Component Model Design¶
🧩 Overview¶
This document defines the component model for the AI-enhanced GitLab development environment, detailing the modular architecture, component responsibilities, and interaction patterns.
🏗️ Component Architecture¶
Layered Architecture¶
graph TB
subgraph "Presentation Layer"
A[IDE Extensions]
B[Web Dashboard]
C[CLI Interface]
end
subgraph "Application Layer"
D[GitLab MCP Server iwakitakuma]
E[Request Processor]
F[Response Formatter]
end
subgraph "Business Logic Layer"
G[AI Orchestrator]
H[GitLab Manager]
I[User Context Manager]
end
subgraph "Integration Layer"
J[AI Service Connectors]
K[GitLab API Client]
L[Webhook Processor]
end
subgraph "Data Access Layer"
M[Configuration Repository]
N[Cache Manager]
O[Session Store]
end
A --> D
B --> D
C --> D
D --> E
E --> G
G --> J
H --> K
I --> M
E --> F
📦 Core Components¶
1. MCP Server Core¶
Purpose: Central coordination hub for all system operations
classDiagram
class MCPServerCore {
-Router router
-MiddlewareChain middlewares
-ComponentRegistry registry
-ConfigManager config
+initialize()
+start()
+stop()
+registerComponent(component)
+handleRequest(request)
}
class Router {
-Map~String, Handler~ routes
+addRoute(path, handler)
+route(request)
+getHandler(path)
}
class MiddlewareChain {
-List~Middleware~ middlewares
+addMiddleware(middleware)
+execute(request, response)
}
MCPServerCore --> Router
MCPServerCore --> MiddlewareChain
Responsibilities: - Request routing and lifecycle management - Component registration and dependency injection - Configuration management - Error handling and logging
2. AI Orchestrator¶
Purpose: Manages AI service interactions and prompt optimization
classDiagram
class AIOrchestrator {
-List~AIProvider~ providers
-PromptManager promptManager
-ResponseProcessor responseProcessor
+processRequest(request)
+selectProvider(criteria)
+optimizePrompt(prompt)
+aggregateResponses(responses)
}
class AIProvider {
<>
+generateResponse(prompt)
+getCapabilities()
+getStatus()
}
class OpenAIProvider {
-String apiKey
-String model
+generateResponse(prompt)
+getCapabilities()
+getStatus()
}
class AnthropicProvider {
-String apiKey
-String model
+generateResponse(prompt)
+getCapabilities()
+getStatus()
}
AIOrchestrator --> AIProvider
OpenAIProvider ..|> AIProvider
AnthropicProvider ..|> AIProvider
Responsibilities: - AI service selection and load balancing - Prompt engineering and optimization - Response aggregation and formatting - Fallback and error handling
3. GitLab Manager¶
Purpose: Handles all GitLab API interactions and data management
classDiagram
class GitLabManager {
-GitLabClient client
-WebhookHandler webhookHandler
-EventProcessor eventProcessor
+getProjects()
+getMergeRequests(projectId)
+getIssues(projectId)
+processWebhook(payload)
+createComment(content)
}
class GitLabClient {
-String baseUrl
-String accessToken
-HttpClient httpClient
+makeRequest(endpoint, method, data)
+authenticate()
+handleRateLimit()
}
class WebhookHandler {
-Map~String, EventHandler~ handlers
+registerHandler(eventType, handler)
+processWebhook(payload)
+validateWebhook(payload)
}
GitLabManager --> GitLabClient
GitLabManager --> WebhookHandler
Responsibilities: - GitLab API communication - Webhook processing and event handling - Data transformation and caching - Rate limiting and error recovery
4. User Context Manager¶
Purpose: Manages user sessions, preferences, and contextual information
classDiagram
class UserContextManager {
-SessionStore sessionStore
-PreferenceManager preferences
-ContextBuilder contextBuilder
+getUserContext(userId)
+updateUserPreferences(userId, prefs)
+buildRequestContext(request)
+invalidateSession(sessionId)
}
class SessionStore {
-CacheInterface cache
-int ttl
+createSession(userId)
+getSession(sessionId)
+updateSession(sessionId, data)
+expireSession(sessionId)
}
class PreferenceManager {
-Repository~UserPreference~ repository
+getPreferences(userId)
+updatePreferences(userId, prefs)
+getDefaultPreferences()
}
UserContextManager --> SessionStore
UserContextManager --> PreferenceManager
Responsibilities: - User authentication and session management - Preference storage and retrieval - Context building for AI requests - Permission and access control
🔌 Integration Components¶
1. AI Service Connectors¶
Purpose: Abstraction layer for different AI service providers
classDiagram
class AIServiceConnector {
<>
#String apiKey
#HttpClient client
+connect()
+disconnect()
+sendRequest(request)
+handleResponse(response)
+getHealth()
}
class OpenAIConnector {
-String endpoint
-String model
+generateCompletion(prompt)
+generateCode(specification)
+analyzeCode(code)
}
class AnthropicConnector {
-String endpoint
-String model
+generateCompletion(prompt)
+generateCode(specification)
+analyzeCode(code)
}
class LocalAIConnector {
-String endpoint
-String model
+generateCompletion(prompt)
+generateCode(specification)
+analyzeCode(code)
}
AIServiceConnector <|-- OpenAIConnector
AIServiceConnector <|-- AnthropicConnector
AIServiceConnector <|-- LocalAIConnector
2. IDE Integration Components¶
Purpose: Handle communication with various IDE clients
classDiagram
class IDEIntegration {
<>
+sendNotification(message)
+requestInput(prompt)
+updateUI(changes)
+getSelection()
}
class VSCodeIntegration {
-MCPClient mcpClient
-ExtensionContext context
+initialize(context)
+registerCommands()
+handleRequest(request)
}
class CodiumIntegration {
-MCPClient mcpClient
-ExtensionContext context
+initialize(context)
+registerCommands()
+handleRequest(request)
}
IDEIntegration <|.. VSCodeIntegration
IDEIntegration <|.. CodiumIntegration
🗄️ Data Components¶
1. Configuration Repository¶
Purpose: Centralized configuration management
classDiagram
class ConfigurationRepository {
-ConfigSource[] sources
-ConfigValidator validator
-EventEmitter eventEmitter
+loadConfiguration()
+getConfiguration(key)
+updateConfiguration(key, value)
+validateConfiguration(config)
}
class ConfigSource {
<>
+loadConfig()
+saveConfig(config)
+watchChanges()
}
class FileConfigSource {
-String filePath
+loadConfig()
+saveConfig(config)
+watchChanges()
}
class EnvironmentConfigSource {
-Map~String, String~ envVars
+loadConfig()
+saveConfig(config)
+watchChanges()
}
ConfigurationRepository --> ConfigSource
ConfigSource <|-- FileConfigSource
ConfigSource <|-- EnvironmentConfigSource
2. Cache Manager¶
Purpose: Intelligent caching for improved performance
classDiagram
class CacheManager {
-CacheProvider provider
-EvictionPolicy evictionPolicy
-int defaultTTL
+get(key)
+set(key, value, ttl)
+delete(key)
+clear()
+getStats()
}
class CacheProvider {
<>
+get(key)
+set(key, value, ttl)
+delete(key)
+exists(key)
}
class RedisProvider {
-RedisClient client
+get(key)
+set(key, value, ttl)
+delete(key)
+exists(key)
}
class MemoryProvider {
-Map~String, CacheEntry~ cache
+get(key)
+set(key, value, ttl)
+delete(key)
+exists(key)
}
CacheManager --> CacheProvider
CacheProvider <|.. RedisProvider
CacheProvider <|.. MemoryProvider
🔄 Component Interaction Patterns¶
1. Request Processing Pattern¶
sequenceDiagram
participant Client
participant MCPCore as MCP Core
participant AIOrch as AI Orchestrator
participant GitLabMgr as GitLab Manager
participant Cache
Client->>MCPCore: Request
MCPCore->>MCPCore: Validate & Route
MCPCore->>Cache: Check Cache
alt Cache Hit
Cache->>MCPCore: Cached Response
else Cache Miss
MCPCore->>GitLabMgr: Get Context
GitLabMgr->>MCPCore: Context Data
MCPCore->>AIOrch: Process Request
AIOrch->>MCPCore: AI Response
MCPCore->>Cache: Store Response
end
MCPCore->>Client: Final Response
2. Event-Driven Pattern¶
sequenceDiagram
participant GitLab
participant WebhookHandler as Webhook Handler
participant EventProcessor as Event Processor
participant AIOrch as AI Orchestrator
participant Notification as Notification Service
GitLab->>WebhookHandler: Webhook Event
WebhookHandler->>EventProcessor: Process Event
EventProcessor->>AIOrch: Analyze Changes
AIOrch->>EventProcessor: Analysis Result
EventProcessor->>Notification: Send Notification
EventProcessor->>GitLab: Post Comment
🧪 Component Testing Strategy¶
Unit Testing¶
YAML
component_tests:
mcp_server_core:
- test_request_routing
- test_middleware_execution
- test_component_registration
ai_orchestrator:
- test_provider_selection
- test_prompt_optimization
- test_response_aggregation
gitlab_manager:
- test_api_communication
- test_webhook_processing
- test_error_handling
Integration Testing¶
YAML
integration_tests:
ai_gitlab_flow:
- test_code_review_workflow
- test_issue_analysis_flow
- test_documentation_generation
ide_integration:
- test_vscode_communication
- test_codium_communication
- test_real_time_assistance
📊 Component Metrics¶
Performance Metrics¶
YAML
metrics:
request_processing:
- request_duration
- request_throughput
- error_rate
ai_orchestrator:
- provider_response_time
- prompt_optimization_time
- success_rate
gitlab_manager:
- api_response_time
- webhook_processing_time
- rate_limit_hits
Health Metrics¶
YAML
health_checks:
components:
- mcp_server_status
- ai_provider_availability
- gitlab_connectivity
- cache_availability
thresholds:
response_time: 500ms
error_rate: 1%
availability: 99.9%
🔧 Component Configuration¶
Component Registry¶
YAML
components:
mcp_server_core:
class: MCPServerCore
singleton: true
dependencies:
- router
- middleware_chain
ai_orchestrator:
class: AIOrchestrator
dependencies:
- ai_providers
- prompt_manager
gitlab_manager:
class: GitLabManager
dependencies:
- gitlab_client
- webhook_handler
Dependency Injection¶
YAML
dependencies:
ai_providers:
- name: openai
class: OpenAIProvider
config:
api_key: "${OPENAI_API_KEY}"
model: "gpt-4"
- name: anthropic
class: AnthropicProvider
config:
api_key: "${ANTHROPIC_API_KEY}"
model: "claude-3"
This component model provides a solid foundation for building a scalable, maintainable, and testable AI-enhanced GitLab development environment.