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A front-end UI for an AI retrieval augmented generation (RAG) application was built, demonstrating an AI chatbot designed to answer questions using data from a provided PDF about a fictional web design agency. The application facilitates users in submitting queries, retrieves answers asynchronously, and displays query results alongside source references. This tutorial guides through creating a NextJS static website and showcases API integration. The existing back-end was already created with FastAPI and hosted on AWS, and users can experiment with a publicly available API endpoint if desired.
AI chatbot utilizes retrieval augmented generation to respond to user queries.
Front end is built using NextJS with a focus on modern UI components.
Existing public API endpoint available for testing the application functionalities.
Claude LLM model used for the AI service via Amazon Bedrock integration.
Automation for generating TypeScript client from FastAPI schema simplifies the process.
The integration of FastAPI with a modern front-end framework like NextJS represents a significant trend in web development. Combining these technologies allows for the rapid creation of scalable applications while leveraging advanced AI capabilities. As demonstrated in this project, utilizing RAG enriches the user experience by providing contextually relevant answers, highlighting the evolving nature of AI applications in practical scenarios.
This tutorial exemplifies the growing accessibility of AI technology for building real-world applications. It raises important questions about user data privacy and the management of AI interactions, especially without robust authentication systems. Developers must consider ethically sound approaches in user data management, even in educational or simplified contexts, ensuring compliance with data protection regulations and fostering trust with users.
This method aids the chatbot in accessing relevant content from documents to answer user queries effectively.
Used in this video to create a scalable front end for the AI application.
6+ based on standard Python type hints. The back-end of the application is already implemented with FastAPI, providing endpoints for the front end to consume.
Integrated via Amazon Bedrock, it serves as the backend AI service for responding to user inquiries in this project.
The application utilizes UUIDs to simulate user identification without requiring authentication processes.
Its Claude LLM model is utilized in this tutorial for backend AI functionalities.
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The source code for the project is hosted there for users to access and follow along.
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