Being-Creative-with-AI/C4/Setting-up-the-RAG-environment-in-Linux/English

Title of the Script: Setting up the RAG environment in Linux.

Author: EduPyramids

Keywords: RAG, Python, Linux, Ubuntu, LangChain, ChromaDB, Embeddings, Vectors, Retrieval, AI, Terminal, pip, Search, Environment, EduPyramids, video tutorial.

Visual Cue	Narration
Slide 1 Title Slide	Welcome to this Spoken Tutorial on Setting up the RAG environment in Linux.
Slide 2 Learning Objective	In this tutorial, we will learn how to: Create a working environment to build a RAG system.
Slide 3 Disclaimer Slide As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.	As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance.
Slide 4 System Requirements	To record this tutorial, I am using: Ubuntu 24.04 LTS Firefox version 148.0.2 Learners will also need a working internet connection
Slide 5 Prerequisites https://EduPyramids.org	To follow this tutorial, * Basic understanding of Python will be helpful, but not mandatory For the Prerequisites of this tutorial, visit the website shown on your screen
Slide 6 Code files The following code file is required to practice this tutorial re-command.txt This file is provided in the Code Files link of this tutorial page Please download and extract the file.	The following code file is required to practice this tutorial. This file is provided in the Code Files link of this tutorial page. Please download and extract the file.
	Let us begin.
Press Ctrl + Alt + T keys simultaneously.	Press Ctrl, Alt, and T keys simultaneously to open the terminal.
Type: python3 --version and press Enter	In the terminal window, type this command and press Enter. The Python version is displayed.
Highlight Python version.	Ubuntu usually comes with Python pre-installed by default. In this tutorial, my system is using Python 3.12.3 If your system does not have Python installed, please refer to the additional sheet.
Type: sudo apt update and press Enter.	Now in the terminal, type this command and press Enter.
Enter the administrative password if prompted.	Enter the administrative password if prompted.
Highlight: sudo apt update	This command refreshes the list of available packages from Ubuntu’s repositories
Type: sudo apt install python3 python3-venv python3-pip	Now type this command carefully
Highlight: sudo apt install python3 python3-venv python3-pip Now press Enter	This installs Python, pip, and packages needed for virtual environments. Now press Enter
Press Y to continue and press Enter	Terminal asks Do you want to continue? Press Y to continue and press Enter This may take some time. The installation is now completed.
	Now let us create a new project folder.
Type : mkdir rag_project and press enter	Type this command and press Enter
Highlight the folder in the home directory.	A new folder is created in the home directory
Type: cd rag_project and press Enter	Switch back to the terminal. Type: cd rag_project and press Enter to go inside the folder.
Highlight the prompt.	Notice that the prompt changes to the project directory.
	Let us now create a virtual environment.
Type: python3 -m venv venv and press Enter.	Type this command and press Enter.
	A virtual environment isolates project dependencies. It prevents conflicts with system-wide packages.
Type: source venv/bin/activate and press Enter.	Now let us activate the virtual environment. Type this command and press Enter.
Cursor on the Prompt change	The environment name appears in the terminal, indicating its activation.
Terminal	Now we need to install the required libraries.
Type: pip install pandas chromadb langchain langchain-community sentence-transformers and press Enter Highlight LangChain Highlight Sentence-transformers Highlight ChromaDB	Pause the tutorial and type this command carefully and press Enter. This command installs all libraries required for the RAG environment setup. LangChain manages the retrieval workflow in the RAG pipeline. Sentence-transformers convert text into embeddings. Embeddings represent the meaning of text as vectors for retrieval. ChromaDB stores embeddings for similarity search and retrieval.
Installation progress	This may take some time. Python is installed, and the required libraries are configured Now the setup is complete.
	The system is now ready for building a RAG application.
	With this, we come to the end of this tutorial.
Slide 7 Summary In this tutorial, we learnt how to:* Create a working environment to build a RAG system.	In this tutorial, we learnt how to:* Create a working environment to build a RAG system.
Slide 8 Acknowledgement Domain Inputs: Bhavani Shankar R and Saisudha Sugavanam Script Writer: Ketki Naina Admin Reviewer: Arthi Varadarajan Quality Reviewer: Sakina Sidhwa Novice Reviewer: Misbah Samir AI Narration: Debosmita Mukherjee Screen Recording: Ketki Naina Video Editor: Arvind Pillai Web Developer: Ankita Singhal	Thank you for joining.
Slide 9 Acknowledgement This Spoken Tutorial is brought to you by EduPyramids Educational Services Private Limited at SINE, IIT Bombay.

Contributors and Content Editors

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