Build & Test Ai Agents, Chatbot, Rag With Ollama & Local Llm

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Build & Test Ai Agents, Chatbot, Rag With Ollama & Local Llm
Published 2/2025
Created by Karthik KK
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 71 Lectures ( 8h 21m ) | Size: 4.2 GB
Learn Building and Testing AI Agent, ChatBot, RAG with LangChain and LangSmith using Ollama and Local LLMs and RAGAs​

What you'll learn
Running LLMs in Local Machine for development of LLM application
Understand the power of Langchain for building Local LLM application
Understand Chain, Prompts, ChatPromptTemplates, ChatMessageHistory
Building Chatbots with Historical Information with Langchain
Building RAG application with Vector stores, Embedding and Local LLMs
Understanding and Building Tools for LLMs
Building AI Agents with Tooling support for LLMs
Testing/Evaluating AI Agent & RAG Application with RAGAs
Requirements
Basics of Python
Enthusiasm to learn the power of LLMs knowledge to enhance your app workflow
Enthusiasm to build AI Agents, RAG applications and Testing them
Description
Build & Test AI Agents, Chatbots, and RAG with Ollama & Local LLMs This course is designed for complete beginners-even if you have zero knowledge of LangChain, you'll learn step by step how to build LLM-based applications using local Large Language Models (LLMs).We'll go beyond development and dive into evaluating and testing AI agents, RAG applications, and chatbots using RAGAs to ensure they deliver accurate and reliable results, following key industry metrics for AI performance.What You'll Learn:Fundamentals of LangChain & LangSmithChat Message History in LangChain for storing conversation dataRunning Parallel & Multiple Chains (RunnableParallels, etc.)Building Chatbots with LangChain & Streamlit (with message history)Understanding Tools and Tool chains in LLMBuilding Tools and Custom Tools for LLM Creating AI Agents using LangChainImplementing RAG with vector stores & local LLM embeddingsUsing AI Agents and RAG with Tooling while building LLM AppsOptimizing & Debugging AI applications with LangSmithEvaluating & Testing LLM applications with RAGAsReal-world projects & hands-on testing strategiesAssessing RAG & AI Agents with RAGAsThis entire course is taught inside Jupyter Notebook with Visual Studio, providing an interactive, guided experience where you can run the code seamlessly and follow along effortlessly.By the end of this course, you'll be able to build, test, and optimize AI-powered applications with confidence!
Who this course is for
Beginner Developer or QA Engineer
AI Engineer/Tester
AI Tester/Gen AI Tester
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