pip install raglight & Start Chatting with Docs

RAGLight is a lightweight, modular Python library that makes it simple to implement Retrieval-Augmented Generation (RAG) and extend it with Agentic RAG and RAT pipelines. Whether you’re using Ollama, LMStudio, vLLM, OpenAI, Google Gemini, or Mistral, RAGLight helps you connect your documents, embeddings, and vector stores into a seamless workflow.

In this video, you’ll learn:
✅ How to install and set up RAGLight (pip install raglight)
✅ Using the CLI wizard to chat with your documents instantly
✅ Configuring ignore folders for clean indexing
✅ Creating a RAGPipeline with knowledge bases (Folder + GitHub sources)
✅ Building an Agentic RAG Pipeline with reflection loops and agents
✅ Customizing embeddings, vector stores (Chroma), and LLM providers

🔌 With MCP integration, you can even extend RAGLight with external tools like databases and code execution.

By the end of this tutorial, you’ll know how to:
• Index your documents with a vector database
• Connect any LLM provider (Ollama, LMStudio, Mistral, vLLM, OpenAI, Gemini)
• Run powerful RAG and Agentic RAG pipelines in just a few lines of Python
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📂 Links & Resources:
🔗 RAGLight GitHub: https://github.com/Bessouat40/RAGLight
📦 Install: pip install raglight
💡 Perfect for developers, researchers, and anyone building context-aware AI solutions!
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Hastags:
#RAG, #RAGLight, #AI, #LLM, #Python, #Ollama, #Mistral, #Gemini , #OpenAI, Langchain

TimeStamp:
0:00 Intro
1:22 RagLight
1:55 Ubuntu Startup
2:20 Chat
4:40 Inference
5:20 Vscode example
7:55 Conclusion