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.