Advanced_RAG
Discover the advanced use of Retrieval-Augmented Generation (RAG) with the Langchain framework for Python, designed to enhance the understanding capabilities of Large Language Models (LLMs). This resource provides insights into essential components such as the Multi Query Retriever and discusses advanced techniques including Agentic RAG variants, which improve contextual knowledge and generate more accurate responses. Explore methods for query transformation, routing to data sources, and indexing within VectorDBs to refine retrieval processes. Understand various RAG systems, ranging from basic models to complex self-reflective and corrective agentic processes, aimed at achieving adaptability and precision in language generation.
