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Contoso GmbH
Assignment
Image Management API - Coding Challenge
Deadline:
You will have 7 days to complete the challenge.
Overview:
Build a scalable image management service that allows teams to securely store, organize, and retrieve images using modern cloud technologies. This challenge tests your ability to work with FastAPI, cloud storage, containerization, and advanced machine learning capabilities.
![Image Management API Architecture](images/image.png)
Core Requirements:
1. REST API with FastAPI
Create a RESTful API using FastAPI with proper endpoint documentation (OpenAPI/Swagger)
Implement appropriate error handling and response status codes
Include proper logging and request validation
2. Data Model
Design and implement the following collections:
Teams: Store team information
Users: Store user details with team associations (no auth flow required)
API Keys: Generate and manage API keys for users
Images: Store metadata about uploaded images with team ownership
3. Cloud Storage Integration
Implement file upload functionality for images
Configure Google Cloud Storage integration for storing image files
Generate unique filenames and manage metadata appropriately
Handle different image formats and validate uploads
4. Access Control
Implement API key authentication (no login flow required)
Ensure users can only access images belonging to their team
Add appropriate middleware for validating API keys
Log all access attempts for audit purposes
5. Containerization & Deployment
Create a Dockerfile for the application
Deploy the containerized application to Google Cloud Run
Configure appropriate environment variables and secrets management
Document the deployment process thoroughly
6. Database Integration
Choose an appropriate database technology for the problem (MongoDB, Firestore,
PostgreSQL, etc.)
Create proper database schemas and relationships
Implement efficient querying patterns
Ensure proper indexing for performance
Bonus Challenges:
1. Image Understanding
Generate image embeddings using a vision-language model (like CLIP, Google Vision
API, etc.)
Store these embeddings in a vector database (Pinecone, Weaviate, etc.)
Establish relationships between images and their embeddings
2. Semantic Image Search
Create an endpoint that accepts natural language prompts
Use the prompt to retrieve relevant images based on semantic similarity
Return ranked results with relevance scores
Optimize for both accuracy and response time
Deliverables:
Source code in a Git repository with clear documentation
Dockerfile and deployment configurations
API documentation (Swagger UI or similar)
Brief architecture document explaining your design decisions
Instructions for local testing and cloud deployment
Any scripts used for setup or data seeding
Evaluation Criteria:
Code Quality: Clean, maintainable code with proper error handling
Architecture: Well-designed system architecture with appropriate separation of concerns
Security: Proper API key validation and access control
Performance: Efficient database queries and image handling
Scalability: Design choices that allow for horizontal scaling
Documentation: Clear and comprehensive documentation
Bonus Points: Implementation of image understanding and semantic search features
Time Expectation:
Core requirements: 4-6 hours
Bonus challenges: Additional 2-4 hours
Note:
While we've provided a time expectation, we value quality over speed. Focus on delivering a well-designed solution rather than rushing to implement all features. Feel free to reach out through Mail in case you have any questions.
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