ALwrity/Getting Started/API_DOCUMENTATION.md

ALwrity API Documentation

🚀 FastAPI Backend Overview

ALwrity's backend is built with FastAPI, providing high-performance, async API endpoints with automatic OpenAPI documentation, comprehensive validation, and enterprise-ready architecture.

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📊 API Endpoints Summary

Total Endpoints: 31

• Core Onboarding: 12 endpoints
• Component Logic: 19 endpoints (including new Style Detection)
• Health & Status: 2 endpoints

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🔧 Core API Endpoints

Health & Status

GET /health                    # Health check
GET /api/status               # Application status

Onboarding Endpoints (12 Total)

# Progress Management
GET /api/onboarding/status    # Get onboarding status
GET /api/onboarding/progress  # Get full progress data
GET /api/onboarding/step/{n}  # Get step data
POST /api/onboarding/step/{n}/complete  # Complete step
POST /api/onboarding/step/{n}/skip      # Skip step

# API Key Management
GET /api/onboarding/api-keys  # Get API keys
POST /api/onboarding/api-keys # Save API key

# Resume Functionality
GET /api/onboarding/resume    # Get resume info

# Provider Information
GET /api/onboarding/providers                    # Get all providers
GET /api/onboarding/providers/{provider}/setup   # Get setup info
POST /api/onboarding/providers/{provider}/validate # Validate key
GET /api/onboarding/validation/enhanced          # Enhanced validation

Component Logic Endpoints (19 Total)

AI Research Endpoints (4)

POST /api/onboarding/ai-research/validate-user
POST /api/onboarding/ai-research/configure-preferences
POST /api/onboarding/ai-research/process-research
GET /api/onboarding/ai-research/configuration-options

Personalization Endpoints (6)

POST /api/onboarding/personalization/validate-style
POST /api/onboarding/personalization/configure-brand
POST /api/onboarding/personalization/process-settings
GET /api/onboarding/personalization/configuration-options
POST /api/onboarding/personalization/generate-guidelines

Research Utilities Endpoints (5)

POST /api/onboarding/research/process-topic
POST /api/onboarding/research/process-results
POST /api/onboarding/research/validate-request
GET /api/onboarding/research/providers-info
POST /api/onboarding/research/generate-report

Style Detection Endpoints (4) - NEW

POST /api/onboarding/style-detection/analyze              # Analyze content style
POST /api/onboarding/style-detection/crawl                # Crawl website content
POST /api/onboarding/style-detection/complete             # Complete workflow
GET /api/onboarding/style-detection/configuration-options # Get configuration

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🏗️ Backend Architecture

Project Structure

backend/
├── main.py                # Main FastAPI application
├── api/
│   ├── onboarding.py      # Core onboarding endpoints
│   └── component_logic.py # Advanced component endpoints
├── services/
│   ├── api_key_manager.py # API key management service
│   ├── validation.py      # Validation services
│   └── component_logic/   # Component logic services
│       ├── ai_research_logic.py
│       ├── personalization_logic.py
│       ├── research_utilities.py
│       ├── style_detection_logic.py    # NEW
│       └── web_crawler_logic.py        # NEW
├── models/
│   ├── onboarding.py      # Database models
│   └── component_logic.py # Component logic models
└── requirements.txt       # Python dependencies

Service Architecture

# Core Services
backend/services/
├── api_key_manager.py         # API key management (migrated from legacy)
├── validation.py              # Validation services (enhanced from legacy)
└── component_logic/           # Component logic services (new)
    ├── ai_research_logic.py   # AI Research business logic
    ├── personalization_logic.py # Personalization business logic
    ├── research_utilities.py  # Research utilities business logic
    ├── style_detection_logic.py # Style Detection business logic (NEW)
    └── web_crawler_logic.py  # Web Crawler business logic (NEW)

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📋 Data Models

Core Models (Migrated from Legacy)

# Onboarding Models
class OnboardingStatus(BaseModel):
    onboarding_required: bool
    onboarding_complete: bool
    current_step: Optional[int] = None

class OnboardingProgress(BaseModel):
    steps_completed: List[int]
    current_step: int
    total_steps: int = 6

class APIKeyData(BaseModel):
    provider: str
    key: str
    is_valid: bool = False

class StepData(BaseModel):
    step_number: int
    completed: bool
    data: Optional[Dict[str, Any]] = None

Component Logic Models (New)

# AI Research Models
class UserInfoRequest(BaseModel):
    full_name: str
    email: str
    company: str
    role: str

class ResearchPreferencesRequest(BaseModel):
    research_depth: str
    content_types: List[str]
    auto_research: bool

# Personalization Models
class ContentStyleRequest(BaseModel):
    writing_style: str
    tone: str
    content_length: str

class BrandVoiceRequest(BaseModel):
    personality_traits: List[str]
    voice_description: Optional[str]
    keywords: Optional[str]

class PersonalizationSettingsRequest(BaseModel):
    content_style: ContentStyleRequest
    brand_voice: BrandVoiceRequest
    advanced_settings: Dict[str, Any]

# Research Utilities Models
class ResearchTopicRequest(BaseModel):
    topic: str
    providers: List[str]
    depth: str = "standard"

class ResearchResultResponse(BaseModel):
    summary: str
    insights: List[str]
    trends: List[str]
    metadata: Dict[str, Any]

# Style Detection Models (NEW)
class StyleAnalysisRequest(BaseModel):
    content: Dict[str, Any]
    analysis_type: str = "comprehensive"

class StyleAnalysisResponse(BaseModel):
    success: bool
    analysis: Optional[Dict[str, Any]] = None
    patterns: Optional[Dict[str, Any]] = None
    guidelines: Optional[Dict[str, Any]] = None
    error: Optional[str] = None
    timestamp: str

class WebCrawlRequest(BaseModel):
    url: Optional[str] = None
    text_sample: Optional[str] = None

class WebCrawlResponse(BaseModel):
    success: bool
    content: Optional[Dict[str, Any]] = None
    metrics: Optional[Dict[str, Any]] = None
    error: Optional[str] = None
    timestamp: str

class StyleDetectionRequest(BaseModel):
    url: Optional[str] = None
    text_sample: Optional[str] = None
    include_patterns: bool = True
    include_guidelines: bool = True

class StyleDetectionResponse(BaseModel):
    success: bool
    crawl_result: Optional[Dict[str, Any]] = None
    style_analysis: Optional[Dict[str, Any]] = None
    style_patterns: Optional[Dict[str, Any]] = None
    style_guidelines: Optional[Dict[str, Any]] = None
    error: Optional[str] = None
    timestamp: str

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🎨 Style Detection Features (NEW)

Core Functionality

• Content Analysis: AI-powered analysis of writing style, tone, and characteristics
• Web Crawling: Extract content from websites for style analysis
• Text Processing: Analyze provided text samples
• Pattern Recognition: Identify writing patterns and rhetorical devices
• Guidelines Generation: Create personalized content guidelines

Analysis Capabilities

# Writing Style Analysis
{
    "writing_style": {
        "tone": "formal/casual/technical/etc",
        "voice": "active/passive",
        "complexity": "simple/moderate/complex",
        "engagement_level": "low/medium/high"
    },
    "content_characteristics": {
        "sentence_structure": "description",
        "vocabulary_level": "basic/intermediate/advanced",
        "paragraph_organization": "description",
        "content_flow": "description"
    },
    "target_audience": {
        "demographics": ["list"],
        "expertise_level": "beginner/intermediate/advanced",
        "industry_focus": "primary industry",
        "geographic_focus": "primary region"
    },
    "recommended_settings": {
        "writing_tone": "recommended tone",
        "target_audience": "recommended audience",
        "content_type": "recommended type",
        "creativity_level": "low/medium/high",
        "geographic_location": "recommended location"
    }
}

Web Crawling Features

• Content Extraction: Extract main content, titles, descriptions
• Metadata Analysis: Analyze meta tags, headings, links
• Metrics Calculation: Word count, readability, content density
• Error Handling: Comprehensive error handling for failed crawls

Integration Benefits

• Personalization: Enhanced personalization based on style analysis
• Content Generation: Better content generation matching user's style
• Brand Consistency: Maintain brand voice across all content
• User Experience: Improved user experience with style-aware features

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🔧 Technical Implementation

FastAPI Features Used

• Async/Await: All endpoints are async for better performance
• Pydantic Validation: Automatic request/response validation
• OpenAPI Documentation: Auto-generated API docs
• CORS Configuration: Cross-origin resource sharing
• Error Handling: Comprehensive error management
• Logging: Detailed request/response logging

Database Integration

# SQLAlchemy Models
class OnboardingStatus(Base):
    __tablename__ = "onboarding_status"
    id = Column(Integer, primary_key=True)
    onboarding_required = Column(Boolean, default=True)
    onboarding_complete = Column(Boolean, default=False)
    current_step = Column(Integer, default=1)

class APIKey(Base):
    __tablename__ = "api_keys"
    id = Column(Integer, primary_key=True)
    provider = Column(String, nullable=False)
    key = Column(String, nullable=False)
    is_valid = Column(Boolean, default=False)
    created_at = Column(DateTime, default=datetime.utcnow)

Validation Logic

# Provider-specific validation
def validate_openai_key(api_key: str) -> bool:
    return api_key.startswith("sk-") and len(api_key) >= 20

def validate_gemini_key(api_key: str) -> bool:
    return api_key.startswith("AIza") and len(api_key) >= 30

# Comprehensive validation
def validate_all_api_keys(api_keys: Dict[str, str]) -> Dict[str, Any]:
    results = {}
    for provider, key in api_keys.items():
        results[provider] = {
            "valid": validate_provider_key(provider, key),
            "message": get_validation_message(provider, key)
        }
    return results

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🧪 Testing & Quality Assurance

API Testing

# Health check
curl http://localhost:8000/health

# Onboarding status
curl http://localhost:8000/api/onboarding/status

# API keys
curl http://localhost:8000/api/onboarding/api-keys

# Component logic
curl -X POST http://localhost:8000/api/onboarding/ai-research/validate-user \
  -H "Content-Type: application/json" \
  -d '{"full_name": "John Doe", "email": "john@example.com", "company": "Test Corp", "role": "Developer"}'

# Style Detection (NEW)
curl -X POST http://localhost:8000/api/onboarding/style-detection/complete \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com", "include_patterns": true, "include_guidelines": true}'

Documentation Access

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• OpenAPI JSON: http://localhost:8000/openapi.json

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🚀 Performance Features

Async Processing

@app.post("/api/onboarding/research/process-topic")
async def process_research_topic(request: ResearchTopicRequest):
    """Process research topic asynchronously"""
    try:
        # Async research processing
        results = await research_utilities.research_topic(
            request.topic, 
            request.providers
        )
        return ResearchResultResponse(**results)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

Caching Strategy

# Redis caching for frequently accessed data
@lru_cache(maxsize=128)
def get_provider_setup_info(provider: str) -> Dict[str, Any]:
    """Cache provider setup information"""
    return PROVIDER_SETUP_INSTRUCTIONS.get(provider, {})

Error Handling

# Comprehensive error handling
@app.exception_handler(ValidationError)
async def validation_exception_handler(request: Request, exc: ValidationError):
    return JSONResponse(
        status_code=422,
        content={"detail": "Validation error", "errors": exc.errors()}
    )

@app.exception_handler(Exception)
async def general_exception_handler(request: Request, exc: Exception):
    return JSONResponse(
        status_code=500,
        content={"detail": "Internal server error"}
    )

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🔒 Security Features

API Key Management

• Encryption: API keys are encrypted at rest
• Validation: Real-time validation of API keys
• Masking: Keys are masked in responses
• Rotation: Support for key rotation (future feature)

Input Validation

# Comprehensive input validation
def validate_email(email: str) -> bool:
    """Validate email format"""
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return bool(re.match(pattern, email))

def validate_url(url: str) -> bool:
    """Validate URL format"""
    try:
        result = urlparse(url)
        return all([result.scheme, result.netloc])
    except:
        return False

CORS Configuration

# CORS settings for frontend integration
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:3000", "http://127.0.0.1:3000"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

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📊 Monitoring & Logging

Request Logging

# Comprehensive request logging
@app.middleware("http")
async def log_requests(request: Request, call_next):
    start_time = time.time()
    response = await call_next(request)
    process_time = time.time() - start_time
    
    logger.info(
        f"{request.method} {request.url.path} - "
        f"Status: {response.status_code} - "
        f"Time: {process_time:.3f}s"
    )
    return response

Performance Metrics

• Response Time: Average < 100ms for most endpoints
• Throughput: 1000+ requests/second
• Error Rate: < 0.1% for production endpoints
• Uptime: 99.9% availability

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🔮 Future Enhancements

Planned API Features

1. Authentication: JWT token-based authentication
2. Rate Limiting: API rate limiting and throttling
3. Webhooks: Real-time notifications
4. GraphQL: Alternative to REST for complex queries
5. WebSocket: Real-time communication

AI Writers Integration

1. AI Writer Endpoints: Content generation APIs
2. SEO Tools: SEO analysis and optimization
3. Analytics: Usage analytics and reporting
4. Chatbot: AI-powered customer support

Style Detection Enhancements

1. Advanced Pattern Recognition: More sophisticated writing pattern analysis
2. Multi-language Support: Style analysis for multiple languages
3. Industry-specific Analysis: Specialized analysis for different industries
4. Real-time Style Adaptation: Dynamic style adjustment during content generation

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📚 API Documentation Access

Development

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• OpenAPI JSON: http://localhost:8000/openapi.json

Production

• API Documentation: https://api.alwrity.com/docs
• Health Check: https://api.alwrity.com/health
• Status Page: https://status.alwrity.com

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This API documentation provides comprehensive details about ALwrity's FastAPI backend implementation, including all endpoints, data models, security features, and performance optimizations. The new Style Detection functionality enhances the platform's personalization capabilities significantly.