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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.

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

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

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

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.