What are APIs?
Learn the fundamental concepts of Application Programming Interfaces and their importance in modern software development.
Interface
Communication
Integration
An API (Application Programming Interface) is a set of protocols, routines, and tools that specify how software components should interact. APIs enable different applications to communicate and share data seamlessly.
# API Fundamentals
api_concepts = {
"definition": "Set of protocols for building and integrating application software",
"purpose": {
"abstraction": "Hide implementation details",
"modularity": "Enable component reuse",
"interoperability": "Allow different systems to communicate",
"scalability": "Support distributed architectures"
},
"types": {
"web_apis": "HTTP-based interfaces for web services",
"library_apis": "Programming interfaces for code libraries",
"operating_system_apis": "System calls and OS functions",
"database_apis": "Interfaces for data access and manipulation"
},
"ai_context": {
"model_serving": "Expose AI models as web services",
"data_integration": "Connect AI systems with data sources",
"workflow_orchestration": "Chain multiple AI services",
"third_party_integration": "Access external AI capabilities"
}
}
REST Architectural Style
Master the principles of Representational State Transfer (REST) for building scalable web APIs.
REST Principles:
• Stateless: Each request contains all necessary information
• Client-server: Separation of concerns between UI and data storage
• Cacheable: Responses should be cacheable when appropriate
• Uniform interface: Consistent resource identification and manipulation
• Layered system: Support for intermediary layers like proxies
• Code on demand (optional): Server can send executable code
RESTful AI APIs:
REST is ideal for AI APIs because it provides a simple, scalable approach to exposing model predictions and managing AI resources. The stateless nature fits well with model inference patterns.
# REST API Design for AI
rest_ai_api = {
"resources": {
"models": "/api/v1/models",
"predictions": "/api/v1/models/{model_id}/predict",
"training_jobs": "/api/v1/training",
"datasets": "/api/v1/datasets"
},
"http_methods": {
"GET": "Retrieve model info or prediction results",
"POST": "Submit prediction requests or start training",
"PUT": "Update model configuration",
"DELETE": "Remove models or stop training jobs"
},
"example_endpoints": {
"list_models": "GET /api/v1/models",
"get_model": "GET /api/v1/models/sentiment-v1",
"predict": "POST /api/v1/models/sentiment-v1/predict",
"batch_predict": "POST /api/v1/models/sentiment-v1/batch"
}
}
HTTP Methods and Status Codes
Understand proper usage of HTTP methods and status codes for meaningful API communication.
HTTP Methods for AI APIs:
• GET: Retrieve model metadata, prediction results, training status
• POST: Submit predictions, start training, upload data
• PUT: Update model configurations, replace datasets
• PATCH: Partial updates to model settings
• DELETE: Remove models, cancel jobs, clear caches
Status Code Best Practices:
Use appropriate status codes to help clients understand response outcomes and implement proper error handling. This is crucial for robust AI API integrations.
# HTTP Status Codes for AI APIs
ai_api_status_codes = {
"success_codes": {
"200": "Prediction completed successfully",
"201": "Model created or training job started",
"202": "Prediction request accepted for processing",
"204": "Model deleted successfully"
},
"client_error_codes": {
"400": "Invalid input data format",
"401": "Authentication required",
"403": "Insufficient permissions for model access",
"404": "Model not found",
"422": "Input validation failed",
"429": "Rate limit exceeded"
},
"server_error_codes": {
"500": "Model inference failed",
"502": "Upstream model service unavailable",
"503": "Service temporarily overloade