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Udemy - Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM 2025-8

0 students Last updated Aug 2025

About the course

Udemy - Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM 2025-8

Course content

Sections: 17

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Activities: 1

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Resources: 109

Announcements
1 Introduction
2 Why Langchain
3 Understanding Langchain Ecosystem

1 Introduction
2 Understanding LLMs to run in Ollama
3 Running Local LLMs with Ollama
4 Using Ollama Model in GUI based App in Local Machine
5 Understanding Ollama command line further
6 Running Ollama Serve to access LLMs via API

1 Introduction
2 Writing First Langchain code
3 Setting Langsmith for Observability Debugging and Testing Langchain code
4 Working with Langchain Prompt Template
5 Working with ChatPromptTemplate
6 Working with MessagePromptTemplate

1 Introduction and Runnables
2 Understanding and working with Chaining in LangChain
3 Chaining with StrOutputParse
4 Understanding and working with Multiple Chains
5 Understanding and working with RunnableParallel
6 Running Python functions with RunnableLambda
7 Creating Custom Chain with Chain decorator

1 Introduction
2 Creating Chat Message History in LangChain
3 Running Chat Message History in LangChain
4 Storing Chat Message History in SQL Chat Message History

1 Introduction
2 Initial Setup of Streamlit application in our Notebook
3 Creating Session History for Chat in Streamlit
4 Maintaining the session history in Streamlit application UI
5 Creating New Session history and maintaining the session history
6 Streaming Chat Response in Streamlit app with LangChain Stream
7 Building More cosmetic changes for ChatBot

1 Introduction
2 Extracting Multiple PDF Files with PyPDF Document Loader
3 Text Splitting in Chunks
4 Embedding Texts in to Vectors using Embedding Models
5 Initialize Chroma Database as Vector Datastores
6 Stores Data chunks into Vector Datastores
7 Retrieval of Data from Vector datastore
8 Retrievers for Vector stores
9 Manual Retrieval of Data from Vector stores with LangChain Prompts and LLM
10 Using LangChain Hub for Prompts
11 Using RetrievalQA to retrieve data from Vector Stores and LLMs

1 Introduction
2 Working with Community Driven Custom Tools with Local LLM and LangChain
3 Creating Custom Tool with LangChain
4 Binding Custom tools with Local LLM
5 Executing Custom Tools with Local LLM Custom Logic
6 Invoking All tools from LLM

1 Introduction
2 Using AI Agents to choose the Custom Tools with LLM
3 Using PromptTemplate for AI Agent
4 Using Playwright Browser Tool to perform Web Page Extraction and Navigation
5 Using AI Agent to invoke Web Page Extraction with Playwright Browser Tool

1 Introduction
2 Creating Playwright Web Browser Tool and Embedding
3 Creating Vector Stores and Tool
4 Creating AI Agent To call RAG and Playwright Web browser Tool

1 Understanding Evaluation of LLM
2 Non Traditional LLMs EvaluationTesting
3 Different LLM Evaluation Libraries
4 Understanding RAGAs for LLMs Evaluation
5 Evaluating LLMs Application with SingleTurnSample with RAGAs
6 Evaluating LLMs Application with MultiTurnSample with RAGAs

1 Introduction
2 Creating RAG Vector Stores for RAGAs
3 Creating Retriever And RetrieverQA
4 Create RAGAs MultiSetSample Dataset dynamically from Vector Stores and LLM
5 Evaluating RAG system with RAGAs using Local LLM
6 Evaluating RAG System with RAGAs using OpenAI GPT 40 Model

1 Introduction
2 Importing Test Data CSV and Vector Stores for Reference used in RAGAs
3 Creating Tool to Retrieve data from RAG with Tool decorator
4 Adding Custom Tool to AI Agent to Test AI Agent invocation
5 Create RAGAs recommended DataSet from our Agent
6 Evaluating Dataset with RAGAs
7 Executing MultiSetSample dataset from AI Agents RAG with RAGAs

1 Introduction
2 Configuring DeepEval in Visual Studio code with Jupyter Notebook
3 Creating DeepEval Golden DataSet
4 Creating actual retrieved context from RAG via Vector Stores and LLM
5 Creating LLMTestCase required for DeepEval with Actual and Expected data
6 Running DeepEval Metrics for our RAG response
7 Improving the RAG response based on the DeepEval Metrics

1 Introduction
2 Setting up Custom Tools and AI Agents with Tools
3 Creating Golden Dataset with Tool Calling using ToolCall class
4 Uploading Golden Dataset to DeepEval Confident AI
5 Getting Intermediate Steps from AI Agent to get the actual tools called by Agent
6 Creating Data for LLMTestCase
7 Preparing LLMTestCase Format from actual and expected with Golden Dataset
8 Running ToolCorrectnessMetrics to Evaluate the Tools called by Agent
9 Extending ToolCorrectnessMetrics with more in depth Evaluation

1 Introduction
2 Installation of libraries to build MCP Server
3 Creating MCP Server instance with FastMCP
4 Adding Tools to your MCP with mcptool
5 Running your MCP Server configuring it with Claude Desktop
6 Building File System Reader MCP Server using FastMCP
7 Configuring your MCP Server with Claude Desktop and running it

1 Introduction
2 Creating Playwright Browser and Page Instance for your MCP Server
3 Adding Click and Navigate Tools for your Playwright MCP Server
4 Running Playwright MCP Server by configuring it with Claude Desktop
5 Adding Javascript Executor and Fill Tool for your Playwright MCP Server
6 Adding Get Text Get Title tool for your Playwright MCP Server
7 Comparing the FastMCP Code with Node Implementations
8 Executing Fully powered Custom Playwright MCP Server with your Claude Desktop

Instructors

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Udemy - Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM 2025-8

Udemy - Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM 2025-8

Course modified date: 19 Aug 2025

Udemy - Build & Test AI Agents, ChatBot, RAG with Ollama & Local LLM 2025-8

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