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Basic Information

Advanced_RAG is a collection of Python Jupyter notebooks that provide a practical, developer-focused guide to building Retrieval-Augmented Generation (RAG) systems using the Langchain framework. The repository documents architecture flows and visual diagrams that show how queries travel from user input through retrieval to final generation. It covers both foundational and advanced RAG patterns, including query construction, Multi Query Retriever approaches, reranking and fusion, routing to multiple datasources, vector database indexing, and agentic RAG designs. Several notebooks explore advanced agent behaviors such as self-reflection, corrective and adaptive agentic flows, and a local LLAMA 3 agent example. The content is intended for engineers, researchers, and practitioners who want hands-on examples and conceptual diagrams to implement and experiment with RAG pipelines.

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Features
The repo groups detailed notebooks each focused on a specific capability: an introduction to RAG, query transformations, routing to datasources, indexing to various VectorDBs, and retrieval mechanisms like reranking and fusion. It highlights a Multi Query Retriever pattern and includes visual architecture flows. Advanced notebooks demonstrate Self-Reflection-RAG, Agentic RAG, Adaptive Agentic RAG, and Corrective Agentic RAG. There is a notebook showing a local LLAMA 3 agent integration. The materials emphasize practical, stepwise examples and diagrams, making it easy to follow implementations in Langchain and to experiment with vector indexing, retrieval tuning, and agentic orchestration.
Use Cases
This repository helps practitioners learn to enhance LLM outputs with relevant external knowledge by combining retrieval and generation. The notebooks walk users through building end-to-end RAG pipelines, transforming queries for better retrieval, routing requests to appropriate data sources, and creating VectorDB indexes. Advanced notebooks show how to add self-reflection, corrective loops, and agentic behaviors to improve answer quality and robustness. The LLAMA 3 local notebook demonstrates running agentic RAG setups locally. Overall, the content supports hands-on experimentation, accelerates prototyping of retrieval strategies in Langchain, and provides architectural guidance for production-ready RAG designs.

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