Recipe Bazaar AI is a production-style demonstration of how to design and implement a scalable, AI-powered search system on top of a relational database. The project explores how modern applications can move beyond traditional SQL queries by introducing a dedicated search layer that delivers fast, relevant, and intelligent results while maintaining a clean separation of concerns.
This system is designed to simulate a real-world architecture where:
- A relational database serves as the source of truth
- A search index is used for fast and relevant querying
- An API layer exposes modern search capabilities
- AI techniques enhance query understanding and result quality
The goal is to demonstrate how to build a system that is both developer-friendly and scalable under growing data and traffic demands.
The system is composed of the following components:
-
API Layer
- Handles incoming requests
- Exposes search endpoints
- Coordinates between database and search services
-
Relational Database
- Stores structured application data
- Acts as the single source of truth
-
Search Index + Indexer
- Transforms relational data into a searchable format
- Enables fast, relevance-based queries
- Supports near real-time updates
-
AI Layer
- Enhances search queries
- Improves ranking and relevance
- Enables future extension into semantic search
-
Separation of concerns
Search functionality is decoupled from the transactional database to reduce load and improve performance. -
Index-based querying
Instead of relying on complex SQL queries, the system uses indexing to achieve faster and more flexible search. -
Containerized environment
Docker is used to ensure reproducibility and simplify local development and deployment. -
Real-time indexing
Updates to the system are reflected quickly in search results to maintain consistency.
| Feature | Description |
|---|---|
| Full-Text Search | Search across multiple fields with relevance ranking |
| Autocomplete | Type-ahead suggestions with typo tolerance |
| Weighted Search | Prioritize fields such as title over description |
| Real-Time Updates | Newly added data becomes searchable quickly |
This project is designed with scalability in mind:
- At higher scale, indexing can be moved to asynchronous background jobs
- Caching can be introduced to reduce repeated query processing
- Real-time indexing improves freshness but may impact throughput
- Query performance depends on index size and complexity
Tradeoffs were made between simplicity, performance, and real-time consistency.
The system introduces AI as a way to improve search quality:
- Enhances user queries for better matching
- Opens the door for semantic search capabilities
- Requires balancing cost, latency, and accuracy
Future improvements may include embedding-based search and smarter ranking strategies.
The entire system is containerized for quick setup.
git clone https://github.com/ijeesti/recipe-bazaar-ai.git
cd recipe-bazaar-ai
docker-compose up --buildOnce running, open Swagger UI in your browser to explore and test the API endpoints.
###🔮 Future Improvements Distributed indexing for large-scale datasets API rate limiting and request throttling Observability (logging, monitoring, metrics) CI/CD pipeline for automated deployment Advanced AI features such as semantic search
Pull requests are welcome. For major changes, please open an issue first to discuss your ideas.
This project demonstrates how to design a modern search system that: 1- Scales beyond traditional database queries 2- Uses AI to improve search experience 3- Applies real-world engineering tradeoffs 4- Maintains clean and extensible architecture