request to aura-aiservices/generative/prompts with token and correlatorId (x-correlator)
Kernel -> AuraGatewayApi: Request to aiservices/generative/prompts with token-info header and correlatorId
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AuraGatewayApi: Generate prompt
AuraGatewayApi -> AtriaModelGateway: Send prompt to atria-model-gateway
activate AtriaModelGateway
AtriaModelGateway -> AtriaRAG: 1.0: Enrich request 
activate AtriaRAG
AtriaRAG -> AtriaRAG: securityStg

opt translateStg.enabled == true
    AtriaRAG -> AtriaRAG: 1.1: Translate user query 
    AtriaRAG -> AtriaModelGateway: Send request to LLM 
    AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
    AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
    AtriaModelGateway --> AtriaRAG: LLM response with translated query
end
opt cleanStg.enabled == true
    AtriaRAG -> AtriaRAG: 1.2: Clean the user query 
    AtriaRAG -> AtriaModelGateway: Send request to LLM 
    AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
    AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
    AtriaModelGateway --> AtriaRAG: LLM response with new cleaned query

end
opt contextStg.enable == true
    alt Ask LLM
        AtriaRAG -> AtriaModelGateway: 1.3: Request LLM to validate the conversational context
        AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
        AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
        AtriaModelGateway --> AtriaRAG: LLM response [SAME CONTEXT] or [DIFFERENT CONTEXT]
        AtriaRAG -> AtriaRAG: Recreate Query
    end
    alt Recreate Query 
        AtriaRAG -> AtriaModelGateway: 1.4: Call LLM to generate new question 
        AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
        AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
        AtriaModelGateway --> AtriaRAG: Response with new question
    end
end

AtriaRAG -> AtriaRAG: retrievalStg

opt postFilteringStg.enable == true
    AtriaRAG -> AtriaRAG: Post Filtering 
    note right: Batch request
    AtriaRAG -> AtriaModelGateway: 1.5: Request LLM for each chunk 
    AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
    AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
    AtriaModelGateway --> AtriaRAG: LLM response [RELEVANT] or [IGNORABLE]
end

AtriaRAG -> AtriaModelGateway: 1.6: Request LLM generativeStg 
AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI 
AtriaModelGateway --> AtriaRAG: LLM response
AtriaRAG --> AtriaModelGateway: 2: Final response 
deactivate AtriaRAG
deactivate AtriaModelGateway

AtriaModelGateway -> AuraGatewayApi: Response Model Gateway
AuraGatewayApi -> AuraGatewayApi: process atria-model-gateway response
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 200 and message with session_id
Kernel -> Application: response 200 and message with session_id

@enduml

Docs:

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

Germany ATRIA General RAG operational workflow

ATRIA technical operational flow corresponding to the operation of the RAG capability, specifically to the so-named General RAG predefined chain, for one OB: Germany.

Flow diagram

Calls to the atria-rag-server component (AtriaRAG in the sequence diagram) executes the predefined RAG chain General RAG.

@startuml
title Germany RAG API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
participant AtriaModelGateway #f58e11
participant AtriaRAG #f5de11
participant AzureOpenAI #9476e7

Application -> Kernel: Create two-legged token with scope aura-aiservices:messaging:write
Note right of Kernel: this token needs refreshing
Kernel -> Application: Response two-legged token
Application -> Kernel: Request to aura-aiservices/generative/prompts with token and correlatorId (x-correlator)
Kernel -> AuraGatewayApi: Request to aiservices/generative/prompts with token-info header and correlatorId
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AuraGatewayApi: Generate prompt
AuraGatewayApi -> AtriaModelGateway: Send prompt to atria-model-gateway
activate AtriaModelGateway
AtriaModelGateway -> AtriaRAG: 1.0: Enrich request 
activate AtriaRAG
AtriaRAG -> AtriaRAG: securityStg


    alt Ask LLM
        AtriaRAG -> AtriaModelGateway: 1.3: Request LLM to validate the conversational context
        AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
        AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
        AtriaModelGateway --> AtriaRAG: LLM response [SAME CONTEXT] or [DIFFERENT CONTEXT]
        AtriaRAG -> AtriaRAG: Recreate Query
        AtriaRAG -> AtriaModelGateway: 1.4: Call LLM to generate new question 
        AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
        AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
        AtriaModelGateway --> AtriaRAG: Response with new question
    end


AtriaRAG -> AtriaRAG: retrievalStg

AtriaRAG -> AtriaRAG: Post Filtering 
note right: Batch request
AtriaRAG -> AtriaModelGateway: 1.5: Request LLM for each chunk 
AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI
AtriaModelGateway --> AtriaRAG: LLM response [RELEVANT] or [IGNORABLE]


AtriaRAG -> AtriaModelGateway: 1.6: Request LLM generativeStg 
AtriaModelGateway -> AzureOpenAI: Send Request to ChatCompletation endpoint
AzureOpenAI --> AtriaModelGateway: Response from AzureOpenAI 
AtriaModelGateway --> AtriaRAG: LLM response
AtriaRAG --> AtriaModelGateway: 2: Final response 
deactivate AtriaRAG
deactivate AtriaModelGateway

AtriaModelGateway -> AuraGatewayApi: Response Model Gateway
AuraGatewayApi -> AuraGatewayApi: process atria-model-gateway response
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 200 and message with session_id
Kernel -> Application: response 200 and message with session_id

@enduml

Docs:

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

Ingestion Process Automation

Technical operational flow of RAG data processing, specifically the automation of the atria-rag-generate-db process

Flow diagram

Flow of calls made to launch the generate-db process.

@startuml
title Ingestion Process Automation Flow

' Define participants with themed colors and clear names
actor User

participant "Azure Blob Storage" as AzureStorage #A2C4E0
participant "Gateway API" as GatewayAPI #bfb1f2
participant "Config Watcher" as ConfigWatcher #f296ee
participant "Deployment API" as DeploymentAPI #f77cbc
participant "Generate DB Process" as GenerateDBProcess #D9EAD3


' === Upload Files Stage ===
User -> AzureStorage : Upload training files
AzureStorage --> User : Response 200 OK

' === Launch generate-db ===
User -> GatewayAPI : Request to /aura-services/v2/operations/data to launch ingestion process
GatewayAPI -> ConfigWatcher : Request to Config Watcher
ConfigWatcher -> GenerateDBProcess : Start generate-db process
GenerateDBProcess --> ConfigWatcher : Response 200 OK
ConfigWatcher --> GatewayAPI : Response 200 OK
GatewayAPI --> User : Response 200 OK


' === Processing Stage ===
GenerateDBProcess -> AzureStorage : Read training files
AzureStorage --> GenerateDBProcess : Response 200 OK
GenerateDBProcess -> GenerateDBProcess : Processing training files

' === Logs Querying ===
... Logging queries can occur anytime ...


User -> GatewayAPI : Request to /aura-services/v2/operations/data/{presetId}/logs
GatewayAPI -> ConfigWatcher : Request to get logs
ConfigWatcher -> DeploymentAPI : Response 200 OK
DeploymentAPI --> ConfigWatcher : Response 200 OK
ConfigWatcher --> GatewayAPI : Response 200 OK
GatewayAPI --> User : Response 200 OK

' === Status Query ===
... Status queries can occur anytime ...

' === Status process ===
User -> GatewayAPI : Request to /aura-services/v2/operations/data/{presetId}/status to get status
GatewayAPI -> ConfigWatcher : Request to get status
ConfigWatcher -> DeploymentAPI : Response 200 OK
DeploymentAPI --> ConfigWatcher : Response 200 OK
ConfigWatcher --> GatewayAPI : Response 200 OK
GatewayAPI --> User : Response 200 OK


@enduml

Docs:

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

Reload config by Redis event in RAG and Model Gateway

Description of the process for updating and reloading ATRIA configuration by means of Redis events

Introduction

Once a preset or application configuration has been modified, a Redis event is automatically triggered to the PresetConfiguration and ApplicationConfiguration channels.

The components atria-model-gateway and atria-rag-server are subscribed to these channels and once an event arrives, they launch the configuration update process.

This process is transparent to the user.

Docs:

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

Feedback capability operational workflow

ATRIA technical operational flow corresponding to the operation of the feedback capability that can be used for Generative AI and RAG capabilities

Flow diagram

@startuml
title Feedback API diagram
participant Application
participant Kernel #1add4d
participant AuraGatewayApi #76bbe7
Note right of Application: The application has made a previous request to `aiservices/generative/prompts` on which it will give feedback. Use the correlatorId sending in the request and the session_id received in the response. View Generative API diagram
Application -> Kernel: Request to aiservices/{session_id}/feedback with token and msg_corrId: correlatorId
Kernel -> AuraGatewayApi: Request to aiservices/{session_id}/feedback with token-info header and msg_corrId: correlatorId
AuraGatewayApi -> AuraGatewayApi: Validate request
AuraGatewayApi -> AuraGatewayApi: Covert message to format atria-model-gateway
AuraGatewayApi -> AtriaModelGateway: Send feedback to atria-model-gateway
AtriaModelGateway -> AuraGatewayApi: Response feedback
AuraGatewayApi -> AuraGatewayApi: process atria-model-gateway response
AuraGatewayApi -> AuraGatewayApi: generate response
AuraGatewayApi -> Kernel: response 204
Kernel -> Application: response 204


@enduml

Generative feedback sequence diagram

Request

curl --location 'https://api.environment.baikalplatform.com/aura-aiservices/v1/{session_id}/feedback' \
--header 'x-correlator: <uuid2>' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--header 'Authorization: Bearer {token}' \
--data '{
  "application": {    
    "name": "app-name",    
  },
  "value": true,  
  "msg_corrId": "{previous-x-correlator}"
}'