llm-inference-web: Building a Modular Large Language Model Inference Web Platform

llm-inference-web is an LLM inference web interface project that supports authentication, guest access, and a modular backend architecture. It aims to lower the barrier to using LLMs, connect model capabilities with end-users, enable developers to quickly test models, and allow end-users to interact in a user-friendly way. The project adopts a modular design that balances security and convenience.

With the development of LLM technology, developers and enterprises face issues such as complex API calls and parameter configuration when integrating model inference capabilities. The llm-inference-web project emerged to provide a complete web interface. Its core value is to lower the usage threshold, support developers in testing models, enable user-friendly interaction for end-users, and adopt a modular design for easy expansion and maintenance.

Authentication and Access Control

Supports registered user mode (complete account system) and guest access mode (basic function experience), with a dual-track system balancing security and convenience.

Modular Backend Design

Advantages include separation of responsibilities, easy expansion, convenient maintenance, and flexible deployment.

Web Interface Interaction

Provides a smooth experience with real-time streaming responses, conversation history management, model parameter adjustment, formatted display, etc.

Inference Engine Integration

Supports mainstream frameworks such as Hugging Face Transformers, vLLM, and OpenAI API. The abstract layer design allows flexible switching of backends.

Session Management Mechanism

Supports multi-user concurrency, independent conversation context, maintains multi-turn coherence, and session data persistence.

Security Considerations

Includes measures such as input filtering (anti-malicious injection), output review, rate limiting, and data isolation.

The project can serve multiple scenarios:

• Internal enterprise AI assistant: Private model deployment, authorized access + guest display;
• Model effect testing platform: Rapid deployment of new models, intuitive evaluation;
• Education and training tool: Students experience AI capabilities without technical details;
• Product prototype verification: Startup teams quickly build prototypes to validate requirements.

llm-inference-web focuses on connecting model capabilities with end-users. The modular architecture and dual-mode access reflect considerations for real-world scenarios. For developers, it is a valuable reference implementation that can be directly deployed or used to learn the architecture. With the development of the LLM ecosystem, such projects will promote the popularization of AI.