Mistral AI Development: AI with Mistral, LangChain & Ollama

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

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.