Dive into Advanced Language Understanding with Advanced_RAG

Welcome to the exciting world of Retrieval-Augmented Generation (RAG)! In this article, we will take you on a guided tour of using the RAG framework through Python notebooks, specifically tailored for enhancing Large Language Models (LLMs) with rich, contextual knowledge. If you’re ready to explore how advanced techniques can elevate your AI applications, let’s get started!

Understanding the Architecture Flows

The architecture of RAG is a fascinating journey, much like a beautifully crafted story, each part leading to a thrilling conclusion. This journey can be broken down into a few key components:

Basic RAG

Picturing RAG is like visualizing a travel itinerary for your queries. Each question posed by the user follows its path until it arrives at the final answer. The flow is depicted in a clear, visual format, enabling a better understanding of the process.

![RAG_User_Flow](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/22457544dc390fc3-5c41-4c8e-b16e-268606a8f4ed)

Advanced RAG Techniques

In this section, we delve into the complex parts that form an advanced RAG system. Just as a movie has multiple scenes that build towards a climax, the RAG system includes various processes such as query construction and document generation for optimal results.

![Advanced RAG Components](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/22457544281e8c66-a33f-485f-ad75-e8d450ccba98)

Multi Query Retriever

The Multi Query Retriever is like a skilled chef who chooses the best ingredients from a variety of sources to enhance the flavor of a dish. In the same way, this feature enhances the retrieval process by selecting the most relevant responses from multiple sources.

![MQR](https://github.com/NisaarAgharia/RAG_From_Scratch/assets/224575445c0db3f0-59e4-4278-af6f-4120a3bb5637)

Advanced RAG Techniques: Beyond Basics

As we progress through the world of RAG, we encounter various advanced techniques:

• Self-Reflection-RAG: A system that can introspect its outputs and enhance its learning based on retrieved documents.

![self-Rag](https://github.com/NisaarAgharia/Advanced_RAG/assets/224575442e58751b-c986-4137-8f85-9294301c3f79)

• Agentic RAG: This approach adds a layer of behaviour to the RAG system, adjusting responses based on learned behavior.

![download](https://github.com/NisaarAgharia/Advanced_RAG/assets/224575444258e17e-7dfa-48da-a5b5-753b3de5d1bc)

• Adaptive Agentic RAG: This technique fine-tunes the agentic behavior dynamically based on context, making responses even more relevant.

![adaptive_rag_agent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544283a734d-bd00-4431-8982-fc5e6ce8f15c)

• Corrective Agentic RAG: A system that learns from its mistakes and adjusts accordingly for better accuracy.

![correctiveRAG](https://github.com/NisaarAgharia/Advanced_RAG/assets/2245754468968fa8-0b0e-46ca-a80e-b30645b1e31b)

• LLAMA 3 Agentic RAG Local: This model offers local processing capabilities, allowing greater flexibility.

![LLAMA3_AGent](https://github.com/NisaarAgharia/Advanced_RAG/assets/22457544a9408eea-814f-416e-a8f6-aec361410719)

Notebooks Overview

Below is a detailed overview of each notebook present in this repository:

• 01_Introduction_To_RAG.ipynb – Basic process of building RAG app(s)
• 02_Query_Transformations.ipynb – Techniques for Modifying Questions for Retrieval
• 03_Routing_To_Datasources.ipynb – Create Routing Mechanism for LLM to select the correct data Source
• 04_Indexing_To_VectorDBs.ipynb – Various Indexing Methods in the Vector DB
• 05_Retrieval_Mechanisms.ipynb – Reranking, RAG Fusion, and other Techniques
• 06_Self_Reflection_Rag.ipynb – RAG that has self-reflection self-grading on retrieved documents and generations.
• 07_Agentic_Rag.ipynb – RAG that has agentic Flow on retrieved documents and generations.
• 08_Adaptive_Agentic_Rag.ipynb – RAG that has adaptive agentic Flow.
• 09_Corrective_Agentic_Rag.ipynb – RAG that has corrective agentic Flow on retrieved documents and generations.
• 10_LLAMA_3_Rag_Agent_Local.ipynb – LLAMA 3 8B Agent Rag that works Locally.

Enhancing Your LLMs

With the powerful combination of RAG and Langchain, you are set to unlock the full potential of your LLMs, leading to more informed and accurate natural language generation. The path to advanced understanding of language models is one filled with innovation and exploration.

Troubleshooting Tips

If you encounter any issues while diving into RAG, here are a few troubleshooting suggestions:

• Make sure you have all required libraries installed in your Python environment.
• Check for any syntax errors in your notebooks.
• Review the documentation of individual techniques if you’re unsure how to implement them.
• For complex issues, consider reaching out in forums or communities for help.

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