| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ~Get Your Files Here ! | |||
| 1 - Introduction & Course Overview | |||
| 1. Introduction.mp4 | 31.2 MB | ||
| 2. What is AI.mp4 | 132 MB | ||
| 3. ChatGPT vs Claude vs Google AI Studio.mp4 | 14.1 MB | ||
| 4. Understanding AI Prompts.mp4 | 41.5 MB | ||
| 5. What We’re Building.mp4 | 11.5 MB | ||
| 6. Downloading the Source Code.html | 819.2 B | ||
| 6. Downloading the Source Code_Resource_python venv step by step guide pdf.pdf | 6.1 KB | ||
| streamlit-rag-app-main | |||
| app.py | 16.4 KB | ||
| devcontainer | |||
| 2 - Understanding RAG Systems | |||
| 3 - Project Setup & Requirements | |||
| 10. What is Python.html | 2.6 KB | ||
| 11. Installing Python.mp4 | 44.6 MB | ||
| 12. What are virtual environments.mp4 | 11.6 MB | ||
| 13. Creating and activating a Virtual Environment on Windows.mp4 | 40.7 MB | ||
| 14. Creating a Virtual Environment on multiple Os.html | 5.2 KB | ||
| 14. Creating a Virtual Environment on multiple Os_Resource_python venv step by step guide pdf.pdf | 6.1 KB | ||
| 15. Updating pip in virtual environment.mp4 | 10.3 MB | ||
| 16. Installing Visual Studio Code Editor.mp4 | 67.6 MB | ||
| 17. Opening project in visual studio code.mp4 | 7.4 MB | ||
| 18. Understanding requirements txt.mp4 | 34.8 MB | ||
| 19. Getting Your OpenAI API Key.mp4 | 30 MB | ||
| 4 - Building Document Readers & Core RAG Functions | |||
| 20. Imports and Configuration.mp4 | 21.1 MB | ||
| 21. OpenAI API Key Setup.mp4 | 20.4 MB | ||
| 22. Creating document reader functions.mp4 | 67.9 MB | ||
| 23. Creating Core RAG Functions.mp4 | 62.7 MB | ||
| 5 - Building the Streamlit Interface | |||
| 24. Creating Streamlit User Interface.mp4 | 22.5 MB | ||
| 25. Document Processing Flow Part 1.mp4 | 36.3 MB | ||
| 26. Document Processing Flow Part 2.mp4 | 55 MB | ||
| 27. Building the main chat interface.mp4 | 71.7 MB | ||
| 6 - Testing Your RAG System | |||
| 28. Testing the RAG System.mp4 | 59.2 MB | ||
| 7 - Deployment to the Internet | |||
| 29. Introduction to Deployment.mp4 | 4.4 MB | ||
| 30. Creating the gitignore File.mp4 | 43.7 MB | ||
| 31. Creating a GitHub Account.mp4 | 63.1 MB | ||
| 32. Create a Streamlit Account.mp4 | 20.3 MB | ||
| 33. Creating a New Repository.mp4 | 25.2 MB | ||
| 34. Uploading Files via Web Interface.mp4 | 38.7 MB | ||
| 35. Deploying to Streamlit Cloud.mp4 | 30 MB | ||
| 7. What is RAG.mp4 | 38.6 MB | ||
| 8. How RAG Works.mp4 | 19.1 MB | ||
| 9. Why RAG Beats Fine Tuning.html | 307.2 B | ||
| devcontainer.json | 1 KB | ||
| gitignore | 307.2 B | ||
| requirements.txt | 204.8 B |
Build Your Own RAG System with Python, Streamlit & OpenAI
https://WebToolTip.com
Published 12/2025
Created by Bluelime Learning Solutions
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 35 Lectures ( 2h 5m ) | Size: 1.14 GB
Master Retrieval-Augmented Generation: Build, & Deploy a Complete AI-Powered Document Chat Application from Scratch
What you'll learn
Understand how text embeddings convert human language into numerical vectors that capture semantic meaning, enabling similarity-based search
Describe the complete RAG pipeline including the five key stages.
Explain what Retrieval-Augmented Generation (RAG) is and articulate why it's superior to fine-tuning for document-based question answering applications
Set up a professional Python development environment with virtual environments to isolate project dependencies
Create and manage a requirements.txt file to document and install project dependencies efficiently
Securely manage sensitive credentials like API keys using environment variables and Streamlit's secrets management system
Read and extract text content from various document formats such as PDF and TXT.
Chunk large documents into smaller segments suitable for retrieval.
Generate embeddings using the OpenAI API for semantic search.
Store and index embeddings efficiently using a vector database.
Execute similarity searches to retrieve relevant document chunks.
Build core RAG logic that connects retrieval and generation into a working pipeline.
Create an interactive Streamlit application for document chat functionality.
Upload documents and ask questions that return grounded and cited answers
Test the RAG application using real-world documents.
Deploy a working RAG system to Streamlit Cloud for public access.
Requirements
Basic computer literacy (file navigation, copy/paste, typing)
A computer running Windows, macOS, or Linux
Internet access for using the OpenAI API and deployment tools
A free OpenAI account to obtain an API key
Basic programming concepts are beneficial but not mandatory
No prior AI or Python experience is necessary.
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