Mistral Ai Development: Ai With Mistral, Langchain & Ollama

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

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.

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 UsedMistral 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 SolutionsBy 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.

Overview

Section 1: Introduction to Mistral AI and Ollama

Lecture 1 What is Mistral AI? Overview of Mistral 7B, Mistral-Instruct, and Mixtral models

Lecture 2 What is Ollama? How it enables running LLMs locally

Lecture 3 Why use Ollama for local AI applications? Advantages & privacy benefits

Lecture 4 How does Mistral AI compare to GPT-4 and LLaMA?

Lecture 5 Installing Ollama & Running Mistral Locally – Step-by-step setup

Lecture 6 Up and Running with Python

Section 2: Setting Up Your AI Environment

Lecture 7 Install and configure Ollama to run Mistral AI locally

Lecture 8 Install required Python libraries

Lecture 9 Run a test query to verify Mistral AI is working

Section 3: Loading and Indexing Documents

Lecture 10 Extract text from PDFs, Word, and TXT files

Lecture 11 Convert text into embeddings for fast searching (using LangChain + ChromaDB)

Lecture 12 Store indexed documents for efficient retrieval

Section 4: Implementing AI-Powered Search

Lecture 13 Build a vector search pipeline to find relevant documents

Lecture 14 Implement retrieval-augmented generation (RAG) for better answers

Lecture 15 Connect Mistral AI via LangChain to generate AI-powered summaries

Section 5: Building the API with FastAPI

Lecture 16 Create an API endpoint to process user queries

Lecture 17 Integrate document retrieval with Mistral AI

Lecture 18 Test the API using Postman or Python requests

Section 6: Designing a Simple User Interface

Lecture 19 Streamlit, file upload functionality and chat-like interface for user queries

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.