Build Local AI Apps with Ollama, Python, and Streamlit

Build private, production-ready AI apps locally with Ollama, Python, Streamlit, RAG, memory, tools, and real projects

Created bySchool of AI, Arjun Vaid

Last updated 7/2026

English

English [Auto],

What you'll learn

Install Ollama and run large language models locally on a computer.
Connect Python applications to Ollama models.
Build interactive AI applications with Streamlit.
Create chatbots with memory, personas, and conversation history.
Process PDF and text documents for local AI workflows.
Generate embeddings and build semantic search with ChromaDB.
Create Retrieval-Augmented Generation applications with citations.
Generate structured JSON outputs, summaries, flashcards, and quizzes.
Build tool-using AI applications and research workflows.
Evaluate, optimize, and manage production-ready local AI applications.

Course content

7 sections • 45 lectures • 7h 50m total length

Demo: Local Factory Operations Assistant9:25
Local AI versus cloud-based AI3:36
Compare local AI and cloud-based AI to weigh privacy, control, offline use, cost, and scalability, and learn when each deployment fits your app and users.
Installing Ollama and managing local models3:54
Install and run local LLMs with Ollama, verify the setup, pull and test models, and manage your local model library to power Python or Streamlit apps efficiently.
Connecting Python applications to Ollama4:19
Use Python as the bridge to Ollama, sending prompts to a local model via a rest api and handling the responses to build private, on-device ai tools.
Prompt design, system instructions, and model parameters4:13
Design clear prompts for local AI models by defining task, context, constraints, and output format; control behavior with system instructions and parameters like temperature and max tokens.
Building basic Streamlit AI interfaces5:25
Turn local AI models into browser-based apps with Streamlit and Python, using input widgets and session state to deliver clear, responsive AI interfaces.
Hands-on Lab: Local Factory Operations Assistant27:12
Launch a local factory operations assistant in Streamlit, defaulting to Olama with an optional OpenAI cloud path, and configure prompts, model service, and data helpers for grounding factory context.

Demo: Private Hotel Guest-Service Chatbot5:55
Build a private hotel guest-service chatbot with Ollama, Python, and Streamlit, exploring memory, prompts, and multi-turn conversations, including preferences, service requests, and chat export in a hands-on lab.
System, user, and assistant messages4:09
learn how system, user, and assistant messages structure conversations to improve chatbot behavior, predictability, and memory, with clear roles, context, and best practices.
Multi-turn conversation management4:01
Master multi-turn conversation management by maintaining conversation history, system instructions, and user preferences to keep responses connected. Learn to pass context, manage memory within token limits, and avoid common mistakes.
Streamlit session state4:03
Master streamlit session state to keep chat history and user settings alive across reruns, ensuring a persistent, responsive AI interface that remembers models, temperatures, and choices.
Chatbot personas and response behavior4:18
Explore how chatbot personas define tone, style, and behavior, guided by system instructions, to create consistent, user-friendly responses.
Saving, resetting, and exporting conversations4:24
Hands-on Lab: Private Hotel Guest-Service Chatbot34:45

Demo: Legal Contract Clause Finder5:02
Extracting text from PDF and text documents4:17
Turn raw PDFs and text files into clean text with a Python extraction pipeline, enabling semantic search, document Q&A, and retrieval-augmented generation through accurate metadata.
Cleaning and preparing document content4:23
Chunking strategies and metadata management4:20
Explore chunking strategies (fixed-size, overlapping, and semantic) paired with metadata management to improve retrieval, citations, and traceability in local AI apps.
Local embeddings and semantic similarity3:54
Learn how embeddings turn text into vectors to enable semantic search and meaning-based retrieval on your machine, using local embeddings and a vector store, with cosine similarity.
Storing and searching documents with ChromaDB4:08
Hands-on Lab: Legal Contract Clause Finder59:49
Develop a local AI app in a hands-on lab that builds a legal contract clause finder using Ollama, Python, and Streamlit.

Understanding the Retrieval-Augmented Generation pipeline4:01
Define and implement retrieval augmented generation (rag) to ground answers with retrieved evidence. Learn the two-phase pipeline: prepare knowledge base with documents, chunks, embeddings, and a retriever-driven grounded prompt.
Retrieving relevant document chunks4:17
Retrieve the strongest evidence by ranking document chunks against the user query, delivering focused, accurate answers while keeping private knowledge local and using similarity based top k selection.
Constructing grounded prompts3:37
Generating answers with citations4:02
Master generating cited AI answers by selecting relevant evidence chunks, drafting with evidence-backed claims, and attaching numbered citations and a source list for verifiability.
Reducing hallucinations and handling unsupported questions4:44
Learn to reduce hallucinations by grounding answers in retrieved evidence, performing an evidence check, and safely refusing when evidence is lacking or contradicts claims.
Hands-on Lab: Private Banking Compliance Assistant43:24
Build a private banking compliance assistant through a hands-on lab using Ollama, Python, and Streamlit to create local ai apps.

Designing reusable prompt templates4:01
Design reusable prompt templates with structured placeholders for role, task, inputs, context, and output format to enable consistent, scalable educational AI outputs across lessons and quizzes.
Generating structured JSON outputs4:26
Summarization and concept explanation4:19
Flashcard and quiz generation4:30
Turn learning content into active practice by generating flashcards and quizzes with an AI learning assistant, producing flashcards, quiz questions, answer keys, and feedback for structured, reusable app data.
Tracking scores, progress, and learning history4:02
Track scores, progress, and learning history to turn practice into a growth system, deliver personalized feedback, and drive better study decisions through a structured learning dashboard.
Hands-on Lab: Aircraft Technician Training Assistant45:01

Creating Python tools for AI applications4:10
Breaking complex questions into research tasks4:13
Searching and retrieving information from multiple sources4:28
Lead a multi-source retrieval workflow for an ai research assistant by clarifying the query and searching across webpages, PDF documents, databases, and internal notes to produce a trustworthy, well-supported answer.
Organizing evidence and tracking references4:00
Organize evidence by capturing each point, linking it to a source with IDs like S1, and recording metadata to feed an evidence matrix for traceable, credible claims.
Generating structured research reports4:23
Hands-on Lab: Local Drug Research Assistant34:52

Structuring multi-page Streamlit applications4:19
Structure multi-page streamlit apps with clear separation into pages, components, services, and data, enabling navigation, session_state-based shared values, and maintainable logic.
Configuration, caching, and performance optimization4:24
Logging, validation, and error handling4:09
Improve local AI apps by implementing logging for visibility, validating inputs and outputs for safety, and applying robust error handling for controlled recovery and clear user guidance.
Evaluating retrieval quality and AI responses4:45
Managing models, documents, users, and vector indexes4:09
Master model management, document management, user management, and vector index management for production local AI apps, with clear ownership, lifecycle, monitoring, and safe rollback strategies.
Hands-on Lab: Retail Operations AI Control Center56:15
Build a retail operations ai control center using Ollama, Python, and Streamlit in a hands-on lab.

Requirements

Basic computer skills and confidence installing software.
A Windows, macOS, or Linux computer capable of running Ollama.
At least 8 GB of RAM; 16 GB or more is recommended.
Sufficient free storage for downloading local AI models.
Basic Python knowledge is helpful but not required.
No previous machine learning or AI experience is necessary.
No paid AI API subscription is required.
A code editor such as Visual Studio Code is recommended.
An internet connection is needed initially to install tools and models.
A willingness to follow practical exercises and experiment with code.

Description

Build powerful, private, and practical AI applications directly on your own computer with Ollama, Python, and Streamlit.

This hands-on course teaches you how to create complete local AI apps without depending on paid AI APIs or sending sensitive information to external cloud services. You will learn how to run large language models locally, connect them to Python, design effective prompts, build interactive user interfaces, and transform simple ideas into useful AI applications.

The course begins with the foundations of local artificial intelligence. You will install Ollama, download and manage local models, adjust model parameters, write system instructions, and connect your models to Python applications. You will then use Streamlit to create clean, browser-based interfaces that make your AI projects easy to use and demonstrate.

As you progress, you will build conversational applications with chat history, memory, customizable personas, session state, conversation exports, and reset controls. You will also learn how to process PDF and text documents, clean and chunk content, generate local embeddings, store vectors in ChromaDB, and perform semantic search across private knowledge.

Next, you will build complete Retrieval-Augmented Generation, or RAG, applications. You will retrieve relevant document sections, construct grounded prompts, generate answers with sources and page references, reduce hallucinations, and handle questions that are not supported by the available documents.

The course also covers structured AI outputs, reusable prompt templates, JSON generation, summarization, flashcards, quizzes, progress tracking, and learning applications. You will then move into tool-using AI, where models can call Python functions, organize research tasks, compare evidence, track references, and produce structured research reports.

Every day includes a practical, industry-focused project. You will build a Factory Operations Assistant, a Hotel Guest-Service Chatbot, a Legal Contract Clause Finder, a Banking Compliance Assistant, an Aircraft Technician Training Assistant, a Drug Research Assistant, and a Retail Operations AI Control Center.

These projects demonstrate how local AI can support manufacturing, hospitality, legal services, financial services, aviation, pharmaceuticals, and retail while improving privacy and data control.

You do not need to be an AI expert to begin. Each concept is explained progressively, with practical examples that connect technical ideas to working applications. Instead of learning isolated theory, you will understand why each component matters, how the pieces work together, and how to troubleshoot common problems. The course helps you build confidence with local models, Python workflows, document pipelines, vector databases, and interactive application development.

By the end of the course, you will understand how to structure multi-page Streamlit applications, manage models and documents, optimize performance, implement logging and validation, evaluate retrieval quality, compare model responses, rebuild vector indexes, and monitor user feedback.

This course is ideal for beginners, Python learners, developers, AI enthusiasts, business professionals, and anyone who wants to build private AI applications, offline AI tools, document Q&A systems, AI chatbots, and production-ready local AI solutions.

Start with one local model, build one simple interface, and finish with a portfolio of seven real-world AI applications you can customize, demonstrate, and expand for your career, portfolio, future business, or organization.

Who this course is for:

Beginners who want to start building practical AI applications.
Python learners looking for real-world AI projects.
Developers who want to build private, local-first AI solutions.
AI enthusiasts interested in Ollama and open local models.
Professionals working with confidential or sensitive documents.
Students building an AI application portfolio.
Business analysts and consultants exploring AI use cases.
Educators creating AI-powered learning and training tools.
Teams evaluating alternatives to cloud-based AI services.
Entrepreneurs who want to prototype AI products without paid APIs.

Build Local AI Apps with Ollama, Python, and Streamlit

What you'll learn

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Course content

Day 1 — Local AI Foundations with Ollama7 lectures • 58min

Day 2 — Conversational AI and Memory7 lectures • 1hr 2min

Day 3 — Document Processing and Semantic Search7 lectures • 1hr 26min

Day 4 — Retrieval-Augmented Generation and Document Q&A6 lectures • 1hr 4min

Day 5 — Structured AI Outputs and Learning Applications6 lectures • 1hr 6min

Day 6 — Tool-Using AI and Research Workflows6 lectures • 56min

Day 7 — Production-Ready Local AI Applications6 lectures • 1hr 18min

Requirements

Description

Who this course is for: