rag-pipeline-hub

A comprehensive hub for building and optimizing Retrieval-Augmented Generation (RAG) pipelines. This repository includes RAG-based models, retrieval techniques, and content generation projects, leveraging large language models to combine information retrieval and natural language processing for enhanced knowledge generation.

RAG Pipeline Hub

Welcome to RAG Pipeline Hub, a comprehensive repository for building, exploring, and optimizing Retrieval-Augmented Generation (RAG) systems. This repository combines information retrieval with large-scale generative models to create smarter, more efficient content generation pipelines.

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What is Retrieval-Augmented Generation (RAG)?

RAG is a powerful architecture that enhances the output of generative models by retrieving relevant information from large databases or documents before generating content. By combining retrieval-based search with natural language generation, RAG improves the accuracy and relevance of responses in applications like question-answering, summarization, and content generation.

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Features

• End-to-End RAG Pipelines: Pre-built and customizable pipelines integrating information retrieval systems and generative models.
• Sample Projects: Example projects demonstrating how to implement RAG for different use cases such as Q&A systems, document summarization, and chatbot integrations.
• Retrieval Techniques: Implementations of various retrieval methods including dense, sparse, and hybrid retrieval for improving the quality of retrieved content.
• Optimized Generative Models: Pre-trained large language models fine-tuned for RAG-based tasks, providing enhanced knowledge-grounded generation.
• Tutorials & Documentation: Step-by-step guides to help developers get started with RAG workflows, covering both the retrieval and generation components.

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Repository Structure

rag-pipeline-hub/
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├── data/                      # Sample data and documents used for retrieval
├── notebooks/                 # Jupyter notebooks for RAG experiments and demos
├── models/                    # Pre-trained models and fine-tuning scripts
├── retrieval/                 # Retrieval techniques (e.g., dense, sparse, hybrid)
├── generation/                # Generative model architectures and configurations
├── pipelines/                 # End-to-end RAG pipelines combining retrieval and generation
├── docs/                      # Detailed documentation and tutorials
└── README.md                  # Project overview and instructions