
Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM
Learn Building and Testing AI Agent, ChatBot, RAG with LangChain and LangSmith using Ollama and Local LLMs and RAGAs
What you'll learn
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
About this course
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 & LangSmith
- Chat Message History in LangChain for storing conversation data
- Running Parallel & Multiple Chains (RunnableParallels, etc.)
- Building Chatbots with LangChain & Streamlit (with message history)
- Understanding Tools and Tool chains in LLM
- Building Tools and Custom Tools for LLM
- Creating AI Agents using LangChain
- Implementing RAG with vector stores & local LLM embeddings
- Using AI Agents and RAG with Tooling while building LLM Apps
- Optimizing & Debugging AI applications with LangSmith
- Evaluating & Testing LLM applications with RAGAs
- Real-world projects & hands-on testing strategies
- Assessing RAG & AI Agents with RAGAs
This 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!
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