NeuraFind.ai

Project Summary

This project is a modern web application built using Next.js and React that focuses on providing an AI-powered product selection experience. The application helps users find products tailored to their needs by offering intelligent recommendations based on user input in thier natural language. It integrates with OpenAI for generating responses and uses Pinecone to manage embeddings and perform vector searches.

Key Features:

• AI-Powered Recommendations: Leverages OpenAI's capabilities to generate personalized product recommendations and provide detailed responses to user queries.
• Responsive Design: Optimized for mobile, tablet, and desktop devices, ensuring a seamless experience across all screen sizes.
• Dynamic Navigation: Includes navigation links to various sections like Features, Pricing, and Examples to enhance user engagement.
• Interactive UI Elements: Incorporates animations (via Framer Motion) and interactive components (like buttons and tooltips) for a smooth user experience.
• Voice Input: Allows users to interact with the platform via voice commands for more natural engagement.
• Real-Time Data Handling: The backend processes user queries and audio transcriptions efficiently for quick, responsive interactions.

Performance and Optimization

• Code Splitting: Thanks to Next.js, the app automatically performs code splitting, ensuring that only the necessary JavaScript is loaded for the current page, improving load times.
• Static Site Generation (SSG): Pages are statically generated for faster loads and better SEO.
• Efficient State Management: React's context and hooks are used for optimal state management, ensuring responsiveness and performance.
• Image Optimization: Leveraging Next.js's built-in image optimization ensures the best format and size for each image, enhancing performance.

Dependencies

This project uses a variety of modern libraries and tools to ensure optimal performance, interactivity, and scalability. Below are some of the key dependencies:

• React: Frontend framework for building the UI.
• Next.js: Server-side rendering, static site generation, and routing.
• OpenAI API: Provides AI capabilities for generating responses and product recommendations.
• Pinecone: Used for efficient vector storage and retrieval of embeddings.
• Framer Motion: Adds animations and transitions to UI components for a dynamic experience.
• Voice Recognition API: Facilitates voice input for user interaction.

Performance Metrics

• Load Times: With the use of Next.js optimizations like static site generation and code splitting, the expected page load times are under 1 second for optimized pages.
• Scalability: The use of Pinecone for vector storage and retrieval ensures the application can scale efficiently as the user base grows.

User Engagement

• Voice Input & AI Recommendations: These features significantly improve user engagement, allowing for personalized and efficient product discovery.
• Interactive UI: Dynamic animations, tooltips, and responsive design contribute to an engaging and enjoyable user experience.

Getting Started

Prerequisites

To run the project locally, make sure you have the following installed:

• Node.js (v16 or later)
• npm or Yarn (depending on your package manager of choice)

Installation

1. Clone the repository:

   git clone https://github.com/adysingh5711/neurafind.ai_Frontend

2. Navigate into the project directory:

   cd neurafind.ai_Frontend

3. Install the dependencies:

   npm install
   # or
   yarn install

4. Set up environment variables for OpenAI and Pinecone:

   • Rename the .env.example file to .env and add your keys:

5. Run the development server:

   npm run dev
   # or
   yarn dev

6. Open the application in your browser at http://localhost:3000.

Usage

• Search for Products: Enter keywords into the search bar to receive product recommendations.
• Voice Input: Use the voice input feature to make queries or requests by speaking.
• Navigation: Use the dynamic navigation bar to explore other sections like Features, Pricing, and Examples.

Future Improvements

• Advanced Personalization: Enhance the AI recommendation system to provide more accurate and personalized suggestions.
• User Profiles: Allow users to create profiles to save their preferences and recommendations.
• Multi-Language Support: Extend the application to support multiple languages for a broader user base.
• Product Comparison: Add a feature to compare products side by side, helping users make more informed decisions.
• Extension : Add an extension for chrome and firefox to help users find products on any website.

License

Backend Repository: NeuraFind.ai_Backend

License

This project is licensed under the MIT License.

Acknowledgements

• OpenAI for their API and advanced AI capabilities.
• Pinecone for their vector database service that powers efficient product searches.
• Framer Motion for providing easy-to-implement animations and transitions.