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.
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.
Requirement:
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!