LoLLMs - v2.1.0 "Restart"

One tool to rule them all!

A multi-user FastAPI backend and responsive Vue/Tailwind CSS frontend application designed to provide a chat interface powered by the lollms_client library. It features integrated Retrieval-Augmented Generation (RAG) using safe_store, a versatile personality system, multimodal chat, user management, a friend system with direct messaging, and enhanced sharing capabilities.

Live Project: https://github.com/ParisNeo/lollms

✨ Features

• Multi-User Support: Secure token-based authentication. Each user has their own isolated data.
• Simplified Installation: Get started quickly with simple run.sh or run_windows.bat scripts.
• Environment-Based Configuration: Easy setup using a .env file, automatically generated from .env.example.
• Persistent Discussions: Chat histories are saved per user and can be revisited, renamed, starred, and deleted.
• LLM Integration: Uses lollms-client to interact with various LLM backends.
• Streaming Responses: AI responses are streamed for a real-time experience.
• Multimodal Chat: Upload images with text prompts for vision-capable models.
• Advanced Personality System: Create, edit, and delete custom personalities with unique system prompts, scripts, and data sources.
• Retrieval-Augmented Generation (RAG):
  • Organize documents into multiple DataStores per user.
  • Upload various file types (.txt, .pdf, .docx, etc.).
  • Toggle RAG usage per discussion and select specific DataStores.
  • Share DataStores with friends with configurable permissions.
• Friend System & Direct Messaging (DM):
  • Send, accept, and reject friend requests.
  • Engage in real-time direct messaging with friends.
• Admin Panel:
  • Manage users (Add, List, Delete, Reset Password).
  • Configure global settings and manage LLM/TTI/TTS bindings.
• Data Export/Import: Users can export/import their discussions and settings.
• Responsive UI: Built with Vue.js and Tailwind CSS, featuring Markdown rendering, code highlighting, and math rendering with KaTeX.

🚀 Getting Started

Prerequisites

• Python 3.10+
• Git

Installation

The easiest way to get started is by using the provided run scripts, which handle setup and execution.

1. Clone the Repository:

   git clone https://github.com/ParisNeo/lollms.git
   cd lollms

2. Run the Installer:

   • On Windows: Double-click run_windows.bat.
   • On macOS or Linux:

     chmod +x run.sh
     ./run.sh

   The first time you run the script, it will create a Python virtual environment, install all required dependencies, and create a default .env file from .env.example. Subsequent runs will just start the application.

Usage

1. Access the UI: Once the server is running, open your web browser and go to http://localhost:9642 (or the host and port you configured).
2. Create an Admin Account: On the first launch, you will be prompted to create an administrator account.
3. Login: Use your newly created credentials to log in.
4. Explore:
   • Start a new chat.
   • Go to Settings to configure your profile and select an LLM model.
   • Visit the Admin Panel to configure LLM bindings and other global settings.

⚙️ Configuration (.env file)

Configuration is managed through the .env file in the project's root directory. When you first run the application, this file is created for you from .env.example.

• SERVER_HOST & SERVER_PORT: The host and port the application will run on.
• DATABASE_URL: The location of the main SQLite database file.
• SECRET_KEY: Change this to a long, random string for production.
• ALLOW_NEW_REGISTRATIONS: Set to false to disable public sign-ups.
• INITIAL_ADMIN_USERNAME & INITIAL_ADMIN_PASSWORD: Used on first startup if no admin exists.

For detailed information on all available settings, please refer to the comments within the .env.example file.

📚 API Documentation

• Swagger UI: http://localhost:9642/docs
• ReDoc: http://localhost:9642/redoc

🤝 Contributing

Contributions are welcome! Please open an issue or submit a pull request.

📜 License

Apache License 2.0. See LICENSE.