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Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

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NisaarAgharia/Advanced_RAG

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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.

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. RAG_User_Flow

Advanced RAG Techniques :

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

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. MQR

06. Self-Reflection-RAG :

self-Rag

07. Agentic RAG :

download

08. Adaptive Agentic RAG :

adaptive_rag_agent

09. Corrective Agentic RAG :

correctiveRAG

10. LLAMA 3 Agentic RAG Local:

LLAMA3_AGent

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.

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Advanced Retrieval-Augmented Generation (RAG) through practical notebooks, using the power of the Langchain, OpenAI GPTs ,META LLAMA3 ,Agents.

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