Herbie 3.0

H.E.R.B.I.E (Heuristic Engine for Research-Based Information Extraction) 3.0 represents my solution to digital information overload. By combining multiple AI engines and search technologies into a single interface, H.E.R.B.I.E provides comprehensive answers without requiring users to juggle between different services.

The application's architecture uses Django on the backend to manage API connections and data processing, while React powers the responsive frontend interface. When a user submits a query, H.E.R.B.I.E's intelligent routing system analyses the question and determines which engines are most likely to provide relevant information.

For factual questions, H.E.R.B.I.E pulls data from Google and WolframAlpha. For more nuanced or creative requests, it leverages ChatGPT. The system then collates these responses, removing redundancies and highlighting complementary information, to provide a unified answer. For concepts that benefit from visual representation, the integrated image generation component creates custom illustrations on demand.

What makes H.E.R.B.I.E unique is its learning capability – it remembers context from previous questions in a session, allowing for conversational interactions that build upon earlier responses. This creates a more natural research experience compared to traditional search engines.

H.E.R.B.I.E demonstrates my ability to orchestrate complex interactions between multiple APIs while maintaining a seamless user experience – transforming sophisticated technology into an accessible, practical tool for everyday information needs.

Privacy and security considerations

H.E.R.B.I.E was built with strong privacy protections. User queries are processed with minimal data retention, and personal information is never shared with third-party services. All API communications are encrypted, and the application includes transparent controls for users to manage their interaction history.

Technical integration challenges

Connecting diverse AI services required developing specialised adapters for each API, accounting for their unique authentication requirements, rate limits, and response formats. I implemented an asynchronous processing queue to manage parallel requests efficiently, preventing any single slow service from degrading the overall experience.

Personalisation features

H.E.R.B.I.E includes an optional user profile system that saves preferred information sources and topic interests. This allows returning users to receive more relevant results without repeating context. The recommendation engine subtly adjusts over time based on interaction patterns and explicit feedback.

Performance optimisation

To ensure responsive operation even with complex multi-source queries, I implemented a sophisticated caching system for frequent requests and optimised the rendering pipeline to display initial results quickly while background processing continues. This progressive enhancement approach provides immediate value while maximising comprehensive coverage.