Multiple-RAG-Systems

🚀 Multiple-RAG-Systems is a comprehensive collection of Retrieval-Augmented Generation (RAG) implementations, designed for various levels of complexity. It features basic RAGs, advanced RAGs, and a simple RAG implementation using LlamaIndex. This repository serves as a go-to resource for developers exploring different RAG techniques using LangChain, LangGraph, Firecrawl, Crawl4AI, Qdrant, Groq, Mistral, Tavily Search, and more.

📌 Folder Structure

1️⃣ Basic RAGs

The Basic RAGs folder contains foundational RAG implementations tailored for different use cases:

📂 DocLens

• rag_streamlit.py – A document-based RAG system with a Streamlit UI for easy interaction.

📂 FindWise

• search_rag.py – A search-based RAG that retrieves information from the internet and generates responses.

📂 WebFusion

• rag_webbased.py – A URL-based RAG that processes and generates responses from web content.

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2️⃣ Advanced RAGs

The Advanced RAGs folder contains sophisticated implementations in Jupyter notebooks:

📜 agentic_rag.ipynb

• Implements an Agentic RAG using LangChain and LangGraph with tool integration.

📜 agentic_rag_langgraph.ipynb

• Advanced Agentic RAG with reranking, multiquery retriever, and Tavily Search using LangChain and LangGraph.

📜 corrective_rag.ipynb

• Checks document relevance and, if insufficient, modifies the query and performs a web search for better results.

📜 fusion_rag_firecrawl_crawl4ai.ipynb

• Implements multi-retriever fusion RAG using Firecrawl and Crawl4AI, stores results in Qdrant, and generates responses using Mistral.

📜 github_Loader_and_rag_evaluation.ipynb

• Demonstrates a GitHub Loader in LangChain and evaluates RAG outputs using Opik, Ragas, and Giskard.

📜 rag_reranking_techniques.ipynb

• Showcases multiple reranking techniques for improving RAG responses.

📜 speculative_rag.ipynb

• Generates multiple responses using multi-query techniques and selects the best one through scoring.

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3️⃣ RAG with LlamaIndex

The With LlamaIndex folder contains a simple RAG implementation:

📜 naive_rag_llama_index.ipynb

• A lightweight RAG setup using LlamaIndex for retrieval and response generation.

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🚀 Getting Started

1️⃣ Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/rag-library.git
cd rag-library

## 📜 License

This project is licensed under the **MIT License**.

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🚀 **RAG-Library** – Powering intelligent and scalable Retrieval-Augmented Generation workflows!