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Datamodels and Interfaces Component

Overview

The Datamodels and Interfaces components form the core data layer of the Gateway application. They provide a clean separation between data structures (datamodels) and data access logic (interfaces), enabling type-safe, maintainable, and scalable data operations throughout the application.

Component Architecture

graph TB
    subgraph "Application Layer"
        App[Application<br/>Routes, Services, Features]
    end
    
    subgraph "Interfaces Layer"
        IF_RealEstate[Real Estate Interface]
        IF_Chat[Chat Interface]
        IF_App[App Interface]
        IF_Component[Component Interface]
        IF_AI[AI Interface]
        IF_Ticket[Ticket Interface]
        IF_Voice[Voice Interface]
    end
    
    subgraph "Access Control Layer"
        AC_RealEstate[Real Estate Access]
        AC_Chat[Chat Access]
        AC_App[App Access]
        AC_Component[Component Access]
    end
    
    subgraph "Database Connectors"
        Connector_Postgre[PostgreSQL Connector]
    end
    
    subgraph "Databases"
        DB_RealEstate[(Real Estate<br/>Database)]
        DB_Chat[(Chat<br/>Database)]
        DB_App[(App<br/>Database)]
        DB_Component[(Component<br/>Database)]
    end
    
    subgraph "External Systems"
        External_AI[AI APIs<br/>OpenAI, Anthropic, etc.]
        External_Tickets[Ticket Systems<br/>Jira, ClickUp]
        External_Voice[Voice Services<br/>Google Cloud]
    end
    
    App -.->|uses| IF_RealEstate
    App -.->|uses| IF_Chat
    App -.->|uses| IF_App
    App -.->|uses| IF_Component
    App -.->|uses| IF_AI
    App -.->|uses| IF_Ticket
    App -.->|uses| IF_Voice
    
    IF_RealEstate --> AC_RealEstate
    IF_Chat --> AC_Chat
    IF_App --> AC_App
    IF_Component --> AC_Component
    
    AC_RealEstate --> Connector_Postgre
    AC_Chat --> Connector_Postgre
    AC_App --> Connector_Postgre
    AC_Component --> Connector_Postgre
    
    Connector_Postgre --> DB_RealEstate
    Connector_Postgre --> DB_Chat
    Connector_Postgre --> DB_App
    Connector_Postgre --> DB_Component
    
    IF_AI --> External_AI
    IF_Ticket --> External_Tickets
    IF_Voice --> External_Voice
    
    IF_RealEstate -.->|uses| DM_RealEstate[Real Estate<br/>Datamodels]
    IF_Chat -.->|uses| DM_Chat[Chat<br/>Datamodels]
    IF_App -.->|uses| DM_UAM[User & Mandate<br/>Datamodels]
    IF_Component -.->|uses| DM_Files[File<br/>Datamodels]
    IF_AI -.->|uses| DM_AI[AI<br/>Datamodels]

Data Flow

sequenceDiagram
    participant Route as API Route
    participant Service as Service Layer
    participant Interface as Interface
    participant Access as Access Control
    participant Connector as Database Connector
    participant DB as Database
    
    Route->>Service: Request with User Context
    Service->>Interface: Initialize with User
    Interface->>Access: Check Permissions
    Access-->>Interface: Permission Granted
    Service->>Interface: CRUD Operation
    Interface->>Access: Validate Access
    Access-->>Interface: Access Validated
    Interface->>Connector: Execute Query
    Connector->>DB: SQL Query
    DB-->>Connector: Result Set
    Connector-->>Interface: Data Objects
    Interface->>Access: Apply Filtering
    Access-->>Interface: Filtered Data
    Interface-->>Service: Datamodel Instances
    Service-->>Route: Response Data

Component Structure

Datamodels Structure

modules/datamodels/
├── datamodelRealEstate.py    # Real estate domain models
├── datamodelChat.py          # Chat workflow models
├── datamodelAi.py            # AI operation models
├── datamodelUam.py           # User and mandate models
├── datamodelSecurity.py      # Security and authentication models
├── datamodelFiles.py          # File management models
├── datamodelDocument.py       # Document structure models
├── datamodelExtraction.py     # Content extraction models
├── datamodelPagination.py     # Pagination models
├── datamodelVoice.py          # Voice settings models
├── datamodelTickets.py        # Ticket system models
├── datamodelNeutralizer.py    # Data neutralization models
├── datamodelTools.py          # Tool definitions
├── datamodelUtils.py          # Utility models
└── __init__.py                # Package exports

Interfaces Structure

modules/interfaces/
├── interfaceDbRealEstateObjects.py    # Real estate data access
├── interfaceDbRealEstateAccess.py     # Real estate access control
├── interfaceDbChatObjects.py          # Chat data access
├── interfaceDbChatAccess.py           # Chat access control
├── interfaceDbAppObjects.py           # App/user management access
├── interfaceDbAppAccess.py            # App access control
├── interfaceDbComponentObjects.py     # Component management access
├── interfaceDbComponentAccess.py      # Component access control
├── interfaceAiObjects.py              # AI operations interface
├── interfaceTicketObjects.py          # Ticket system interface
└── interfaceVoiceObjects.py           # Voice operations interface

Datamodels Component

datamodelRealEstate.py

erDiagram
    Projekt {
        string id PK
        string mandateId
        string label
        string statusProzess
        json perimeter
        json baulinie
        json parzellen
        json dokumente
        json kontextInformationen
    }
    
    Parzelle {
        string id PK
        string mandateId
        string label
        string kontextGemeinde FK
        json perimeter
        json baulinie
        string bauzone
        float az
        float bz
        json dokumente
        json kontextInformationen
    }
    
    Land {
        string id PK
        string mandateId
        string label
        string abk
        json dokumente
        json kontextInformationen
    }
    
    Kanton {
        string id PK
        string mandateId
        string label
        string id_land FK
        string abk
        json dokumente
        json kontextInformationen
    }
    
    Gemeinde {
        string id PK
        string mandateId
        string label
        string id_kanton FK
        string plz
        json dokumente
        json kontextInformationen
    }
    
    Dokument {
        string id PK
        string mandateId
        string label
        string versionsbezeichnung
        string dokumentTyp
        string dokumentReferenz
        string quelle
        string mimeType
        json kategorienTags
    }
    
    Kontext {
        string id PK
        string thema
        string inhalt
    }
    
    GeoPunkt {
        string koordinatensystem
        float x
        float y
        float z
        string referenz
    }
    
    GeoPolylinie {
        string id PK
        bool closed
        json punkte
    }
    
    Projekt ||--o{ Parzelle : contains
    Projekt ||--o{ Dokument : references
    Projekt ||--o{ Kontext : has
    
    Parzelle ||--o{ GeoPolylinie : contains
    Parzelle ||--o{ GeoPunkt : contains
    Parzelle }o--|| Gemeinde : located_in
    
    Land ||--o{ Kanton : contains
    Kanton ||--o{ Gemeinde : contains
    
    Land ||--o{ Dokument : has
    Land ||--o{ Kontext : has
    Kanton ||--o{ Dokument : has
    Kanton ||--o{ Kontext : has
    Gemeinde ||--o{ Dokument : has
    Gemeinde ||--o{ Kontext : has
    
    GeoPolylinie ||--o{ GeoPunkt : contains

datamodelChat.py

erDiagram
    ChatWorkflow {
        string id PK
        string mandateId
        string status
        string name
        int currentRound
        int currentTask
        int currentAction
        int totalTasks
        int totalActions
        float lastActivity
        float startedAt
        string workflowMode
        int maxSteps
        json logs
        json messages
        json stats
        json tasks
    }
    
    ChatMessage {
        string id PK
        string workflowId FK
        string parentMessageId FK
        string message
        string summary
        string role
        string status
        int sequenceNr
        float publishedAt
        bool success
        string actionId
        json documents
    }
    
    ChatLog {
        string id PK
        string workflowId FK
        string message
        string type
        float timestamp
        string status
        float progress
        json performance
    }
    
    ChatStat {
        string id PK
        string workflowId FK
        float processingTime
        int bytesSent
        int bytesReceived
        int errorCount
        string process
        string engine
        float priceUsd
    }
    
    ChatDocument {
        string id PK
        string messageId FK
        string fileId FK
        string fileName
        int fileSize
        string mimeType
        int roundNumber
        int taskNumber
        int actionNumber
        string actionId
    }
    
    TaskPlan {
        string overview
        json tasks
        string userMessage
    }
    
    TaskItem {
        string id PK
        string workflowId FK
        string userInput
        string status
        string error
        float startedAt
        float finishedAt
        json actionList
        int retryCount
        int retryMax
        bool rollbackOnFailure
        json dependencies
        string feedback
        float processingTime
        json resultLabels
    }
    
    TaskStep {
        string id PK
        string objective
        json dependencies
        json successCriteria
        string estimatedComplexity
        string userMessage
        string dataType
        json expectedFormats
        json qualityRequirements
    }
    
    ActionItem {
        string id PK
        string execMethod
        string execAction
        json execParameters
        string execResultLabel
        json expectedDocumentFormats
        string userMessage
        string status
        string error
        int retryCount
        int retryMax
        float processingTime
        float timestamp
        string result
    }
    
    ActionResult {
        bool success
        string error
        json documents
        string resultLabel
    }
    
    AutomationDefinition {
        string id PK
        string mandateId
        string label
        string schedule
        string template
        json placeholders
        bool active
        string eventId
        string status
        json executionLogs
    }
    
    ChatWorkflow ||--o{ ChatMessage : contains
    ChatWorkflow ||--o{ ChatLog : contains
    ChatWorkflow ||--o{ ChatStat : contains
    ChatWorkflow ||--o{ TaskPlan : has
    ChatWorkflow ||--o{ AutomationDefinition : defines
    
    ChatMessage ||--o{ ChatDocument : references
    TaskPlan ||--o{ TaskStep : contains
    TaskItem ||--o{ ActionItem : contains
    ActionItem ||--o{ ActionResult : produces

datamodelAi.py

erDiagram
    AiModel {
        string name PK
        string displayName
        string connectorType
        string apiUrl
        float temperature
        int maxTokens
        int contextLength
        float costPer1kTokensInput
        float costPer1kTokensOutput
        int speedRating
        int qualityRating
        string priority
        string processingMode
        json operationTypes
        int minContextLength
        bool isAvailable
        string version
        string lastUpdated
    }
    
    OperationTypeRating {
        string operationType
        int rating
    }
    
    AiCallOptions {
        string operationType
        string priority
        bool compressPrompt
        bool compressContext
        bool processDocumentsIndividually
        float maxCost
        int maxProcessingTime
        string processingMode
        string resultFormat
        float safetyMargin
        float temperature
        int maxParts
    }
    
    AiCallRequest {
        string prompt
        string context
        json options
        json contentParts
    }
    
    AiCallResponse {
        string content
        string modelName
        float priceUsd
        float processingTime
        int bytesSent
        int bytesReceived
        int errorCount
    }
    
    AiModelCall {
        json messages
        json model
        json options
    }
    
    AiModelResponse {
        string content
        bool success
        string error
        string modelId
        float processingTime
        json tokensUsed
        json metadata
    }
    
    AiModel ||--o{ OperationTypeRating : has
    AiCallRequest ||--|| AiCallOptions : uses
    AiCallRequest ||--|| AiCallResponse : produces
    AiCallRequest }o--|| AiModel : uses
    AiModelCall }o--|| AiModel : uses
    AiModelCall ||--|| AiCallOptions : uses
    AiModelCall ||--|| AiModelResponse : produces

datamodelUam.py

erDiagram
    Mandate {
        string id PK
        string name
        string language
        bool enabled
    }
    
    User {
        string id PK
        string username
        string email
        string fullName
        string language
        bool enabled
        string privilege
        string authenticationAuthority
        string mandateId FK
    }
    
    UserConnection {
        string id PK
        string userId FK
        string authority
        string externalId
        string externalUsername
        string externalEmail
        string status
        float connectedAt
        float lastChecked
        float expiresAt
        string tokenStatus
        float tokenExpiresAt
    }
    
    UserInDB {
        string id PK
        string username
        string email
        string fullName
        string language
        bool enabled
        string privilege
        string authenticationAuthority
        string mandateId FK
        string hashedPassword
    }
    
    Mandate ||--o{ User : contains
    User ||--o{ UserConnection : has
    User ||--|| UserInDB : extends

datamodelSecurity.py

erDiagram
    Token {
        string id PK
        string userId FK
        string authority
        string connectionId FK
        string tokenAccess
        string tokenType
        float expiresAt
        string tokenRefresh
        float createdAt
        string status
        float revokedAt
        string revokedBy
        string reason
        string sessionId
        string mandateId FK
    }
    
    AuthEvent {
        string id PK
        string userId FK
        string eventType
        float timestamp
        string ipAddress
        string userAgent
        bool success
        string details
    }
    
    Token ||--o{ AuthEvent : generates

datamodelFiles.py

erDiagram
    FileItem {
        string id PK
        string mandateId FK
        string fileName
        string mimeType
        string fileHash
        int fileSize
        float creationDate
    }
    
    FilePreview {
        string content
        string mimeType
        string fileName
        bool isText
        string encoding
        int size
    }
    
    FileData {
        string id PK
        string data
        bool base64Encoded
    }
    
    FileItem ||--|| FilePreview : generates
    FileItem ||--|| FileData : contains

datamodelDocument.py

erDiagram
    StructuredDocument {
        json metadata
        json sections
        string summary
        json tags
    }
    
    DocumentMetadata {
        string title
        string author
        datetime createdAt
        json sourceDocuments
        string extractionMethod
        string version
    }
    
    DocumentSection {
        string id PK
        string title
        string contentType
        json elements
        int order
        json metadata
    }
    
    Paragraph {
        string text
        json formatting
        json metadata
    }
    
    Heading {
        string text
        int level
        json metadata
    }
    
    CodeBlock {
        string code
        string language
        json metadata
    }
    
    Image {
        string data
        string altText
        string caption
        json metadata
    }
    
    BulletList {
        json items
        string listType
        json metadata
    }
    
    ListItem {
        string text
        json subitems
        json metadata
    }
    
    TableData {
        json headers
        json rows
        string caption
        json metadata
    }
    
    StructuredDocument ||--|| DocumentMetadata : has
    StructuredDocument ||--o{ DocumentSection : contains
    DocumentSection ||--o{ Paragraph : can_contain
    DocumentSection ||--o{ Heading : can_contain
    DocumentSection ||--o{ CodeBlock : can_contain
    DocumentSection ||--o{ Image : can_contain
    DocumentSection ||--o{ BulletList : can_contain
    DocumentSection ||--o{ TableData : can_contain
    BulletList ||--o{ ListItem : contains
    ListItem ||--o{ ListItem : contains

datamodelExtraction.py

erDiagram
    ContentExtracted {
        string id PK
        json parts
        json summary
    }
    
    ContentPart {
        string id PK
        string parentId FK
        string label
        string typeGroup
        string mimeType
        string data
        json metadata
    }
    
    ExtractionOptions {
        string prompt
        string operationType
        bool processDocumentsIndividually
        int imageMaxPixels
        int imageQuality
        json mergeStrategy
        bool chunkAllowed
        int maxSize
        int textChunkSize
        int imageChunkSize
        bool enableParallelProcessing
        int maxConcurrentChunks
    }
    
    MergeStrategy {
        string groupBy
        string orderBy
        string mergeType
        int maxSize
        json textMerge
        json tableMerge
        json structureMerge
        json aiResultMerge
        bool preserveChunks
        string chunkSeparator
        bool preserveMetadata
        json metadataFields
        string onError
        bool validateContent
        bool useIntelligentMerging
        string prompt
        json capabilities
    }
    
    PartResult {
        json originalPart
        string aiResult
        int partIndex
        string documentId
        float processingTime
        json metadata
    }
    
    ChunkResult {
        json originalChunk
        string aiResult
        int chunkIndex
        string documentId
        float processingTime
        json metadata
    }
    
    ContentExtracted ||--o{ ContentPart : contains
    ContentPart ||--o{ ContentPart : parent_of
    ContentExtracted ||--|| ExtractionOptions : uses
    ExtractionOptions ||--|| MergeStrategy : uses
    ContentPart ||--|| PartResult : produces
    ContentPart ||--|| ChunkResult : produces

datamodelPagination.py

erDiagram
    PaginationParams {
        int page
        int pageSize
        string sortBy
        string sortOrder
    }
    
    PaginationRequest {
        json params
        json sortFields
    }
    
    SortField {
        string field
        string order
    }
    
    PaginatedResult {
        json items
        json metadata
        json params
    }
    
    PaginationMetadata {
        int page
        int pageSize
        int totalItems
        int totalPages
        bool hasNext
        bool hasPrevious
    }
    
    PaginationRequest ||--|| PaginationParams : uses
    PaginationRequest ||--o{ SortField : contains
    PaginatedResult ||--|| PaginationMetadata : has
    PaginatedResult ||--o{ PaginationParams : uses

datamodelVoice.py

erDiagram
    VoiceSettings {
        string id PK
        string userId FK
        string language
        string voice
        json settings
    }

datamodelTickets.py

erDiagram
    TicketFieldAttribute {
        string fieldName PK
        string fieldType
        json fieldConfig
    }

datamodelNeutralizer.py

erDiagram
    DataNeutraliserConfig {
        string id PK
        string mandateId FK
        string name
        bool enabled
        json attributes
    }
    
    DataNeutralizerAttributes {
        string fieldName PK
        string neutralizationType
        json options
    }
    
    DataNeutraliserConfig ||--o{ DataNeutralizerAttributes : contains

datamodelUtils.py

erDiagram
    Prompt {
        string id PK
        string name
        string content
        json metadata
    }

datamodelTools.py

erDiagram
    CountryCodes {
        string ISO2Code PK
        string tavilyName
        string perplexityName
    }

Note: CountryCodes is a utility class (not a Pydantic BaseModel) that provides static methods for country code mapping. It contains a mapping dictionary but is not persisted to the database.

datamodelJson.py

No database models - contains JSON template constants and supported section types.

Interfaces Component

Overview

The Interfaces component provides a clean abstraction layer for data access operations. Interfaces handle CRUD operations, user context management, access control, and integration with database connectors and external systems.

Objects vs Access Files

Interfaces are split into two file types:

Objects Files (interface*Objects.py)

Purpose: Business logic and CRUD operations for data entities.

Responsibilities:

  • CRUD operations (Create, Read, Update, Delete)
  • Data validation and transformation
  • Business rule enforcement
  • Database/external system communication
  • User context management
  • Pagination and filtering

Pattern: Each Objects file contains methods for manipulating domain entities (e.g., createProjekt(), getWorkflow(), updateUser()).

Access Files (interface*Access.py)

Purpose: Permission checking and data filtering based on user privileges.

Responsibilities:

  • User privilege validation
  • Mandate-based filtering
  • Record ownership checking
  • Access control attribute generation (_hideView, _hideEdit, _hideDelete)
  • Permission decision logic

Pattern: Access files contain two main methods:

  • uam(): Unified Access Management - filters recordsets and adds access control flags
  • canModify(): Checks if user can create/update/delete records

Relationship:

graph TB
    Objects[Objects File] --> Access[Access File]
    Access --> UAM[uam Method]
    Access --> CanModify[canModify Method]
    
    Objects --> CRUD[CRUD Operations]
    CRUD --> AccessCheck{Check Access}
    AccessCheck -->|Read| UAM
    AccessCheck -->|Write| CanModify
    
    UAM --> Filter[Filter Records]
    UAM --> Flags[Add Access Flags]
    
    CanModify --> Permission{Permission?}
    Permission -->|Yes| Allow[Allow Operation]
    Permission -->|No| Deny[Deny Operation]

Interface Types

Interfaces are categorized into two types based on their data source:

Database Interfaces

Why Database Connectors?: These interfaces manage persistent data stored in PostgreSQL databases. They use database connectors to:

  • Store structured data with relationships
  • Ensure data consistency and integrity
  • Provide ACID transactions
  • Support complex queries and filtering
  • Enable mandate-based data isolation

Characteristics:

  • Use DatabaseConnector for PostgreSQL access
  • Implement Access classes for permission control
  • Support pagination and sorting
  • Apply mandate-based filtering automatically
  • Track record ownership (_createdBy)

External System Interfaces

Why External Connectors?: These interfaces integrate with external APIs and services. They use connectors to:

  • Communicate with third-party systems
  • Transform data between formats
  • Handle API authentication and rate limiting
  • Provide abstraction over external service complexity

Characteristics:

  • Use specialized connectors (e.g., ConnectorGoogleSpeech, ConnectorTicketJira)
  • May not require user context (system-level operations)
  • Focus on data transformation and synchronization
  • Handle external API errors and retries

Real Estate Interface (interfaceDbRealEstateObjects.py)

Type: Database Interface
Database: PostgreSQL (Real Estate database)
Access Control: interfaceDbRealEstateAccess.py → RealEstateAccess

Purpose: Manages real estate domain data including projects, parcels, administrative entities, and geographic information.

Why Database Connector: Real estate data requires persistent storage with complex relationships (projects → parcels → administrative units), geographic data (polygons, points), and mandate-based isolation for multi-tenant scenarios.

CRUD Operations:

graph TB
    subgraph "Projekt Operations"
        PCreate[createProjekt]
        PGet[getProjekt]
        PGetAll[getProjekte]
        PUpdate[updateProjekt]
        PDelete[deleteProjekt]
    end
    
    subgraph "Parzelle Operations"
        ParCreate[createParzelle]
        ParGet[getParzelle]
        ParGetAll[getParzellen]
        ParUpdate[updateParzelle]
        ParDelete[deleteParzelle]
    end
    
    subgraph "Dokument Operations"
        DocCreate[createDokument]
        DocGet[getDokument]
        DocGetAll[getDokumente]
        DocUpdate[updateDokument]
        DocDelete[deleteDokument]
    end
    
    subgraph "Administrative Hierarchy"
        GemCreate[createGemeinde]
        GemGet[getGemeinde]
        GemGetAll[getGemeinden]
        GemUpdate[updateGemeinde]
        GemDelete[deleteGemeinde]
        
        KanCreate[createKanton]
        KanGet[getKanton]
        KanGetAll[getKantone]
        KanUpdate[updateKanton]
        KanDelete[deleteKanton]
        
        LanCreate[createLand]
        LanGet[getLand]
        LanGetAll[getLaender]
        LanUpdate[updateLand]
        LanDelete[deleteLand]
    end
    
    subgraph "Kontext Operations"
        KonCreate[createKontext]
        KonGet[getKontext]
        KonGetAll[getKontexte]
        KonUpdate[updateKontext]
        KonDelete[deleteKontext]
    end

Complete CRUD List:

Projekt:

  • createProjekt(projekt: Projekt) → Projekt
  • getProjekt(projektId: str) → Optional[Projekt]
  • getProjekte(recordFilter: Optional[Dict]) → List[Projekt]
  • updateProjekt(projektId: str, updateData: Dict) → Optional[Projekt]
  • deleteProjekt(projektId: str) → bool

Parzelle:

  • createParzelle(parzelle: Parzelle) → Parzelle
  • getParzelle(parzelleId: str) → Optional[Parzelle]
  • getParzellen(recordFilter: Optional[Dict]) → List[Parzelle]
  • updateParzelle(parzelleId: str, updateData: Dict) → Optional[Parzelle]
  • deleteParzelle(parzelleId: str) → bool

Dokument:

  • createDokument(dokument: Dokument) → Dokument
  • getDokument(dokumentId: str) → Optional[Dokument]
  • getDokumente(recordFilter: Optional[Dict]) → List[Dokument]
  • updateDokument(dokumentId: str, updateData: Dict) → Optional[Dokument]
  • deleteDokument(dokumentId: str) → bool

Gemeinde:

  • createGemeinde(gemeinde: Gemeinde) → Gemeinde
  • getGemeinde(gemeindeId: str) → Optional[Gemeinde]
  • getGemeinden(recordFilter: Optional[Dict]) → List[Gemeinde]
  • updateGemeinde(gemeindeId: str, updateData: Dict) → Optional[Gemeinde]
  • deleteGemeinde(gemeindeId: str) → bool

Kanton:

  • createKanton(kanton: Kanton) → Kanton
  • getKanton(kantonId: str) → Optional[Kanton]
  • getKantone(recordFilter: Optional[Dict]) → List[Kanton]
  • updateKanton(kantonId: str, updateData: Dict) → Optional[Kanton]
  • deleteKanton(kantonId: str) → bool

Land:

  • createLand(land: Land) → Land
  • getLand(landId: str) → Optional[Land]
  • getLaender(recordFilter: Optional[Dict]) → List[Land]
  • updateLand(landId: str, updateData: Dict) → Optional[Land]
  • deleteLand(landId: str) → bool

Kontext:

  • createKontext(kontext: Kontext) → Kontext
  • getKontext(kontextId: str) → Optional[Kontext]
  • getKontexte(recordFilter: Optional[Dict]) → List[Kontext]
  • updateKontext(kontextId: str, updateData: Dict) → Optional[Kontext]
  • deleteKontext(kontextId: str) → bool

Access Control Flow:

graph TB
    Request[CRUD Request] --> Objects[RealEstateObjects]
    Objects --> Access[RealEstateAccess]
    
    Access --> CheckPriv[Check Privilege]
    CheckPriv -->|SYSADMIN| AllData[All Records]
    CheckPriv -->|ADMIN| MandateData[Mandate Records]
    CheckPriv -->|USER| OwnData[Own Records]
    
    AllData --> UAM[uam Method]
    MandateData --> UAM
    OwnData --> UAM
    
    UAM --> Filter[Filter by mandateId]
    UAM --> CheckOwn[Check _createdBy]
    UAM --> AddFlags[Add _hideView/_hideEdit/_hideDelete]
    
    Filter --> Return[Return Filtered Data]
    CheckOwn --> Return
    AddFlags --> Return

Key Features:

  • Geographic data support (GeoPolylinie, GeoPunkt)
  • Multi-level administrative hierarchy (Land → Kanton → Gemeinde)
  • Location name resolution (converts names to IDs for filtering)
  • Document versioning and management
  • Context information for projects and administrative units

Chat Interface (interfaceDbChatObjects.py)

Type: Database Interface
Database: PostgreSQL (Chat database)
Access Control: interfaceDbChatAccess.py → ChatAccess

Purpose: Manages chat workflows, messages, logs, statistics, and automation definitions for AI-powered conversation workflows.

Why Database Connector: Chat workflows require persistent storage for conversation history, workflow state, performance metrics, and automation configurations. Data must be queryable, filterable, and mandate-isolated.

CRUD Operations:

graph TB
    subgraph "ChatWorkflow Operations"
        WfGet[getWorkflows - with pagination]
        WfGetOne[getWorkflow - by ID]
        WfCreate[createWorkflow]
        WfUpdate[updateWorkflow]
        WfDelete[deleteWorkflow - cascade]
    end
    
    subgraph "ChatMessage Operations"
        MsgGet[getMessages - by workflowId, pagination]
        MsgCreate[createMessage]
        MsgUpdate[updateMessage]
        MsgDelete[deleteMessage]
        MsgDeleteFile[deleteFileFromMessage]
    end
    
    subgraph "ChatDocument Operations"
        DocGet[getDocuments - by messageId]
        DocCreate[createDocument]
    end
    
    subgraph "ChatLog Operations"
        LogGet[getLogs - by workflowId, pagination]
        LogCreate[createLog]
    end
    
    subgraph "ChatStat Operations"
        StatGet[getStats - by workflowId]
        StatCreate[createStat]
    end
    
    subgraph "AutomationDefinition Operations"
        AutoGet[getAllAutomationDefinitions - pagination]
        AutoGetOne[getAutomationDefinition - by ID]
        AutoCreate[createAutomationDefinition]
        AutoUpdate[updateAutomationDefinition]
        AutoDelete[deleteAutomationDefinition]
    end
    
    subgraph "Utility Operations"
        Unified[getUnifiedChatData - workflow snapshot]
    end

Complete CRUD List:

ChatWorkflow:

  • getWorkflows(pagination: Optional[PaginationParams]) → Union[List[Dict], PaginatedResult]
  • getWorkflow(workflowId: str) → Optional[ChatWorkflow]
  • createWorkflow(workflowData: Dict) → ChatWorkflow
  • updateWorkflow(workflowId: str, workflowData: Dict) → ChatWorkflow
  • deleteWorkflow(workflowId: str) → bool (cascades to messages, logs, stats)

ChatMessage:

  • getMessages(workflowId: str, pagination: Optional[PaginationParams]) → Union[List[ChatMessage], PaginatedResult]
  • createMessage(messageData: Dict) → ChatMessage
  • updateMessage(messageId: str, messageData: Dict) → Dict
  • deleteMessage(workflowId: str, messageId: str) → bool
  • deleteFileFromMessage(workflowId: str, messageId: str, fileId: str) → bool

ChatDocument:

  • getDocuments(messageId: str) → List[ChatDocument]
  • createDocument(documentData: Dict) → ChatDocument

ChatLog:

  • getLogs(workflowId: str, pagination: Optional[PaginationParams]) → Union[List[ChatLog], PaginatedResult]
  • createLog(logData: Dict) → ChatLog

ChatStat:

  • getStats(workflowId: str) → List[ChatStat]
  • createStat(statData: Dict) → ChatStat

AutomationDefinition:

  • getAllAutomationDefinitions(pagination: Optional[PaginationParams]) → Union[List[Dict], PaginatedResult]
  • getAutomationDefinition(automationId: str) → Optional[Dict]
  • createAutomationDefinition(automationData: Dict) → Dict
  • updateAutomationDefinition(automationId: str, automationData: Dict) → Dict
  • deleteAutomationDefinition(automationId: str) → bool

Utility Methods:

  • getUnifiedChatData(workflowId: str, afterTimestamp: Optional[float]) → Dict (returns workflow snapshot with messages, logs, stats)

Access Control Flow:

graph TB
    Request[CRUD Request] --> Objects[ChatObjects]
    Objects --> Access[ChatAccess]
    
    Access --> CheckPriv[Check Privilege]
    CheckPriv -->|SYSADMIN| AllWorkflows[All Workflows]
    CheckPriv -->|ADMIN| MandateWorkflows[Mandate Workflows]
    CheckPriv -->|USER| OwnWorkflows[Own Workflows]
    
    AllWorkflows --> UAM[uam Method]
    MandateWorkflows --> UAM
    OwnWorkflows --> UAM
    
    UAM --> FilterWorkflow[Filter by workflowId mandate]
    UAM --> CheckOwn[Check _createdBy]
    UAM --> AddFlags[Add access flags]
    
    FilterWorkflow --> CheckChild[Check Child Access]
    CheckChild -->|Message| CheckWorkflow[Check workflow ownership]
    CheckChild -->|Log| CheckWorkflow
    CheckChild -->|Stat| CheckWorkflow
    
    CheckWorkflow --> Return[Return Filtered Data]
    CheckOwn --> Return
    AddFlags --> Return

Key Features:

  • Multi-round workflow support with state tracking
  • Normalized data model (workflows, messages, logs, stats in separate tables)
  • Cascade delete (deleting workflow removes all related data)
  • Pagination support for large datasets
  • Unified data retrieval for workflow snapshots
  • Automation workflow definitions
  • Document attachment management

App Interface (interfaceDbAppObjects.py)

Type: Database Interface
Database: PostgreSQL (App database)
Access Control: interfaceDbAppAccess.py → AppAccess

Purpose: Manages users, mandates, authentication tokens, and application-level configuration.

Why Database Connector: User accounts, mandates, and authentication data require secure, persistent storage with strict access control. This is the foundation for all other interfaces' user context.

CRUD Operations:

graph TB
    subgraph "User Operations"
        UserGet[getUsersByMandate - pagination]
        UserGetByUsername[getUserByUsername]
        UserGetOne[getUser - by ID]
        UserCreate[createUser - with password hash]
        UserUpdate[updateUser]
        UserDelete[deleteUser]
    end
    
    subgraph "UserConnection Operations"
        ConnGet[getUserConnections - by userId]
        ConnGetToken[getConnectionToken - by connectionId]
    end
    
    subgraph "Mandate Operations"
        ManGet[getAllMandates - pagination]
        ManGetOne[getMandate - by ID]
        ManCreate[createMandate]
        ManUpdate[updateMandate]
        ManDelete[deleteMandate]
    end
    
    subgraph "Neutralization Config"
        NeuGet[getNeutralizationConfig]
        NeuCreate[createOrUpdateNeutralizationConfig]
        NeuGetAttrs[getNeutralizationAttributes]
        NeuDeleteAttrs[deleteNeutralizationAttributes]
    end
    
    subgraph "Initialization"
        InitRoot[getRootInterface - system init]
        InitRecords[_initRootMandate, _initAdminUser, _initEventUser]
    end

Complete CRUD List:

User:

  • getUsersByMandate(mandateId: str, pagination: Optional[PaginationParams]) → Union[List[User], PaginatedResult]
  • getUserByUsername(username: str) → Optional[User]
  • getUser(userId: str) → Optional[User]
  • createUser(userData: Dict, password: Optional[str]) → User (hashes password with Argon2)
  • updateUser(userId: str, updateData: Union[Dict, User]) → User
  • deleteUser(userId: str) → bool

UserConnection:

  • getUserConnections(userId: str) → List[UserConnection]
  • getConnectionToken(connectionId: str) → Optional[Token]

Mandate:

  • getAllMandates(pagination: Optional[PaginationParams]) → Union[List[Mandate], PaginatedResult]
  • getMandate(mandateId: str) → Optional[Mandate]
  • createMandate(name: str, language: str) → Mandate
  • updateMandate(mandateId: str, updateData: Dict) → Mandate
  • deleteMandate(mandateId: str) → bool

DataNeutraliserConfig:

  • getNeutralizationConfig() → Optional[DataNeutraliserConfig]
  • createOrUpdateNeutralizationConfig(configData: Dict) → DataNeutraliserConfig
  • getNeutralizationAttributes(file_id: str) → List[Dict]
  • deleteNeutralizationAttributes(file_id: str) → bool

Special Methods:

  • getRootInterface() → AppObjects (creates interface with system admin privileges for initialization)
  • getInitialId(model_class: type) → Optional[str] (gets first record ID for a model)

Access Control Flow:

graph TB
    Request[CRUD Request] --> Objects[AppObjects]
    Objects --> Access[AppAccess]
    
    Access --> CheckPriv[Check Privilege]
    CheckPriv -->|SYSADMIN| FullAccess[Full System Access]
    CheckPriv -->|ADMIN| MandateAccess[Mandate Access Only]
    CheckPriv -->|USER| SelfAccess[Own User Record Only]
    
    FullAccess --> UserOps[User Operations]
    MandateAccess --> UserOps
    SelfAccess --> UserOps
    
    UserOps --> CheckOwn[Check Ownership]
    CheckOwn -->|User CRUD| SYSADMINOnly{SYSADMIN?}
    CheckOwn -->|Mandate CRUD| SYSADMINOnly
    CheckOwn -->|Own Profile| Allow[Allow]
    
    SYSADMINOnly -->|Yes| Allow
    SYSADMINOnly -->|No| Deny[Deny]

Key Features:

  • Password hashing with Argon2
  • Multi-provider authentication support (local, external)
  • System initialization (Root mandate, Admin user, Event user)
  • Mandate-based user isolation
  • Token management for external connections
  • Data neutralization configuration

Component Interface (interfaceDbComponentObjects.py)

Type: Database Interface
Database: PostgreSQL (Component/Management database)
Access Control: interfaceDbComponentAccess.py → ComponentAccess

Purpose: Manages component-level data including files, prompts, and voice settings used across the application.

Why Database Connector: Files, prompts, and voice settings require persistent storage with metadata, ownership tracking, and mandate isolation. Files need binary storage with preview generation.

CRUD Operations:

graph TB
    subgraph "File Operations"
        FileGetAll[getAllFiles - pagination]
        FileGet[getFile - by ID]
        FileCreate[createFile - name, mimeType, content]
        FileUpdate[updateFile]
        FileDelete[deleteFile]
        FileGetData[getFileData - binary content]
        FileGetContent[getFileContent - preview]
        FileCreateData[createFileData - store binary]
    end
    
    subgraph "Prompt Operations"
        PromptGetAll[getAllPrompts - pagination]
        PromptGet[getPrompt - by ID]
        PromptCreate[createPrompt]
        PromptUpdate[updatePrompt]
        PromptDelete[deletePrompt]
    end
    
    subgraph "VoiceSettings Operations"
        VoiceGet[getVoiceSettings - by userId]
        VoiceCreate[createVoiceSettings]
        VoiceUpdate[updateVoiceSettings]
        VoiceDelete[deleteVoiceSettings]
        VoiceGetOrCreate[getOrCreateVoiceSettings]
    end
    
    subgraph "Utilities"
        MimeType[getMimeType - from fileName]
    end

Complete CRUD List:

FileItem:

  • getAllFiles(pagination: Optional[PaginationParams]) → Union[List[FileItem], PaginatedResult]
  • getFile(fileId: str) → Optional[FileItem]
  • createFile(name: str, mimeType: str, content: bytes) → FileItem (creates FileItem and FileData)
  • updateFile(fileId: str, updateData: Dict) → Dict
  • deleteFile(fileId: str) → bool (deletes FileItem and FileData)
  • getFileData(fileId: str) → Optional[bytes] (raw binary content)
  • getFileContent(fileId: str) → Optional[FilePreview] (generates preview)
  • createFileData(fileId: str, data: bytes) → bool (stores binary data)

Prompt:

  • getAllPrompts(pagination: Optional[PaginationParams]) → Union[List[Prompt], PaginatedResult]
  • getPrompt(promptId: str) → Optional[Prompt]
  • createPrompt(promptData: Dict) → Dict
  • updatePrompt(promptId: str, updateData: Dict) → Dict
  • deletePrompt(promptId: str) → bool

VoiceSettings:

  • getVoiceSettings(userId: Optional[str]) → Optional[VoiceSettings]
  • createVoiceSettings(settingsData: Dict) → Dict
  • updateVoiceSettings(userId: str, updateData: Dict) → Dict
  • deleteVoiceSettings(userId: str) → bool
  • getOrCreateVoiceSettings(userId: Optional[str]) → VoiceSettings

Utility Methods:

  • getMimeType(fileName: str) → str (detects MIME type from extension)

Access Control Flow:

graph TB
    Request[CRUD Request] --> Objects[ComponentObjects]
    Objects --> Access[ComponentAccess]
    
    Access --> CheckPriv[Check Privilege]
    CheckPriv -->|SYSADMIN| AllFiles[All Files]
    CheckPriv -->|ADMIN| MandateFiles[Mandate Files]
    CheckPriv -->|USER| OwnFiles[Own Files]
    
    AllFiles --> UAM[uam Method]
    MandateFiles --> UAM
    OwnFiles --> UAM
    
    UAM --> FilterMandate[Filter by mandateId]
    UAM --> CheckOwn[Check _createdBy]
    UAM --> AddFlags[Add access flags]
    
    FilterMandate --> Return[Return Filtered Data]
    CheckOwn --> Return
    AddFlags --> Return

Key Features:

  • Binary file storage with metadata
  • Automatic preview generation (text, images, etc.)
  • MIME type detection
  • Prompt template management with initialization
  • Voice settings per user
  • File hash calculation for deduplication

AI Interface (interfaceAiObjects.py)

Type: External System Interface
Connectors: Dynamic discovery via modelRegistry
Access Control: None (system-level operations)

Purpose: Provides centralized AI operations with dynamic model discovery, automatic model selection, and failover handling.

Why External Connector: AI operations require communication with external APIs (OpenAI, Anthropic, Perplexity, Tavily) and internal AI services. The interface abstracts model selection, handles API calls, manages failover, and tracks costs.

Operations:

graph TB
    subgraph "AI Call Operations"
        Call[call - main entry point]
        CallText[call with text/context]
        CallParts[call with content parts]
    end
    
    subgraph "Model Information"
        ModelInfo[getModelInfo - by displayName]
        ModelsByTag[getModelsByTag - filter by tag]
    end
    
    subgraph "Internal Processing"
        SelectModel[_selectModel - dynamic selection]
        ProcessPart[_processContentPartWithFallback]
        MergeResults[_mergePartResults]
        CallWithModel[_callWithModel - execute API call]
    end

Complete Operations List:

AI Calls:

  • call(request: AiCallRequest, progressCallback=None) → AiCallResponse (main entry point, handles text/context or content parts)
  • getModelInfo(displayName: str) → Dict[str, Any] (get model metadata)
  • getModelsByTag(tag: str) → List[str] (filter models by tag)

Internal Methods (used by call):

  • _selectModel(prompt: str, context: str, options: AiCallOptions) → str (selects best model)
  • _callWithTextContext(request: AiCallRequest) → AiCallResponse (handles traditional text/context calls)
  • _callWithContentParts(request: AiCallRequest, progressCallback) → AiCallResponse (handles content parts with chunking)
  • _processContentPartWithFallback(...) → AiCallResponse (processes single part with failover)
  • _callWithModel(model, prompt, context, options) → AiCallResponse (executes actual API call)

Model Selection and Failover Flow:

graph TB
    Request[AI Call Request] --> CheckType{Request Type?}
    
    CheckType -->|Text/Context| TextPath[Text/Context Path]
    CheckType -->|Content Parts| PartsPath[Content Parts Path]
    
    TextPath --> GetFailover[Get Failover Model List]
    PartsPath --> GetFailover
    
    GetFailover --> SelectModel[Select Best Model]
    SelectModel --> FilterOp[Filter by Operation Type]
    FilterOp --> RateCap[Rate by Capabilities]
    RateCap --> ApplyPriority[Apply Priority Rules]
    ApplyPriority --> TryModel[Try Model Call]
    
    TryModel --> Success{Success?}
    Success -->|Yes| Return[Return Response]
    Success -->|No| NextModel{More Models?}
    
    NextModel -->|Yes| TryModel
    NextModel -->|No| Error[Return Error Response]
    
    PartsPath --> ProcessEach[Process Each Part]
    ProcessEach --> Chunk[Model-Aware Chunking]
    Chunk --> TryModel
    ProcessEach --> Merge[Merge Results]
    Merge --> Return

Supported External Systems:

  • OpenAI: GPT models via aicorePluginOpenai
  • Anthropic: Claude models via aicorePluginAnthropic
  • Perplexity: Search-enabled models via aicorePluginPerplexity
  • Tavily: Web search API via aicorePluginTavily
  • Internal: Custom models via aicorePluginInternal

Key Features:

  • Dynamic model discovery (auto-registers available connectors)
  • Operation type-based model selection (e.g., IMAGE_ANALYSE, TEXT_GENERATION)
  • Automatic failover (tries multiple models on failure)
  • Model-aware chunking (respects model context limits)
  • Content part processing (handles images, text, tables)
  • Cost tracking (calculates USD cost per call)
  • Progress callbacks for long-running operations

Ticket Interface (interfaceTicketObjects.py)

Type: External System Interface
Connectors: ConnectorTicketJira, ConnectorTicketClickup
Access Control: Connector-level (API credentials)

Purpose: Synchronizes data with external ticket systems (Jira, ClickUp) by transforming tickets to/from list format for Excel-like operations.

Why External Connector: Ticket systems are external services with their own APIs. The interface provides bidirectional synchronization, field mapping, and data transformation between the application's list format and ticket system formats.

Operations:

graph TB
    subgraph "Factory Method"
        Factory[createTicketInterfaceByType - connectorType, params]
    end
    
    subgraph "Export Operations"
        Export[exportTicketsAsList - read from external]
    end
    
    subgraph "Import Operations"
        Import[importListToTickets - write to external]
    end
    
    subgraph "Internal Transformation"
        Transform[_transformTicketRecords - field mapping]
        Extract[_extractFieldValue - path-based extraction]
        FormatDate[_formatDateForExcel - date formatting]
        FilterEmpty[_filterEmptyRecords - validation]
    end

Complete Operations List:

Factory:

  • createTicketInterfaceByType(taskSyncDefinition: dict, connectorType: str, connectorParams: dict) → TicketInterface (creates interface with appropriate connector)

Export:

  • exportTicketsAsList() → list[dict] (reads tickets from external system, transforms to list format)

Import:

  • importListToTickets(records: list[dict]) → None (transforms list format, writes to external system)

Internal Methods:

  • _transformTicketRecords(tasks: list[dict], includePut: bool) → list[dict] (transforms according to task_sync_definition)
  • _extractFieldValue(issue_data: dict, field_path: list[str], field_name: str) → Any (extracts value using path)
  • _formatDateForExcel(date_value: Any) → Optional[str] (formats dates for Excel compatibility)
  • _isDateField(field_name: str) → bool (detects date fields)
  • _filterEmptyRecords(records: list[dict]) → list[dict] (removes invalid records)

Synchronization Flow:

graph TB
    subgraph "Export Flow"
        ExportStart[exportTicketsAsList] --> ReadConn[Read from Connector]
        ReadConn --> GetTasks[Get Tasks from API]
        GetTasks --> Transform[Transform Fields]
        Transform --> MapFields[Map via task_sync_definition]
        MapFields --> FormatDates[Format Dates]
        FormatDates --> Filter[Filter Empty Records]
        Filter --> ReturnList[Return List Format]
    end
    
    subgraph "Import Flow"
        ImportStart[importListToTickets] --> ReceiveList[Receive List Format]
        ReceiveList --> ExtractFields[Extract Fields]
        ExtractFields --> MapToTicket[Map to Ticket Format]
        MapToTicket --> BuildUpdate[Build Update Payload]
        BuildUpdate --> WriteConn[Write via Connector]
        WriteConn --> UpdateAPI[Update External API]
    end
    
    subgraph "Field Mapping"
        SyncDef[task_sync_definition] --> Direction[Direction: get/put]
        SyncDef --> Path[Field Path: nested access]
        Direction --> Transform
        Path --> MapFields
        Path --> MapToTicket
    end

Supported External Systems:

  • Jira: Via ConnectorTicketJira (uses Jira REST API)
  • ClickUp: Via ConnectorTicketClickup (uses ClickUp API)

Key Features:

  • Dynamic connector selection (Jira or ClickUp)
  • Field mapping configuration (task_sync_definition maps fields bidirectionally)
  • Path-based field extraction (supports nested JSON structures)
  • Date format handling (converts various formats to Excel-compatible)
  • Bidirectional sync (export to list, import from list)
  • Empty record filtering (validates records have IDs)

Voice Interface (interfaceVoiceObjects.py)

Type: External System Interface
Connector: ConnectorGoogleSpeech
Access Control: User context for settings (stored in Component database)

Purpose: Provides speech-to-text, text-to-speech, and translation services using Google Cloud APIs.

Why External Connector: Voice operations require Google Cloud Speech-to-Text, Text-to-Speech, and Translation APIs. The interface abstracts API complexity, handles audio format conversion, and manages user voice settings (stored in database via Component interface).

Operations:

graph TB
    subgraph "Speech-to-Text Operations"
        STT[speechToText - audio to text]
        STTTrans[speechToTranslatedText - audio to translated text]
    end
    
    subgraph "Text-to-Speech Operations"
        TTS[textToSpeech - text to audio]
        TTSTrans[textToTranslatedSpeech - text to translated audio]
    end
    
    subgraph "Translation Operations"
        Trans[translateText - text translation]
    end
    
    subgraph "Voice Settings"
        GetSettings[getVoiceSettings - from Component DB]
        CreateSettings[createVoiceSettings - to Component DB]
        UpdateSettings[updateVoiceSettings - in Component DB]
        GetOrCreate[getOrCreateVoiceSettings]
    end
    
    subgraph "Metadata Operations"
        GetLangs[getAvailableLanguages - from Google API]
        GetVoices[getAvailableVoices - from Google API]
    end

Complete Operations List:

Speech-to-Text:

  • speechToText(audioContent: bytes, language: str, sampleRate: Optional[int], channels: Optional[int]) → Dict[str, Any] (converts audio to text)
  • speechToTranslatedText(audioContent: bytes, fromLanguage: str, toLanguage: str, sampleRate: Optional[int], channels: Optional[int]) → Dict[str, Any] (converts audio to translated text)

Text-to-Speech:

  • textToSpeech(text: str, language: Optional[str], voice: Optional[str]) → Dict[str, Any] (converts text to audio)
  • textToTranslatedSpeech(text: str, fromLanguage: str, toLanguage: str, voice: Optional[str]) → Dict[str, Any] (converts text to translated audio)

Translation:

  • translateText(text: str, sourceLanguage: str, targetLanguage: str) → Dict[str, Any] (translates text)

Voice Settings (delegates to Component interface):

  • getVoiceSettings(userId: str) → Optional[VoiceSettings]
  • createVoiceSettings(settingsData: Dict) → Optional[VoiceSettings]
  • updateVoiceSettings(userId: str, settingsData: Dict) → Optional[VoiceSettings]
  • getOrCreateVoiceSettings(userId: str) → Optional[VoiceSettings]

Metadata:

  • getAvailableLanguages() → Dict[str, Any] (lists supported languages from Google API)
  • getAvailableVoices(languageCode: Optional[str]) → Dict[str, Any] (lists available voices, optionally filtered by language)

Operation Flow:

graph TB
    Request[Voice Operation Request] --> Voice[VoiceObjects]
    Voice --> CheckSettings{Need Settings?}
    
    CheckSettings -->|Yes| GetSettings[Get from Component DB]
    CheckSettings -->|No| Connector[Get Connector]
    
    GetSettings --> Connector
    Connector --> GoogleAPI[Google Cloud API]
    
    GoogleAPI --> STT{Operation Type?}
    STT -->|Speech-to-Text| STTAPI[Speech-to-Text API]
    STT -->|Text-to-Speech| TTSAPI[Text-to-Speech API]
    STT -->|Translation| TransAPI[Translation API]
    
    STTAPI --> Process[Process Response]
    TTSAPI --> Process
    TransAPI --> Process
    
    Process --> Format[Format Response]
    Format --> Return[Return Result]

Supported External Systems:

  • Google Cloud Speech-to-Text: Audio transcription
  • Google Cloud Text-to-Speech: Audio synthesis
  • Google Cloud Translation: Text translation

Key Features:

  • Multi-language support (detects and supports many languages)
  • Audio format handling (auto-detects sample rate, channels)
  • Combined operations (speech-to-translated-text, text-to-translated-speech)
  • User voice preferences (stored in Component database)
  • Language and voice discovery (queries Google API for available options)
  • Error handling with detailed error messages

Access Control Summary

Privilege Levels:

  1. SYSADMIN: Full system access, all mandates
  2. ADMIN: Full access within mandate
  3. USER: Access to own records only

Access Methods:

  • uam(): Filters recordsets by privilege, adds _hideView, _hideEdit, _hideDelete flags
  • canModify(): Checks if user can create/update/delete records based on ownership and privilege

Singleton Pattern: Interfaces use factory functions that cache instances per user context for efficient memory usage.

User Context: All database interfaces require user context (User object with mandateId, userId, privilege) for access control and data filtering.