Building SiteSearch AI: A Fullstack RAG Application using Next.js, NestJS, Python, Gemini & Supabase
📰 Medium · RAG
Learn to build a full-stack SiteSearch AI application using Next.js, NestJS, Python, Gemini, and Supabase to turn webpages into searchable RAG context
Action Steps
- Build a website crawler using Python to extract webpage content
- Configure a vector database using Supabase to store webpage embeddings
- Develop a REST API using NestJS to handle search queries and return relevant results
- Create a frontend application using Next.js to interact with the API and display search results
- Integrate Gemini for AI-powered summarization and FAQ generation
Who Needs to Know This
This project is ideal for a team of full-stack developers, AI engineers, and SEO specialists who want to build a site search AI application to improve website intelligence and user experience
Key Insight
💡 By leveraging RAG technology and AI-powered summarization, you can create a site search application that provides accurate and relevant results, improving user experience and SEO
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🚀 Build a full-stack SiteSearch AI app with Next.js, NestJS, Python, Gemini & Supabase to revolutionize website search! 💡
Key Takeaways
Learn to build a full-stack SiteSearch AI application using Next.js, NestJS, Python, Gemini, and Supabase to turn webpages into searchable RAG context
Full Article
AI-powered website intelligence platform that turns webpages into searchable RAG context for SEO, summaries, FAQs & marketing content. Continue reading on Medium »
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