Dive into the world of advanced language understanding with Advanced_RAG. These Python notebooks offer a guided tour of Retrieval-Augmented Generation (RAG) using the Langchain framework, perfect for enhancing Large Language Models (LLMs) with rich, contextual knowledge.

Architecture Flows

Basic RAG :

Understand the journey of a query through RAG, from user input to the final generated response, all depicted in a clear, visual flow.

Advanced RAG Techniques :

Explore the intricate components that make up an advanced RAG system, from query construction to generation.

02. Multi Query Retriever :

Get to grips with the Multi Query Retriever structure, which enhances the retrieval process by selecting the best responses from multiple sources.

06. Self-Reflection-RAG :

07. Agentic RAG :

08. Adaptive Agentic RAG :

09. Corrective Agentic RAG :

10. LLAMA 3 Agentic RAG Local:

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.

Enhance your LLMs with the powerful combination of RAG and Langchain for more informed and accurate natural language generation.