I've always been fascinated by the power of AI and its potential to revolutionize the way we search and interact with information. This led me to build Web Researcher, an AI-powered search engine with a generative UI. Here’s how I did it:
Note: This post outlines the steps and technologies I used to build Web Researcher. Feel free to explore and adapt these concepts to your own projects.
Overview
Web Researcher is an AI-powered search engine designed to understand and respond to user queries using a generative UI. The goal was to create a seamless and intuitive user experience that leverages the latest advancements in AI and web development.
Key Features
- Search and Answer Using Generative UI: The core functionality involves understanding the user's questions and generating relevant answers.
- Understand User's Questions: Utilizing natural language processing to accurately interpret user queries.
- Search History Functionality: Keeping track of past searches for easy reference and continuity.
- Get Answers from Specified URLs: Allowing users to fetch answers from specific URLs.
- Use as a Search Engine: Providing comprehensive search capabilities with AI-generated results.
Technologies Used
App Framework: Next.js
I used Next.js as the framework for building the application. Its features, like server-side rendering and static site generation, are perfect for creating fast and efficient web apps.
Text Streaming / Generative UI: Vercel AI SDK
The Vercel AI SDK was instrumental in implementing the generative UI. It allowed for real-time text streaming, enhancing the interactive experience of the search engine.
Generative Model: OpenAI
To power the generative responses, I integrated the OpenAI model. Its advanced capabilities in understanding and generating human-like text were crucial for accurate and relevant search results.
Search API: Tavily AI / Serper
For the search functionality, I utilized Tavily AI and Serper. These APIs provided robust search capabilities, ensuring comprehensive and accurate results.
Reader API: Jina AI
To extract information from specified URLs, I used the Jina AI Reader API. It enabled the application to fetch and process content from various sources effectively.
Serverless Database: Upstash
For storing search history and other data, I opted for Upstash. Its serverless architecture and Redis-compatible API made it an excellent choice for scalable and efficient data storage.
Component Library: shadcn/ui
The shadcn/ui component library provided a rich set of UI components, making the development process faster and the UI more consistent.
Headless Component Primitives: Radix UI
To build accessible and customizable components, I incorporated Radix UI. Its headless component primitives were essential for creating a flexible and user-friendly interface.
Styling: Tailwind CSS
For styling, I chose Tailwind CSS. Its utility-first approach allowed me to create a clean and responsive design efficiently.
Conclusion
Building Web Researcher has been a rewarding experience, combining cutting-edge technologies to create a powerful and intuitive search engine. The integration of AI and a generative UI has transformed how users interact with search results, providing a more engaging and informative experience.
Feel free to reach out if you have any questions or suggestions. Happy coding!