RAGLight is a Python library for building Retrieval-Augmented Generation (RAG) systems, offering modular components for integrating various LLMs, embedding models, and vector stores. It targets developers and researchers looking to create context-aware AI applications with support for RAG, Agentic RAG, and RAT (Retrieval Augmented Thinking) pipelines, simplifying the process of connecting documents to LLM responses.

How It Works

RAGLight employs a modular architecture allowing users to swap components like embedding models (e.g., HuggingFace all-MiniLM-L6-v2), LLM providers (Ollama, LMStudio, OpenAI, Mistral, vLLM), and vector stores (Chroma). It supports various data sources including local folders and GitHub repositories, with an intelligent ignore-folders feature to exclude irrelevant files during indexing. The library facilitates building RAG, Agentic RAG, and RAT pipelines, enabling complex workflows with agents and reflection loops.

Quick Start & Requirements

• Install via pip: pip install raglight
• Requires a local LLM service (Ollama, LMStudio) or API keys for OpenAI/Mistral.
• CLI wizard available: raglight chat or raglight agentic-chat.
• Documentation and examples are available in the repository.

Highlighted Details

• Supports multiple LLM providers including Ollama, LMStudio, OpenAI, Mistral, and vLLM.
• Integrates with HuggingFace for embedding models.
• Offers distinct pipelines for RAG, Agentic RAG, and RAT.
• Includes a CLI wizard for simplified setup and interaction.
• Features an "ignore folders" mechanism for efficient document indexing.

Maintenance & Community

The project appears to be actively maintained by Bessouat40. Community interaction channels are not explicitly mentioned in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The README does not explicitly state the license, which could be a blocker for commercial adoption. While it lists supported LLM providers, the setup and configuration details for each might require deeper investigation. The project is presented as a library, implying users need to write Python code to leverage its full capabilities beyond the CLI wizard.