NextChat (ChatGPT Next Web)

What is the project about?

NextChat is a lightweight and fast AI assistant application. It's a user interface designed to interact with various large language models (LLMs).

What problem does it solve?

It provides a clean, user-friendly, and feature-rich interface for interacting with AI models, making it easier for users to access and utilize the power of AI. It also addresses privacy concerns by storing data locally. It simplifies deployment and offers a cross-platform client.

What are the features of the project?

One-click deployment: Easy deployment on Vercel.
Cross-platform client: Desktop client available for Linux, Windows, and MacOS.
LLM Compatibility: Works with self-deployed LLMs, and supports various models like Claude, DeepSeek, GPT-4, and Gemini Pro.
Privacy-focused: Data stored locally in the browser.
Markdown support: Supports LaTex, mermaid diagrams, and code highlighting.
Responsive design: Works well on different screen sizes, includes dark mode, and is a Progressive Web App (PWA).
Fast loading: Optimized for quick initial loading.
Prompt templates: Create, share, and debug chat tools with prompt templates (masks).
Prompt library: Includes a collection of useful prompts.
Chat history compression: Automatically compresses chat history for longer conversations and token saving.
Internationalization (I18n): Supports multiple languages.
Share Functionality: Share conversations as images or to ShareGPT.
System and User Prompts: Customize the AI's behavior.
Plugins: Support for plugins like web search and calculators.
Realtime Chat: Support for real-time chat interactions.
Artifacts: Preview, copy, and share generated content.

What are the technologies used in the project?

Frontend: Likely JavaScript/TypeScript with a framework like React, Next.js, or similar (implied by PWA and deployment targets).
Backend: Node.js (mentioned in requirements).
Deployment: Vercel, Docker, Zeabur, Gitpod.
Desktop Client: Tauri.
AI Models: OpenAI API (GPT-3.5, GPT-4), Google Gemini Pro, Anthropic Claude, DeepSeek, RWKV-Runner, LocalAI, and others.
Database/Storage: Browser local storage, with optional Upstash integration for synchronization.

What are the benefits of the project?

Ease of use: Simple deployment and user-friendly interface.
Flexibility: Supports various AI models and deployment options.
Privacy: Local data storage.
Performance: Fast loading and responsive design.
Extensibility: Prompt templates and plugin support.
Cross-platform: Accessible on web and desktop.
Enterprise Edition: Offers features for business use, including brand customization, resource integration, permission control, knowledge integration, security auditing, and private deployment.

What are the use cases of the project?

General AI interaction: Conversing with AI, asking questions, generating text.
Content creation: Writing articles, emails, code, etc.
Learning and research: Exploring AI capabilities, getting information.
Prototyping AI applications: Testing and developing AI-powered features.
Enterprise knowledge management: Integrating with internal knowledge bases.
Customer support: Building AI-powered chatbots.
Education: Providing an interactive learning environment.
Personal assistant: Managing tasks, setting reminders, etc.