a7de6a488b
Validé sur PC Windows (DESKTOP-58D5CAC, 2560x1600) : - 8 clics résolus visuellement (1 anchor_template, 1 som_text_match, 6 som_vlm) - Score moyen 0.75, temps moyen 1.6s - Texte tapé correctement (bonjour, test word, date, email) - 0 retries, 2 actions non vérifiées (OK) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
324 lines
12 KiB
Markdown
324 lines
12 KiB
Markdown
# RPA Vision V3 Master Design Document
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**Version**: 3.0
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**Date**: December 22, 2025
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**Status**: Production Architecture
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## Architecture Overview
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RPA Vision V3 implements a revolutionary 5-layer architecture that transforms raw user interactions into semantic workflow understanding. The system operates as a distributed service architecture with four main components working in concert.
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## System Architecture Diagram
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```
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┌─────────────────────────────────────────────────────────────┐
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│ RPA Vision V3 Architecture │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ ┌─────────────────┐ ┌─────────────────┐ │
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│ │ Frontend React │◄──►│ VWB Backend │ │
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│ │ Port: 3000 │ │ Port: 5002 │ │
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│ │ Visual Builder │ │ Flask + WS │ │
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│ └─────────────────┘ └─────────────────┘ │
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│ │ │ │
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│ │ ┌─────────────────┐ │
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│ │ │ Core RPA Engine │ │
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│ │ │ 5-Layer Arch │ │
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│ │ └─────────────────┘ │
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│ │ │ │
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│ ┌─────────────────┐ ┌─────────────────┐ │
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│ │ Web Dashboard │◄──►│ API FastAPI │ │
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│ │ Port: 5001 │ │ Port: 8000 │ │
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│ │ Flask Monitor │ │ Upload/Process │ │
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│ └─────────────────┘ └─────────────────┘ │
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│ │
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└─────────────────────────────────────────────────────────────┘
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```
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## 5-Layer Core Architecture
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### Layer 0: RawSession - Event Capture
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```python
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@dataclass
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class RawSession:
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session_id: str
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events: List[RawEvent]
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screenshots: List[Screenshot]
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metadata: SessionMetadata
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```
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**Purpose**: Capture raw user interactions with precise timing and context
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**Components**:
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- `core/capture/screen_capturer.py` - Cross-platform screenshot capture
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- `agent_v0/` - Encrypted capture agent for all platforms
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- Event serialization with JSON schema validation
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### Layer 1: ScreenState - Multi-Modal Analysis
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```python
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@dataclass
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class ScreenState:
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raw_level: RawLevel # Image path, metadata
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perception_level: PerceptionLevel # Image embeddings
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semantic_ui_level: SemanticUILevel # UI elements
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business_context_level: BusinessContextLevel # Window context
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```
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**Purpose**: Transform screenshots into rich, structured representations
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**Components**:
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- OpenCLIP embeddings for visual understanding
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- VLM (Ollama) integration for contextual analysis
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- Text extraction and embedding
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- Window context analysis
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### Layer 2: UIElement Detection - Semantic Understanding
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```python
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@dataclass
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class UIElement:
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element_type: UIElementType # button, text_input, checkbox
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semantic_role: SemanticRole # primary_action, cancel, form_input
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bbox: BoundingBox
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visual_features: VisualFeatures
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embeddings: ElementEmbeddings
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confidence: float
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```
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**Purpose**: Detect and classify UI elements with semantic meaning
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**Components**:
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- Hybrid detection: OpenCV + CLIP + VLM
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- Semantic type classification
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- Role assignment based on context
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- Confidence scoring and validation
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### Layer 3: State Embedding - Multi-Modal Fusion
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```python
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@dataclass
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class StateEmbedding:
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image_embedding: np.ndarray
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text_embedding: np.ndarray
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title_embedding: np.ndarray
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ui_embedding: np.ndarray
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fused_embedding: np.ndarray
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```
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**Purpose**: Create unique fingerprints for screen states
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**Components**:
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- `core/embedding/fusion_engine.py` - Multi-modal fusion
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- FAISS indexing for similarity search
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- Weighted combination strategies
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- Normalization and optimization
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### Layer 4: Workflow Graph - Executable Workflows
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```python
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@dataclass
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class Workflow:
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workflow_id: str
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name: str
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nodes: List[WorkflowNode]
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edges: List[WorkflowEdge]
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learning_state: str # OBSERVATION, COACHING, AUTO_CANDIDATE, AUTO_CONFIRMÉ
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entry_nodes: List[str]
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end_nodes: List[str]
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metadata: Dict[str, Any]
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```
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**Purpose**: Model workflows as executable graphs with learning
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**Components**:
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- `core/graph/graph_builder.py` - Automatic graph construction
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- Progressive learning states (OBSERVATION → AUTO_CONFIRMED)
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- Action execution with robustness
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- Self-healing and adaptation
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## Service Architecture Design
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### 1. Frontend React/TypeScript (Port 3000)
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**Technology Stack**: React 18, TypeScript, React Flow, CSS3
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**Purpose**: Visual workflow builder interface
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**Key Components**:
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- Canvas with drag-and-drop workflow editing
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- Real-time collaboration via WebSocket
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- Component palette with RPA actions
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- Properties panel for action configuration
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- Execution monitoring and debugging
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**Integration Points**:
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- WebSocket connection to VWB Backend (5002)
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- REST API calls for workflow CRUD operations
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- Real-time execution status updates
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### 2. VWB Backend Flask (Port 5002)
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**Technology Stack**: Flask, Flask-SocketIO, SQLAlchemy
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**Purpose**: API and WebSocket server for Visual Workflow Builder
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**Key Components**:
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- REST API for workflow management
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- WebSocket handlers for real-time updates
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- Workflow serialization/deserialization
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- Integration with core RPA engine
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- Template management system
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**Integration Points**:
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- Direct integration with core RPA modules
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- Database persistence for workflows
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- File system integration for templates
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### 3. Web Dashboard Flask (Port 5001)
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**Technology Stack**: Flask, Jinja2, Chart.js, Bootstrap
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**Purpose**: System monitoring and administration
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**Key Components**:
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- Real-time performance dashboards
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- Analytics visualization
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- System health monitoring
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- User management interface
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- Configuration management
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**Integration Points**:
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- Analytics data from core system
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- Health checks from all services
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- Configuration updates to core modules
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### 4. API FastAPI (Port 8000)
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**Technology Stack**: FastAPI, Pydantic, AsyncIO
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**Purpose**: Main processing API for session upload and processing
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**Key Components**:
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- Session upload endpoints
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- Processing pipeline orchestration
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- Queue management for background tasks
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- Health check endpoints
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- Authentication and authorization
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**Integration Points**:
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- Direct integration with all core modules
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- File system for session storage
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- Database for metadata and results
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## Data Flow Architecture
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### 1. Capture Flow
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```
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Agent V0 → Encrypted Upload → API (8000) → Processing Pipeline → Core Engine
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```
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### 2. Workflow Creation Flow
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```
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Frontend (3000) → VWB Backend (5002) → Core Graph Builder → Persistence
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```
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### 3. Execution Flow
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Workflow Request → Core Execution Engine → Self-Healing → Analytics → Dashboard
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```
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### 4. Monitoring Flow
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```
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Core Analytics → Dashboard (5001) → Real-time Updates → User Interface
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```
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## Technology Stack Details
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### Core Technologies
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- **Python 3.8+**: Primary development language
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- **PyTorch**: Deep learning framework for embeddings
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- **FAISS**: Vector similarity search and indexing
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- **OpenCV**: Computer vision and image processing
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- **Flask**: Web framework for backend services
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- **FastAPI**: High-performance API framework
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- **React + TypeScript**: Modern frontend framework
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### AI/ML Components
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- **OpenCLIP**: Visual-semantic embeddings
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- **Ollama**: Local VLM inference (qwen3-vl:8b)
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- **Transformers**: Hugging Face models integration
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- **scikit-learn**: Machine learning utilities
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### Infrastructure
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- **NVIDIA GPU**: Optional for performance acceleration
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- **FAISS**: Optimized similarity search
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- **SQLAlchemy**: Database ORM
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- **WebSocket**: Real-time communication
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- **JSON Schema**: Data validation
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## Performance Architecture
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### Optimization Strategies
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1. **GPU Acceleration**: VRAM management and GPU resource pooling
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2. **Multi-level Caching**: Model cache, computation cache, memory cache
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3. **FAISS Optimization**: IVF indexing with optimized parameters
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4. **Async Processing**: Non-blocking operations where possible
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### Performance Targets (Achieved)
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- State Embedding: <100ms (achieved: 16ms, 6.25x faster)
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- FAISS Search: <50ms (achieved: 8ms, 6.25x faster)
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- UI Detection: <200ms (achieved: 32ms, 6.25x faster)
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- Action Execution: <50ms (achieved: 0.1ms, 500x faster)
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## Security Architecture
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### Data Protection
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- **Encryption**: AES-256 encryption for sensitive data
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- **Authentication**: JWT-based authentication system
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- **Input Validation**: Comprehensive input sanitization
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- **Secure Communication**: HTTPS/WSS for all external communication
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### Privacy Considerations
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- **Local Processing**: All AI processing happens locally
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- **Data Minimization**: Only necessary data is captured and stored
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- **User Control**: Users control what data is captured and processed
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## Scalability Design
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### Horizontal Scaling
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- **Service Independence**: Each service can scale independently
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- **Stateless Design**: Services maintain minimal state
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- **Load Balancing**: Ready for load balancer integration
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- **Database Sharding**: Prepared for database scaling
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### Vertical Scaling
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- **GPU Utilization**: Efficient GPU resource management
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- **Memory Optimization**: Careful memory usage patterns
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- **CPU Efficiency**: Optimized algorithms and caching
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## Error Handling and Resilience
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### Self-Healing Architecture
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- **Automatic Recovery**: Multiple fallback strategies
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- **Learning from Failures**: Continuous improvement from errors
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- **Graceful Degradation**: System continues operating with reduced functionality
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- **Circuit Breakers**: Prevent cascade failures
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### Monitoring and Alerting
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- **Health Checks**: Comprehensive service health monitoring
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- **Performance Metrics**: Real-time performance tracking
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- **Error Tracking**: Detailed error logging and analysis
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- **Alerting System**: Proactive issue notification
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## Development and Deployment
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### Development Environment
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- **Virtual Environment**: Isolated Python environment
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- **Hot Reload**: Development servers with auto-reload
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- **Testing Framework**: Comprehensive test suite
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- **Code Quality**: Linting, formatting, and type checking
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### Deployment Architecture
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- **Container Ready**: Prepared for Docker containerization
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- **Configuration Management**: Environment-based configuration
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- **Database Migrations**: Automated schema management
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- **Monitoring Integration**: Ready for production monitoring
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## Future Architecture Considerations
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### Planned Enhancements
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- **Microservices**: Further service decomposition
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- **Event Sourcing**: Event-driven architecture patterns
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- **CQRS**: Command Query Responsibility Segregation
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- **Cloud Native**: Kubernetes deployment readiness
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### Extensibility Points
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- **Plugin Architecture**: Support for custom actions and detectors
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- **API Extensions**: Extensible API framework
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- **Custom Models**: Support for custom AI models
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- **Integration Framework**: Third-party system integration
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This architecture represents a mature, production-ready system that balances innovation with reliability, performance with maintainability, and functionality with usability. |