Neural Scholar Engine is an advanced, AI-powered research and search interface designed to provide deep, accurate, and academically rigorous answers. Built with Next.js and powered by Google's Gemini 3 Pro/Flash, it goes beyond simple search to offer a specialized research assistant for students and professionals.

Check out the live application running on Google Cloud Run: neuralsearchengine.app

Neural Scholar Engine bridges the gap between traditional search engines and AI assistants. It offers real-time web access, multi-step reasoning ("Chain of Thought"), and specialized modes for academic subjects.

• High Accuracy & Precision: Delivers trustworthy, fact-based answers by cross-referencing multiple sources, ensuring information is highly accurate and reliable.
• Rich Media Integration: Intelligently searches and curates the most relevant images and videos to visually enhance answers and provide verified context.
• Real-time Streaming: Visualizes the AI's "thinking" process with granular status updates (e.g., "Searching Google...", "Reading sources...").
• Academic Modes: Specialized agents for:
  • Physics (ISC Class 11/12): Solves numerical problems with strict 5-step CoT methodology.
  • Computer Science: Generates Java code adhering to ISC syllabus standards.
  • Accounts/Commerce: Specialized financial concepts assistance.
• Multimodal Search: contextual understanding of images for solving problems or answering visual queries.
• Memory & Context: Intelligent query rewriting to understand follow-up questions (e.g., "Show me more details about him").

• Framework: Next.js 15 (App Router)
• AI Models: Google Gemini 3 Pro & Flash (via Vertex AI / AI Studio)
• Styling: Tailwind CSS, Lucide Icons
• Auth: Auth.js (NextAuth)
• Search: Google Custom Search JSON API, YouTube Data API

Follow these steps to set up the project locally on your machine.

• Node.js (v18 or higher)
• npm or bun
• Git

If you want to contribute or make your own version, start by forking this repository.

1. Click the Fork button at the top right of this page on GitHub.
2. Clone your forked repository to your local machine:

git clone https://github.com/YOUR_USERNAME/Neural-Search-Engine1.git
cd Neural-Search-Engine1

npm install
# or
bun install

Create a .env.local file in the root directory. You will need API keys from Google Cloud Platform.

# Google Cloud & AI
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_SEARCH_API_KEY=your-search-api-key
Google_Search_CX_ID=your-search-engine-id
YOUTUBE_API_KEY=your-youtube-api-key

# Specialized Search Engines (Optional but recommended for Academic modes)
GOOGLE_SEARCH_CX_ID_ISC_PHYSICS=your-physics-cx-id
GOOGLE_SEARCH_CX_ID_ISC_COMPUTER=your-computer-cx-id
GOOGLE_SEARCH_CX_ID_ISC_ACCOUNTS=your-accounts-cx-id

# Optional
AUTH_SECRET=your-random-secret-key
AUTH_GOOGLE_ID=your-google-oauth-client-id
AUTH_GOOGLE_SECRET=your-google-oauth-client-secret

Start the development server:

npm run dev

Open http://localhost:3000 in your browser.

We welcome contributions!

1. Fork the project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

Distributed under the Apache 2.0 License. See LICENSE for more information.