RAG with Python Build Chatbots That Talk to Your Data

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RAG with Python Build Chatbots That Talk to Your Data
Published 7/2026
Created by School of AI, Arjun Vaid
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 45 Lectures ( 10h 4m ) | Size: 5.9 GB

Build PDF Chatbots, Semantic Search Engines, Vector Databases, and Enterprise AI Assistants with Python
What you'll learn
Understand how Retrieval-Augmented Generation, or RAG, works and when to use it instead of relying only on a large language model.
Build Python applications that allow users to ask questions about their own documents and data.
Extract, clean, and process content from PDF, text, Markdown, and CSV files.
Split documents into effective chunks while preserving useful metadata such as filenames, headings, and page numbers.
Generate text embeddings and store them in vector databases such as ChromaDB or FAISS.
Build semantic search systems that retrieve information based on meaning instead of exact keyword matches.
Create a PDF chatbot that answers questions and displays supporting sources and page citations.
Improve retrieval quality using metadata filters, similarity thresholds, hybrid search, query rewriting, and reranking.
Build conversational RAG applications that understand follow-up questions and maintain chat history.
Reduce hallucinations by creating grounded prompts, evidence-based responses, and insufficient-information fallbacks.
Build a multi-document enterprise knowledge assistant for departments such as HR, IT, finance, and operations.
Add role-based access controls so users retrieve only the documents they are permitted to view.
Evaluate RAG applications using retrieval accuracy, answer relevance, groundedness, citation quality, and response latency.
Build an evaluation and monitoring dashboard for reviewing failed questions and improving application performance.
Create and deploy a complete Streamlit-based Enterprise Knowledge and Research Copilot as a capstone project.
Requirements
Basic Python knowledge, including variables, functions, loops, lists, dictionaries, and importing packages, is helpful.
No previous experience with Retrieval-Augmented Generation, vector databases, embeddings, or LangChain is required.
No advanced machine learning or mathematics knowledge is required.
A computer running Windows, macOS, or Linux is required.
Python 3.10 or later should be installed.
A code editor such as Visual Studio Code, PyCharm, or Jupyter Notebook is recommended.
Learners should be comfortable installing Python packages and running basic commands in a terminal.
An internet connection may be required to install packages, download models, or access hosted AI services.
Learners may use a hosted language model API or a local model through Ollama, depending on their preferred setup.
Sample documents and starter resources can be provided during the course, so learners do not need to prepare their own dataset.
Curiosity and a willingness to build practical projects are the most important Requirements.
Description
Learn how to build powerfulRetrieval-Augmented Generation applications with Python in this practical, project-based course. You will createPDF chatbots,semantic search engines,vector database applications, and a completeenterprise knowledge assistant that can answer questions using your own documents and private data.
Large language models are impressive, but they often produce outdated, unsupported, or inaccurate answers.Retrieval-Augmented Generation, commonly known asRAG, solves this problem by connecting an AI model to external knowledge sources. Instead of depending only on the model's built-in knowledge, a RAG application retrieves relevant information from your documents and uses that information to generate a more accurate, grounded response.
Throughout this course, you will learn the complete workflow for buildingRAG chatbots with Python. You will start by understanding the core architecture of a RAG system, including document ingestion, text extraction, chunking, embeddings, retrieval, prompt construction, and answer generation.
You will build a fully functionalPDF chatbot that allows users to upload documents and ask natural-language questions about their content. You will learn how to extract text from PDFs, preserve page numbers, clean document content, create overlapping text chunks, and return answers with supporting citations.
The course also coverstext embeddings,vector search, andvector databases such asChromaDB andFAISS. You will learn how to convert document chunks into numerical vectors, store those vectors, perform similarity searches, and retrieve information based on meaning instead of exact keyword matches.
As your skills grow, you will explore advancedsemantic search techniques, including metadata filtering, similarity thresholds, query rewriting, hybrid search, keyword retrieval, and result reranking. These techniques will help you improve retrieval accuracy and build more reliable AI applications.
You will also create aconversational RAG chatbot that remembers previous messages, understands follow-up questions, retrieves fresh evidence for every response, and clearly separates conversational memory from document knowledge. You will add citations, confidence indicators, insufficient-evidence responses, and practical guardrails to reduce hallucinations and unsupported claims.
For the enterprise section of the course, you will build a multi-documententerprise knowledge assistant for departments such as HR, IT, finance, operations, and compliance. You will organize documents using metadata, create department-specific collections, manage document versions, support multiple file formats, and implement role-based access controls.
You will also learn how to evaluate and improve your RAG system using metrics such as retrieval relevance, groundedness, answer relevance, citation accuracy, and response latency. You will build aRAG evaluation dashboard for testing questions, reviewing retrieved sources, identifying failed answers, and comparing different retrieval configurations.
By the end of the course, you will complete a portfolio-readyEnterprise Knowledge and Research Copilot usingPython,Streamlit,embeddings,vector databases, and moderngenerative AI techniques.
This course is ideal for Python developers, AI beginners, data professionals, freelancers, startup founders, and anyone interested in buildingAI chatbots that talk to your data.
Who this course is for
Python developers who want to build AI applications that can answer questions from private or custom data.
Beginners in generative AI who want a practical introduction to RAG, embeddings, semantic search, and vector databases.
Software developers who want to add document-question-answering features to websites, internal tools, or SaaS products.
Data analysts and data professionals who want to create natural-language interfaces for reports, documents, and business data.
AI and machine learning students who want to move from theoretical concepts to portfolio-ready applications.
Business automation professionals who want to build internal knowledge assistants for HR, IT, finance, operations, or customer support.
Freelancers and consultants who want to offer document-chatbot and enterprise-search solutions to clients.
Startup founders and product builders who want to create AI assistants that work with company-specific knowledge.
Technical instructors and educators who want to build AI study assistants, research tools, or searchable course-resource applications.
Anyone interested in building PDF chatbots, semantic search engines, conversational RAG systems, or enterprise knowledge assistants with Python.
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