NisaarAgharia/Advanced_RAG
A collection of Jupyter notebooks demonstrating advanced RAG (Retrieval-Augmented Generation) techniques using LangChain and LLMs.
Not currently ranked — collecting fresh signals.
star history
This repository provides practical tutorials on building RAG systems, covering techniques like multi-query retrieval, self-reflection, and agentic RAG. It uses LangChain as the primary framework, integrates vector databases for document retrieval, and supports multiple LLMs including OpenAI GPT and Meta LLAMA 3. The notebooks demonstrate how to enhance LLM responses by combining retrieval mechanisms with generation, including advanced agent-based approaches for more robust AI systems.
Frequently asked
- What is NisaarAgharia/Advanced_RAG?
- A collection of Jupyter notebooks demonstrating advanced RAG (Retrieval-Augmented Generation) techniques using LangChain and LLMs.
- Is Advanced_RAG open source?
- Yes — NisaarAgharia/Advanced_RAG is an open-source project tracked on heatdrop.
- What language is Advanced_RAG written in?
- NisaarAgharia/Advanced_RAG is primarily written in Jupyter Notebook.
- How popular is Advanced_RAG?
- NisaarAgharia/Advanced_RAG has 481 stars on GitHub.
- Where can I find Advanced_RAG?
- NisaarAgharia/Advanced_RAG is on GitHub at https://github.com/NisaarAgharia/Advanced_RAG.