Rag-Powered Ai: Build A Chatbot Inpython, Langchain & Ollama

Learn to Build an AI-Powered PDF Q&A Chatbot with RAG, Ollama, LangChain, and Vector Embeddings in Python

What you'll learn

Understand Retrieval-Augmented Generation (RAG) – Learn how RAG improves LLM responses by combining real-world data with AI-generated text.

Build a PDF Q&A Chatbot – Develop a working chatbot that extracts and retrieves relevant information from a PDF using LangChain, Ollama

Implement Vector Embeddings & Semantic Search – Generate vector embeddings for document text and use a local database for information retrieval

Run Local AI Models with Ollama – Set up and interact with local large language models (LLMs) like Mistral and Llama3 to generate AI-driven responses.

Requirements

Basic Python Knowledge – Familiarity with Python programming (variables, functions, and loops).

Fundamental Understanding of AI/LLMs – Some exposure to large language models (LLMs) and AI concepts is helpful but not required.

VS Code & Command Line Basics – Ability to install and run Python packages using the terminal or command prompt.

No Prior Experience with LangChain or Ollama Needed – The course covers these tools from scratch.

Description

Course Description:Welcome to "Building a RAG Application with Ollama, LangChain, and Vector Embeddings in Python"! This hands-on course is designed for Python developers, data scientists, and AI enthusiasts looking to dive into the world of Retrieval-Augmented Generation (RAG) and learn how to build intelligent document-based applications.In this course, you will learn how to create a powerful PDF Q&A chatbot using state-of-the-art AI tools like Ollama, LangChain, and Vector Embeddings. You'll gain practical experience in processing PDF documents, extracting and generating meaningful information, and integrating a local Large Language Model (LLM) to provide context-aware responses to user queries.What you will learn:What is RAG (Retrieval-Augmented Generation) and how it enhances the power of LLMsHow to process PDF documents using LangChainExtracting text from PDFs and splitting it into chunks for efficient retrievalGenerating vector embeddings using semantic search for better accuracyHow to query and retrieve relevant information from documents using Vector DBIntegrating a local LLM with Ollama to generate context-aware responsesPractical tips for fine-tuning and improving AI model responsesCourse Highlights:Step-by-step guidance on setting up your development environment with VS Code, Python, and necessary libraries.Practical projects where you’ll build a fully functional PDF Q&A chatbot from scratch.Hands-on experience with Ollama (a powerful tool for running local LLMs) and LangChain (for document-based AI processing).Learn the fundamentals of vector embeddings and how they improve the search and response accuracy of your AI system.Build your skills in Python and explore how to apply machine learning techniques to real-world scenarios.By the end of the course, you'll have the skills to build and deploy your own AI-powered document Q&A chatbot. Whether you are looking to implement AI in a professional setting, develop your own projects, or explore advanced AI concepts, this course will provide the tools and knowledge to help you succeed.Who is this course for?Python Developers who want to integrate AI into their projects.Data Scientists looking to apply RAG-based techniques to their workflows.AI Enthusiasts and learners who want to deepen their knowledge of LLMs and AI tools like Ollama and LangChain.Beginners interested in working with AI and machine learning to build real-world applications.Get ready to dive into the exciting world of AI, enhance your Python skills, and start building your very own intelligent PDF-based chatbot!

Overview

Section 1: Introduction to RAG and Course Overview

Lecture 1 Introduction

Lecture 2 How RAG improves LLM responses with real data

Lecture 3 Why use Ollama + LangChain + Vector Embeddings?

Lecture 4 Overview of the project: Building a PDF-based RAG chatbot

Section 2: Setting Up the Development Environment

Lecture 5 Installing VS Code, Python, and Pip

Lecture 6 Installing Ollama for Local LLMs

Lecture 7 Setting up a Python Virtual Environment

Lecture 8 Installing required libraries:

Section 3: Running Ollama Locally

Lecture 9 What is Ollama, and how does it work?

Lecture 10 Downloading & running LLMs like Phi Mistral, Llama3

Section 4: Loading a PDF Document into LangChain

Lecture 11 Using PyPDF to extract text from PDFs

Section 5: Generating Vector Embeddings & Storing Data

Lecture 12 Purpose of Chunks and Overlap

Lecture 13 What are embeddings, and why do we need them?

Lecture 14 Choosing an embedding model (OpenAI vs Local models)

Lecture 15 Generating vector representations for document text

Section 6: Retrieving Information from the PDF (RAG in Action)

Lecture 16 Querying user input against the PDF document

Lecture 17 Fetching relevant document chunks using semantic search

Lecture 18 Passing the retrieved context to Ollama’s LLM

Section 7: Final Project - Build a PDF Q&A Chatbot

Lecture 19 Project Overview: Interactive chatbot for PDF document Q&A

Section 8: Course Wrap-Up & Next Steps

Lecture 20 Summary of key concepts

Lecture 21 RAG Use Case with benefits

Lecture 22 RAG Use Case Summary

Lecture 23 RAG-vs-LLM-Unveiling-the-AI-Powerhouses

Python developers interested in AI and LLM-powered applications.,Data scientists & ML engineers exploring Retrieval-Augmented Generation (RAG).,Tech enthusiasts & AI beginners who want to build AI-driven document Q&A systems.,Students & researchers looking to extract insights from large PDF documents using AI.