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title: Vector
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sdk: docker
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---
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# Vector AI 🚀
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Vector AI is a premium, self-hosted AI workspace that combines the conversational power of ChatGPT with the research capabilities of Perplexity. Built on top of **Flowise**, it provides a stunning, high-performance interface for both casual chat and professional document development.
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---
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## ✨ Key Features
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### 💬 Advanced Chat Interface
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- **SSE Streaming**: Ultra-fast, real-time response streaming for a seamless experience.
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- **Live Activity Indicators**: See exactly what the AI is doing—whether it's "Thinking", "Searching", or using specific tools.
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- **LaTeX & KaTeX**: Full support for mathematical equations, fractions, and scientific notation with beautiful rendering.
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- **File Uploads**: Support for processing PDF and text files directly within the chat.
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- **Speech-to-Text**: Built-in voice input for hands-free interaction.
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### 🧪 Vector Labs (Workspaces)
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- **Project-Based Editing**: Create and manage long-form documents in dedicated workspaces.
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- **Surgical AI Edits**: Highlight specific sections of your text and have the AI refine just that selection.
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- **Persistent State**: Projects are saved locally and persist through page refreshes and sessions.
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- **Global Context**: The AI understands the full document while you edit, ensuring consistency.
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### 📄 Export & Integration
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- **Modern Word Export**: Export your documents to `.docx` with clean, modern typography (Arial/Inter style).
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- **PDF Generation**: Quick PDF exports for sharing your work.
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- **Model Selector**: Easily switch between different Flowise Chatflows for specialized tasks.
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---
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## 🚀 Quick Start
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### 1. Prerequisites
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- **Node.js** (v18 or higher)
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- A running **Flowise** instance (local, HF Spaces, or Railway)
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### 2. Installation
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```bash
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# Clone the repository
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git clone https://github.com/your-repo/vector-ai.git
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cd vector-ai
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# Install dependencies
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npm install
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```
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### 3. Configuration
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Create a `.env` file in the root directory:
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```env
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PORT=3000
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# Primary Chat Models
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MODEL_1_NAME="Vector GPT"
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MODEL_1_ID="YOUR_CHATFLOW_ID_1"
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MODEL_1_HOST="https://your-flowise.hf.space"
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MODEL_2_NAME="Vector Research"
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MODEL_2_ID="YOUR_CHATFLOW_ID_2"
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MODEL_2_HOST="https://your-flowise.hf.space"
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# Dedicated Labs Model (Optional - falls back to Model 1)
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LABS_MODEL_NAME="Vector Editor"
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LABS_MODEL_ID="YOUR_LABS_CHATFLOW_ID"
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LABS_MODEL_HOST="https://your-flowise.hf.space"
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# Optional: Flowise Authentication
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# FLOWISE_API_KEY=your_api_key
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```
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### 4. Development & Build
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```bash
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# Run in development mode
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npm run dev
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# Build the frontend and run the production server
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npm run build
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npm start
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```
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---
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## 🛠 Tech Stack
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- **Frontend**: [React](https://reactjs.org/) + [Vite](https://vitejs.dev/)
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- **Styling**: [Tailwind CSS](https://tailwindcss.com/) + [Framer Motion](https://www.framer.com/motion/)
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- **Backend**: [Node.js](https://nodejs.org/) + [Express](https://expressjs.com/)
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- **Streaming**: Server-Sent Events (SSE)
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- **AI Orchestration**: [Flowise](https://flowiseai.com/)
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- **Rich Text**: [React Markdown](https://github.com/remarkjs/react-markdown) + [KaTeX](https://katex.org/)
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---
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## ☁️ Deployment
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### Render / Railway / HF Spaces
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This project is designed to be easily deployable as a single service:
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1. **Build Command**: `npm install && npm run build`
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2. **Start Command**: `npm start`
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3. **Environment Variables**: Add your `MODEL_*` variables in the platform's dashboard.
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---
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## 📝 License
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This project is licensed under the MIT License.
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title: Vector
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sdk: docker
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