A Q & A based PDF - Retrieval Augmented Generation (RAG) application developed with Next js & OpenAi API

(RAG) Retrieval Augmented Generation :-

RAG is a technique that combines information retrieval and language generation to produce more informed and contextual responses. The app leverages Next.js for the front-end, LlamaIndex.TS for managing the retrieval and generation process, and OpenAI's language model for the generation component. It serves as a proof-of-concept for integrating RAG into web applications.

Demo

Technologies Used :-

1. Next js
2. RAG (Retrieval Augmented Generation)
3. OpenAi API
4. LlamaIndex.TS
5. Vercel AI SDK

Features:

1. Next.js Front-end: Provides a seamless and responsive user interface for interacting with the application.

2. PDF-Based Q&A: Enables users to upload PDF documents and automatically extract text for querying.

3. OpenAI API Integration: Uses advanced language models to generate accurate responses based on the extracted PDF text.

4. Retrieval Augmented Generation (RAG): Combines retrieval techniques with AI generation to enhance the relevance and accuracy of responses.

5. Dynamic Content Processing: Supports both server-side and client-side functionalities to handle and process data efficiently.

Benefits:

1. Informed Responses: Provides contextual and accurate answers by leveraging PDF content and AI.

2. Real-time Information Retrieval: Dynamically extracts and processes information from user-uploaded PDFs.

3. Scalable Architecture: Designed to handle multiple requests and large documents efficiently.

4. Easy Integration: Can be seamlessly integrated into existing web applications for added functionality.

5. Practical AI Implementation: Demonstrates the effective use of RAG in real-world applications.

How It Compares:

1. Comprehensive Solution: Offers a more advanced approach compared to traditional Q&A systems.

2. Cutting-edge Technologies: Integrates Next.js, OpenAI, and PDF processing for a robust application.

3. Practical RAG Example: Provides a working model of how RAG can be implemented in web apps.

4. Development Foundation: Serves as a base for further innovation and experimentation in AI applications.

5. Enhanced Response Quality: Combines retrieval and generation to deliver more relevant and accurate answers.