Mistral AI Development: AI with Mistral, LangChain & Ollama

Learn AI-powered document search, RAG, FastAPI, ChromaDB, embeddings, vector search, and Streamlit UI (AI)

Created bySchool of AI

Last updated 2/2026

English

What you'll learn

Set up and configure Mistral AI & Ollama locally for AI-powered applications.
Extract and process text from PDFs, Word, and TXT files for AI search.
Convert text into vector embeddings for efficient document retrieval.
Implement AI-powered search using LangChain and ChromaDB.
Develop a Retrieval-Augmented Generation (RAG) system for better AI answers.
Build a FastAPI backend to process AI queries and document retrieval.
Design an interactive UI using Streamlit for AI-powered knowledge retrieval.
Integrate Mistral AI with LangChain to generate contextual responses.
Optimize AI search performance for faster and more accurate results.
Deploy and run a local AI-powered assistant for real-world use cases.

Course content

6 sections • 20 lectures • 2h 3m total length

Certificate of Completion0:29
Finish the course on udemy, download your udemy certificate, and email it to schoolofaillc at gmail.com for verification and the official school of ai certificate.
What is Mistral AI? Overview of Mistral 7B, Mistral-Instruct, and Mixtral models2:36
Discover Mistral AI's open weight models, including Mistral 7B, Mistral-Instruct, and Mixtral, their transformer dense and MOE architectures, and ideal uses like Q&A, instruction following, and high performance text processing.
What is Ollama? How it enables running LLMs locally1:29
Ulama enables running llms locally with minimal setup, loading models like Mistral and llama, uses memory-efficient quantized models, and offers a built-in server and local API without cloud services.
Why use Ollama for local AI applications? Advantages & privacy benefits1:12
Discover why Ollama enables local ai with privacy and security benefits, offline execution, no api fees, faster responses, and support for retrieval augmented generation with Croma DB and fast api.
How does Mistral AI compare to GPT-4 and LLaMA?2:18
Compare Mistral seven B, Mistral instruct, GPT-4, and Llama 2, detailing parameters, strengths, and limitations, and learn when to choose open-source Mistral for efficient AI chat and reasoning.
Installing Ollama & Running Mistral Locally – Step-by-step setup7:26
Install llama, pull mistral models, and run mistral locally for a quick setup. Learn to run via api, use python json payloads, and verify on the local host.
Up and Running with Python39:29
Learn Python from scratch: install Python, set up a coding space, write basic programs, control flow, data types, loops, lists, functions, and build a number guessing game.

Install and configure Ollama to run Mistral AI locally4:31
Install and configure ollama to run Mistral AI locally. Verify the setup by testing localhost 11434 and pulling Mistral models such as Mistral and Mistral Instruct for local use.
Install required Python libraries5:00
Create and activate a Python virtual environment, then install dependencies from requirements.txt. Explore libraries such as FastAPI, uvicorn, chroma db, PyMuPDF, python-docx, ulama, and Streamlit.
Run a test query to verify Mistral AI is working3:45
Create a Python test script that sends a json payload to the local Mistral API with a prompt and stream false, and prints the full AI response.

Extract text from PDFs, Word, and TXT files8:18
Build a local document processing pipeline in Mistral AI to extract, store, and index text from PDFs, Word, and TXT files using PyMuPDF and python-docx.
Convert text into embeddings for fast searching (using LangChain + ChromaDB)6:55
Convert text to embeddings for fast search using LangChain and chroma db, splitting text into 500-character chunks with 50 overlap and storing embeddings via a Hugging Face model.
Store indexed documents for efficient retrieval4:56
Store indexed documents as embeddings in chroma db, then retrieve the top five similar text chunks using a similarity search to power the document retrieval pipeline.

Build a vector search pipeline to find relevant documents4:29
Build an AI powered vector search pipeline using embeddings in Chroma DB to retrieve top five document passages, then explore retrieval augmented generation and connecting to Mistral AI via LangChain.
Implement retrieval-augmented generation (RAG) for better answers5:20
Implement retrieval augmented generation (RAG) to deliver contextual, grounded answers by retrieving document chunks with chroma and generating responses via Mistral AI and Ollama, grounded in real documents.
Connect Mistral AI via LangChain to generate AI-powered summaries5:12
Connect mistral ai via LangChain to generate ai-powered summaries by retrieving relevant documents from the chroma db with a retrieval QA pipeline and an LLM integration.

Create an API endpoint to process user queries7:23
Expose a fully functional AI powered search via a fast API endpoint for front end UI, chatbot, or other apps, leveraging Mistral AI, LangChain, Chroma DB, and embeddings.
Integrate document retrieval with Mistral AI1:48
Integrate document retrieval with MySQL by running a uvicorn API server, exposing a root endpoint, and preparing to test the /query data retrieval in the next section.
Test the API using Postman or Python requests4:18
Test the API using Postman or Python requests by sending a POST with a JSON body to the query endpoint, and verify the 200 response with Vivian Aranha's experience data.

Requirements

Basic Python knowledge is recommended but not required.
Familiarity with APIs and HTTP requests is helpful but optional.
A computer with at least 8GB RAM (16GB recommended for better performance).
Windows, macOS, or Linux with Python 3.8+ installed.
Basic understanding of AI concepts is a plus but not mandatory.
No prior experience with Ollama, LangChain, or Mistral AI is needed.
Willingness to learn and experiment with AI-powered applications.
Admin access to install necessary tools like FastAPI, Streamlit, and ChromaDB.
A stable internet connection to download required models and dependencies.
Curiosity and enthusiasm to build AI-powered search applications!

Description

Are you ready to build AI-powered applications with Mistral AI, LangChain, and Ollama? This course is designed to help you master local AI development by leveraging retrieval-augmented generation (RAG), document search, vector embeddings, and knowledge retrieval using FastAPI, ChromaDB, and Streamlit. You will learn how to process PDFs, DOCX, and TXT files, implement AI-driven search, and deploy a fully functional AI-powered assistant—all while running everything locally for maximum privacy and security.

What You’ll Learn in This Course?

Set up and configure Mistral AI and Ollama for local AI-powered development.
Extract and process text from documents using PDF, DOCX, and TXT file parsing.
Convert text into embeddings with sentence-transformers and Hugging Face models.
Store and retrieve vectorized documents efficiently using ChromaDB for AI search.
Implement Retrieval-Augmented Generation (RAG) to enhance AI-powered question answering.
Develop AI-driven APIs with FastAPI for seamless AI query handling.
Build an interactive AI chatbot interface using Streamlit for document-based search.
Optimize local AI performance for faster search and response times.
Enhance AI search accuracy using advanced embeddings and query expansion techniques.
Deploy and run a self-hosted AI assistant for private, cloud-free AI-powered applications.

Key Technologies & Tools Used

Mistral AI – A powerful open-source LLM for local AI applications.
Ollama – Run AI models locally without relying on cloud APIs.
LangChain – Framework for retrieval-based AI applications and RAG implementation.
ChromaDB – Vector database for storing embeddings and improving AI-powered search.
Sentence-Transformers – Embedding models for better text retrieval and semantic search.
FastAPI – High-performance API framework for building AI-powered search endpoints.
Streamlit – Create interactive AI search UIs for document-based queries.
Python – Core language for AI development, API integration, and automation.

Why Take This Course?

AI-Powered Search & Knowledge Retrieval – Build document-based AI assistants that provide accurate, AI-driven answers.
Self-Hosted & Privacy-Focused AI – No OpenAI API costs or data privacy concerns—everything runs locally.
Hands-On AI Development – Learn by building real-world AI projects with LangChain, Ollama, and Mistral AI.
Deploy AI Apps with APIs & UI – Create FastAPI-powered AI services and user-friendly AI interfaces with Streamlit.
Optimize AI Search Performance – Implement query optimization, better embeddings, and fast retrieval techniques.

Who Should Take This Course?

AI Developers & ML Engineers wanting to build local AI-powered applications.
Python Programmers & Software Engineers exploring self-hosted AI with Mistral & LangChain.
Tech Entrepreneurs & Startups looking for affordable, cloud-free AI solutions.
Cybersecurity Professionals & Privacy-Conscious Users needing local AI without data leaks.
Data Scientists & Researchers working on AI-powered document search & knowledge retrieval.
Students & AI Enthusiasts eager to learn practical AI implementation with real-world projects.

Course Outcome: Build Real-World AI Solutions

By the end of this course, you will have a fully functional AI-powered knowledge assistant capable of searching, retrieving, summarizing, and answering questions from documents—all while running completely offline.

Enroll now and start mastering Mistral AI, LangChain, and Ollama for AI-powered local applications.

Who this course is for:

Anyone Curious About AI who wants to build practical AI applications without prior experience!
Students & Learners eager to gain hands-on experience with AI-powered search tools.
Cybersecurity & Privacy-Conscious Users who prefer local AI models over cloud solutions.
Python Programmers looking to enhance their skills with AI frameworks like LangChain.
Researchers & Knowledge Workers needing AI-based document search assistants.
Tech Entrepreneurs & Startups exploring self-hosted AI solutions.
Backend Engineers who want to implement AI-powered APIs using FastAPI.
Software Developers interested in building AI-driven document retrieval systems.
Data Scientists & ML Engineers looking to integrate AI search into real-world projects.
AI Enthusiasts & Developers who want to build local AI-powered applications.

Mistral AI Development: AI with Mistral, LangChain & Ollama

What you'll learn

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Course content

Introduction to Mistral AI and Ollama7 lectures • 55min

Setting Up Your AI Environment3 lectures • 13min

Loading and Indexing Documents3 lectures • 20min

Implementing AI-Powered Search3 lectures • 15min

Building the API with FastAPI3 lectures • 13min

Designing a Simple User Interface1 lecture • 7min

Requirements

Description

Who this course is for: