Build-RAG-Agent-with-LLM

This repository contains the implementation of a Retrieval-Augmented Generation (RAG) agent using Large Language Models (LLMs). RAG agents combine the power of information retrieval with text generation, enabling applications such as intelligent question-answering systems, conversational agents, and more.

If you're interested in a detailed, step-by-step explanation of how this project was built, including code walkthroughs and in-depth analysis, do check out My Medium blog post- "Build RAG Agents using LLMs: Step-by-step Guide".

Features

• Retrieval Component: Efficiently retrieves relevant information from a large corpus.
• Generation Component: Generates context-aware responses using a fine-tuned LLM.
• Ranking Component: Ranks generated responses to select the most relevant one.
• Training and Fine-Tuning: Easily fine-tune pre-trained LLMs for your specific use case.
• Deployment Ready: Integrate the RAG agent into your applications for practical use.

Install the dependencies

    git clone https://github.com/SreeEswaran/Build-RAG-Agent-with-LLM.git
    cd Build-RAG-Agent-with-LLM
    pip install -r requirements.txt

Usage

1. Run the RAG Agent

   python main.py

2. Finetune the model

   python src/training.py

Contact

If you have any questions, suggestions, or just want to connect, feel free to reach out through the following platforms:

• Topmate-For mentoring, coaching or guidance: Link to my Topmate Profile
• Medium Blogs: Link to my Medium Profile
• LinkedIn: Link to my LinkedIn Profile

Don't forget to follow me for more content and updates!