https://github.com/shenrui-li-ds/deep-search
An agentic search application that helps users understand complex topics
https://github.com/shenrui-li-ds/deep-search
agentic llm search-engine tailwind
Last synced: 6 days ago
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An agentic search application that helps users understand complex topics
- Host: GitHub
- URL: https://github.com/shenrui-li-ds/deep-search
- Owner: shenrui-li-ds
- Created: 2025-01-29T08:04:18.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-06T21:02:25.000Z (4 months ago)
- Last Synced: 2025-02-06T22:19:29.447Z (4 months ago)
- Topics: agentic, llm, search-engine, tailwind
- Language: TypeScript
- Homepage:
- Size: 455 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DeepSearch 🔍
DeepSearch is a modern web search application that combines the power of multiple AI models to provide comprehensive, well-cited search results. Built with Next.js and Tailwind CSS, it offers a seamless search experience with features like query refinement, smart summarization, and related searches.
## ✨ Features
- **Smart Query Refinement**: Automatically improves search queries for better results
- **Multi-Provider Support**:
- Alibaba Cloud
- DeepSeek
- OpenAI
- Tavily Search API
- **Rich Search Results**:
- Comprehensive summaries with citations
- Source links and snippets
- Image previews when available
- Related searches
- **Modern UI/UX**:
- Dark/Light mode support
- Responsive design
- Real-time search suggestions
- Parallelization for better performance
## 🚀 Getting Started
### Prerequisites
- Node.js 18+ and npm (for local development)
- Docker and Docker Compose (for containerized deployment)
- API keys for:
- Alibaba Cloud
- DeepSeek
- OpenAI
- Tavily Search API
### 🖥️ Local Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/deep-search.git
cd deep-search
```
2. Install dependencies:
```bash
npm install
```
3. Create a `.env.local` file in the root directory:
```env
ALIBABACLOUD_API_KEY=your_alibabacloud_api_key
DEEPSEEK_API_KEY=your_deepseek_api_key
OPENAI_API_KEY=your_openai_api_key
TAVILY_API_KEY=your_tavily_api_key
```
4. Start the development server:
```bash
npm run dev
```
Visit `http://localhost:3000` to start using DeepSearch.
### 🐋 Docker Installation (TODO)
1. Clone the repository and create `.env.local` as described above.
2. Build and run with Docker Compose:
```bash
docker-compose up -d
```
This will:
- Build the Docker image with Node.js 18
- Set up the environment variables
- Start the container in detached mode
- Map port 3000 to your host machine
3. Access the application at `http://localhost:3000`
To stop the container:
```bash
docker-compose down
```
## 🔧 How It Works
1. **Query Refinement**:
- User enters a search query
- Selected AI provider (OpenAI/DeepSeek/Qwen) refines the query for better results
- The refined query is used for the actual search
2. **Search Process**:
- Tavily API performs the web search
- Results include titles, snippets, URLs, and images
3. **Summarization**:
- Selected AI provider analyzes search results
- Generates a comprehensive research report with citations
- Formats output in markdown with proper source attribution
- Related searches are generated based on the report
4. **Result Display**:
- Summary with clickable citations
- Source list with links and snippets
- Image previews from sources
- Related search suggestions
## 🎨 UI Preview
TODO
## 🛠️ Configuration
### API Providers
Configure API providers in `.env.local`:
```env
ALIBABACLOUD_API_KEY=your_key
DEEPSEEK_API_KEY=your_key
OPENAI_API_KEY=your_key
TAVILY_API_KEY=your_key
```
### Search Settings
Customize search behavior in `src/lib/settings-context.tsx`:
- Default provider
- Search depth
- Result count
- Image inclusion
## 📝 License
MIT License - feel free to use this project for your own purposes.
## 🚧 In Development
TODO