ChatGPT
AI conversational assistant for answering questions, writing, and coding help
ChuanhuChatGPT
Lightweight web UI for ChatGPT and multiple LLMs
A feature-rich web interface for ChatGPT, GPT-4, Claude, Gemini, and local LLMs with file-based Q&A, web search, agents, and fine-tuning support.
Overview
Modern Interface for Multiple LLMs
Chuanhu Chat delivers a polished, Gradio-based web interface for interacting with leading language models—including GPT-5, GPT-4o, Claude 3, Google Gemini, DeepSeek R1, and locally deployed models like ChatGLM and LLaMA. Designed for researchers, developers, and teams who need flexible LLM access, it combines a mobile-responsive UI with advanced features like knowledge-base Q&A, internet search integration, and autonomous agent assistants.
Key Capabilities
The platform supports file-based question answering, enabling users to upload documents for context-aware responses. Built-in web search keeps answers current, while the agent assistant mode automates multi-step problem solving. GPT-3.5 fine-tuning is natively supported, and custom model endpoints allow integration with local inference servers. Conversation histories are automatically saved, searchable with regex, and can be auto-named by the LLM itself.
Deployment & Audience
Chuanhu Chat offers one-click deployment and can be installed as a Progressive Web App on Chrome, Edge, and Safari. Multi-user history isolation ensures privacy, and system prompts enable effective role-playing scenarios. Ideal for teams requiring a unified interface across commercial APIs and self-hosted models, it balances ease of use with deep configurability.
Highlights
Unified interface for 15+ LLM APIs and local models (GPT, Claude, Gemini, LLaMA, ChatGLM)
Knowledge base Q&A from uploaded files and real-time web search integration
Autonomous agent assistant and native GPT-3.5 fine-tuning support
Mobile-responsive UI with PWA installation, regex-searchable conversation history, and auto-naming
Pros
- Supports both commercial APIs (OpenAI, Anthropic, Google) and local model deployment
- Rich feature set including agents, web search, file Q&A, and fine-tuning in one interface
- Mobile-optimized with PWA support and polished UI including glassmorphism effects
- Multi-user history isolation and advanced conversation management (search, rename, auto-naming)
Considerations
- Gradio-based architecture may limit deep UI customization compared to custom frameworks
- Some models (XMChat, Midjourney) lack streaming support
- Documentation and interface primarily in Chinese, requiring translation for non-Chinese users
- Feature breadth may introduce complexity for users needing only basic chat functionality
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Fit guide
Great for
- Teams needing a single interface for multiple LLM providers and local models
- Researchers experimenting with fine-tuning, agents, and knowledge-base augmentation
- Organizations requiring multi-user deployments with conversation privacy and history management
- Developers integrating custom model endpoints or local inference servers
Not ideal when
- Users seeking a minimalist chat interface without advanced features
- Production applications requiring extensive white-labeling or UI customization
- Teams without technical resources to manage local model deployments
- Projects needing enterprise-grade authentication and compliance features out of the box
How teams use it
Multi-Model Research Comparison
Researchers switch between GPT-4, Claude 3, and local LLaMA models within one interface to benchmark performance across tasks without managing separate tools.
Document-Augmented Customer Support
Support teams upload product manuals and knowledge bases, enabling the LLM to answer customer queries with accurate, context-specific information from internal documents.
Autonomous Task Automation
Users leverage the agent assistant to automatically break down complex requests—like market research or data analysis—into subtasks executed without manual intervention.
Custom Model Fine-Tuning Workflow
Developers fine-tune GPT-3.5 on domain-specific data directly through the interface, then deploy and test the custom model alongside standard APIs for immediate comparison.
Tech snapshot
Python68%
JavaScript14%
CSS10%
HTML8%
Shell1%
Dockerfile1%
Frequently asked questions
Which LLM providers does Chuanhu Chat support?
Chuanhu Chat supports OpenAI (GPT-5, GPT-4, GPT-3.5), Anthropic Claude 3, Google Gemini, Azure OpenAI, DeepSeek R1, and Chinese providers like iFlytek Spark and MiniMax. It also supports local deployment of ChatGLM, LLaMA, StableLM, MOSS, and Qwen models.
Can I deploy and use my own local language models?
Yes, Chuanhu Chat supports one-click local deployment of models including ChatGLM (2 and 3), LLaMA (with Lora), StableLM, MOSS, and Qwen. You can also configure custom model endpoints to integrate local inference servers.
How does the knowledge base feature work?
Upload documents to create a knowledge base, and the LLM will use those files as context to answer questions. This enables document-specific Q&A, allowing the model to reference your uploaded content for more accurate, grounded responses.
Is conversation history private in multi-user deployments?
Yes, Chuanhu Chat isolates conversation histories by user, ensuring that each user's chat records are private and not visible to others in multi-user environments.
Can I install Chuanhu Chat as a standalone app?
Yes, Chuanhu Chat supports Progressive Web App (PWA) installation on Chrome, Edge, and Safari, allowing you to install it as a native-like application on desktop and mobile devices.
Project at a glance
Stable- Stars
- 15,403
- Watchers
- 15,403
- Forks
- 2,264
LicenseGPL-3.0
Repo age2 years old
Last commit5 months ago
Self-hostingSupported
Primary languagePython
Last synced 55 minutes ago