RAG-To-Know
The repository explores various RAG techniques, including implementation guides, use cases, and best practices. Each article is designed to help researchers, developers, and enthusiasts understand and implement RAG systems efficiently.
Stars: 58
RAG-To-Know is a versatile tool for knowledge extraction and summarization. It leverages the RAG (Retrieval-Augmented Generation) framework to provide a seamless way to retrieve and summarize information from various sources. With RAG-To-Know, users can easily extract key insights and generate concise summaries from large volumes of text data. The tool is designed to streamline the process of information retrieval and summarization, making it ideal for researchers, students, journalists, and anyone looking to quickly grasp the essence of complex information.
README:
The repository explores various RAG techniques, including implementation guides, use cases, and best practices. Each article is designed to help researchers, developers, and enthusiasts understand and implement RAG systems efficiently.
Feel free to contact me if you want to contribute further to the repository!
| Article Title | Article Link | Code Implementation |
|---|---|---|
| Simple RAG Implementation with Contextual Semantic Search | Read here | Simple RAG |
| Evaluating RAG with LLM-as-a-Judge: A Guide to Production Monitoring | Read here | LLM-as-a-Judge |
| RAG Reranking to Elevate Retrieval Results | Read here | Reranking |
| Building Guardrails Around Your RAG Pipeline | Read here | Guardrail |
| Improve RAG Output with Enhanced Retrieval Techniques | Read here | Enhance Retrieval |
| Fine-Tuning vs. RAG – Which Reigns Supreme? | Read here | |
| Enhance RAG Accuracy with Corrective-RAG (CRAG) | Read here | CRAG |
| Explainable RAG for More Trustworthy Systems | Read here | Explainable RAG |
| 9 Chunking Strategies to Improve RAG | Read here | Chunking Strategy |
| Creating a Useful Voice-Activated Fully Local RAG System | Read here | Project |
| Implementing Multi-Modal RAG Systems | Read here | Project |
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RAG-To-Know is a versatile tool for knowledge extraction and summarization. It leverages the RAG (Retrieval-Augmented Generation) framework to provide a seamless way to retrieve and summarize information from various sources. With RAG-To-Know, users can easily extract key insights and generate concise summaries from large volumes of text data. The tool is designed to streamline the process of information retrieval and summarization, making it ideal for researchers, students, journalists, and anyone looking to quickly grasp the essence of complex information.
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