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RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.
| Date | Stars |
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| 2026-07-24 | 670 |
| 2026-07-25 | 670 |
| 2026-07-28 | 670 |
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| 2026-07-31 | 670 |
| 2026-08-06 | 670 |
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# RAGLight
[](https://pepy.tech/projects/raglight)
[](https://github.com/Bessouat40/RAGLight/actions/workflows/test.yml)
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<img alt="RAGLight" height="200px" src="./media/raglight.png">
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**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.
---
## 📚 Table of Contents
- [Requirements](#⚠️-requirements)
- [Features](#features)
- [Import library](#import-library-🛠️)
- [Chat with Your Documents Instantly With CLI](#chat-with-your-documents-instantly-with-cli-💬)
- [Ignore Folders Feature](#ignore-folders-feature-🚫)
- [Ignore Folders in Configuration Classes](#ignore-folders-in-configuration-classes-🚫)
- [Deploy as a REST API (raglight serve)](#deploy-as-a-rest-api-raglight-serve-🌐)
- [Start the server](#start-the-server)
- [Launch the Chat UI](#launch-the-chat-ui-💬)
- [Endpoints](#endpoints)
- [Configuration via environment variables](#configuration-via-environment-variables)
- [Deploy with Docker Compose](#deploy-with-docker-compose)
- [Environment Variables](#environment-variables)
- [Providers and Databases](#providers-and-databases)
- [LLM](#llm)
- [Embeddings](#embeddings)
- [Vector Store](#vector-store)
- [Quick Start](#quick-start-🚀)
- [Knowledge Base](#knowledge-base)
- [RAG](#rag)
- [Agentic RAG](#agentic-rag)
- [MCP Integration](#mcp-integration)
- [Use Custom Pipeline](#use-custom-pipeline)
- [Override Default Processors](#override-default-processors)
- [Hybrid Search](#hybrid-search-bm25--semantic--rrf-)
- [Qdrant Vector Store](#qdrant-vector-store-️)
- [Query Reformulation](#query-reformulation-✍️)
- [Streaming Output](#streaming-output-⚡)
- [Conversation History](#conversation-history-💬)
- [AWS Bedrock](#aws-bedrock-☁️)
- [Observability with Langfuse](#observability-with-langfuse)
- [Use RAGLight with Docker](#use-raglight-with-docker)
- [Build your image](#build-your-image)
- [Run your image](#run-your-image)
---
> ## ⚠️ 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
- **Embeddings Model Integration**: Plug in your preferred embedding models (e.g., HuggingFace **all-MiniLM-L6-v2**) for compact and efficient vector embeddings.
- **LLM Agnostic**: Seamlessly integrates with different LLMs from different providers (Ollama, LMStudio, Mistral, OpenAI, Google Gemini, AWS Bedrock).
- **RAG Pipeline**: Combines document retrieval and language generation in a unified workflow.
- **Agentic RAG Pipeline**: Use Agent to improve your RAG performances.
- 🔌 **MCP Integration**: Add external tool capabilities (e.g. code execution, database access) via MCP servers.
- **Flexible Document Support**: Ingest and index various document types (e.g., PDF, TXT, DOCX, Python, Javascript, ...).
- **Extensible Architecture**: Easily swap vector stores, embedding models, or LLMs to suit your needs.
- 🔍 **Hybrid Search (BM25 + Semantic + RRF)**: Combine keyword-based BM25 retrieval with dense vector search using Reciprocal Rank FExcerpt of 40,953 characters
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:05767508322a5cbc, topic:rag, topic:retrieval-augmented-generation, desc:retrieval-augmented generation
matched fp:05767508322a5cbc, topic:agentic-ai, topic:agentic-workflow, readme:agentic