RAGLight

RAGLight is a lightweight and modular Python library for implementing Retrieval-Augmented Generation (RAG). It enhances the capabilities of Large Language Models (LLMs) by combining document retrieval with natural language inference.

Designed for simplicity and flexibility, RAGLight provides modular components to easily integrate various LLMs, embeddings, and vector stores, making it an ideal tool for building context-aware AI solutions.

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📚 Table of Contents

• Requirements

• Features

• Import library

• Chat with Your Documents Instantly With CLI

  • Ignore Folders Feature
  • Ignore Folders in Configuration Classes

• Deploy as a REST API (raglight serve)

  • Start the server
  • Launch the Chat UI
  • Endpoints
  • Configuration via environment variables
  • Deploy with Docker Compose

• Environment Variables

• Providers and Databases

  • LLM
  • Embeddings
  • Vector Store

• Quick Start

  • Knowledge Base
  • RAG
  • Agentic RAG
  • MCP Integration
  • Use Custom Pipeline
  • Override Default Processors
  • Hybrid Search
  • Qdrant Vector Store
  • Query Reformulation
  • Streaming Output
  • Conversation History
  • AWS Bedrock
  • Observability with Langfuse

• Use RAGLight with Docker

  • Build your image
  • Run your image

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⚠️ Requirements

Actually RAGLight supports :

• Ollama
• Google Gemini
• LMStudio
• vLLM
• OpenAI API
• Mistral API
• AWS Bedrock

If you use LMStudio, you need to have the model you want to use loaded in LMStudio. If you use AWS Bedrock, configure your AWS credentials (env vars, ~/.aws/credentials, or IAM role) — no extra install needed.

Features