• Post category:StudyBullet-19
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Learn AI-powered document search, RAG, FastAPI, ChromaDB, embeddings, vector search, and Streamlit UI

What you will 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.

Why take this course?

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?


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  • 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.

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