Master Local AI: Build Intelligent Apps with Mistral, LangChain & Ollama

What you will learn:

Build a local AI-powered document search engine.
Master document processing techniques for various file types.
Create effective vector embeddings using sentence transformers.
Implement Retrieval-Augmented Generation (RAG) for enhanced AI responses.
Develop a high-performance API with FastAPI for query handling.
Build an interactive user interface using Streamlit.
Integrate Mistral AI and LangChain for seamless LLM interaction.
Optimize AI search for speed and accuracy.
Deploy and manage local AI models using Ollama.
Securely utilize local AI for privacy-conscious applications.

Description

Unlock the power of local AI development with our comprehensive course on building intelligent applications using Mistral, LangChain, and Ollama. This course goes beyond the basics, guiding you through the entire process of creating a fully functional, privacy-focused AI assistant. You’ll learn to harness the capabilities of powerful LLMs like Mistral, manage them efficiently with Ollama, and integrate them seamlessly with other essential tools.

Dive deep into practical applications:

Master document processing: Extract and process textual data from various file formats (PDF, DOCX, TXT) with ease.
Build robust AI search functionality: Utilize vector embeddings and ChromaDB to create efficient and accurate search capabilities.
Implement Retrieval-Augmented Generation (RAG): Enhance your AI's responses with contextually relevant information retrieved from your documents.
Develop high-performance APIs: Create robust APIs using FastAPI to power your AI applications and ensure smooth query handling.
Design intuitive user interfaces: Build engaging and user-friendly interfaces using Streamlit for seamless interaction.
Optimize for performance: Learn advanced techniques to optimize your AI search performance for speed and accuracy.
Deploy your own private AI assistant: Run your AI assistant locally, guaranteeing maximum privacy and security without relying on cloud services.

This course is perfect for: AI developers, ML engineers, Python programmers, software engineers, tech entrepreneurs, cybersecurity professionals, data scientists, researchers, and anyone seeking practical, hands-on experience with cutting-edge AI technologies. Start building your own private, secure, and powerful AI solutions today!

Curriculum

Introduction to Local AI Development

This section lays the groundwork for your journey into local AI. You'll begin by understanding the core concepts behind Mistral AI, exploring its various models (Mistral 7B, Mistral-Instruct, Mixtral), and comparing its capabilities to other leading LLMs. Then, we introduce Ollama, a crucial tool for running LLMs locally, highlighting its advantages in terms of privacy and control. You'll learn how to install and configure both Ollama and Mistral, ensuring a smooth transition into the practical aspects of the course. Finally, we'll dive into Python setup, equipping you with all the necessary tools for effective development.

Setting Up Your Development Environment

This section focuses on setting up your ideal development environment. You'll learn how to successfully install and configure Ollama for running Mistral AI locally. We'll guide you through installing the essential Python libraries, crucial for smooth interaction with different components of your AI system. Finally, a test query will help you verify that Mistral AI is working correctly and that everything is set up as expected.

Document Ingestion & Embedding

This section covers efficient document handling. Learn to extract text from various file formats, including PDFs, Word documents, and TXT files. You'll then delve into the world of embeddings using LangChain and ChromaDB, transforming text into vector representations for optimized search. Finally, you'll master storing indexed documents for efficient retrieval in your AI system.

Building an Intelligent Search System

Here, you'll build the heart of your AI application. You'll construct a vector search pipeline that pinpoints relevant documents based on user queries. Then, learn the fundamentals of Retrieval-Augmented Generation (RAG) and integrate it into your system, significantly enhancing the quality and context of AI responses. We will also connect Mistral AI through LangChain, leveraging its power to generate AI-powered summaries of retrieved documents.

API Development with FastAPI

This section focuses on creating a robust and scalable API using FastAPI. You'll learn how to design an API endpoint that efficiently processes user queries, integrating your document retrieval and AI response generation functionalities. Finally, we'll guide you through testing your API using tools such as Postman or Python's `requests` library to ensure its optimal performance.

User Interface Design with Streamlit

This final section guides you through building a user-friendly interface for your powerful AI assistant. Using Streamlit, you'll develop a chat-like interface that allows users to easily upload files and receive accurate, context-aware responses. This section completes the cycle, showcasing how to combine the backend functionality with an easily accessible frontend.