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$
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I will integrate gemini API and rag into your react node app
Nur Hossain
About this gig
Are you looking to make your web application smarter with cutting-edge AI?
I am a Full-Stack MERN & Next.js Developer specializing in seamless Google Gemini API and RAG (Retrieval-Augmented Generation) integrations. Whether you need a smart chatbot or a complex document-reading AI, I build scalable, production-ready solutions.
What I Can Do For You:
- Custom RAG Implementation: Allow your AI to read your custom data (PDFs, docs, databases) using Vector Databases like Qdrant.
- Smart Chatbots: Context-aware AI assistants tailored strictly to your business logic.
- AI-Driven Tools: Automated question generation, code assistants, or data summarizers.
- Full-Stack AI SaaS: End-to-end platform development from UI to backend logic.
My Tech Stack:
- Google Gemini API & LangChain
- Vector Databases (Qdrant)
- Frontend: React.js, Next.js, Tailwind
- Backend: Node.js, Express.js, MongoDB
I focus on clean code and robust architecture, ensuring your AI features perform perfectly under load.
Please message me before placing an order to discuss your specific AI use case and data requirements!
Get to know Nur Hossain
Nur Hossain
Full Stack MERN Engineer , AI And Automation Specialist
- FromBangladesh
- Member sinceSep 2020
Languages
English
I am a Full-Stack MERN Engineer specializing in web automation and AI integrations. I build scalable web apps, custom utility tools, and advanced bots using React, Node.js, and MongoDB. My expertise includes data scraping and browser automation with Puppeteer, and developing cross-platform desktop apps using Electron. I also integrate modern AI (Gemini API, RAG) and handle Linux VPS deployments. Whether you need a complex web platform or a reliable scraping bot, I deliver clean, optimized code. Let's discuss your project!
FAQ
What is RAG and why does my app need it?
RAG (Retrieval-Augmented Generation) allows the AI to answer questions based on your specific private data (like PDFs, company documents, or databases) rather than just its general knowledge. It makes the AI highly accurate and relevant to your business.
Which Vector Database do you use for RAG?
I primarily use Qdrant for storing and retrieving vector embeddings. It is highly scalable, fast, and integrates perfectly with Node.js backend architectures.
Do I need to provide the Gemini API key?
Yes, you will need to provide your own Google Gemini API key so that the AI usage and billing are securely managed under your own Google Cloud account.
Can you integrate this into my existing React or Next.js project?
Absolutely. I can seamlessly plug the Gemini API and RAG architecture into your existing MERN or Next.js codebase without disrupting your current features or database.
Can you build a full AI SaaS platform from scratch?
Yes! If you choose the Premium package, I will build the entire web application from the ground up. This includes the frontend UI, secure user authentication, backend database, and the complete AI integration.
