2025 Master Langchain and Ollama - Chatbot, RAG and Agents

Requirements

• Basic Python programming knowledge

• Familiarity with APIs and web requests

• Basic understanding of machine learning concepts

• Access to a computer with internet for installations and setups

Description

This course is a practical guide to integrating Langchain and Ollama to build, automate, and deploy AI applications. Learn to set up these tools, create prompt templates, automate workflows, manage data retrieval, and deploy real-world applications on AWS. Each section is designed to provide you with hands-on skills and experience.

What You Will Learn

1. Ollama & Langchain Setup

   • Complete setup and installation of Ollama and Langchain.

   • Configure base URLs and handle direct API calls.

   • Establish the environment for efficient integration.

2. Prompt Engineering

   • Understand AI, human, and system message prompts.

   • Use AIPromptTemplate, Human, System, and ChatMessagePromptTemplate to shape responses.

   • Explore the invoke method to control the model's behavior.

3. Chains for Workflow Automation

   • Learn Sequential, Parallel, and Router Chains to build flexible workflows.

   • Work with custom chains and explore Chain Runnables for added automation.

   • Implement real-world workflows using Langchain's chaining capabilities.

4. Output Parsing

   • Format data with parsers like JSON, CSV, Markdown, and Pydantic.

   • Parse structured output and use date-time output handling for organized data.

5. Chat Message Memory

   • Use BaseChatMessageHistory and InMemoryChatMessageHistory for managing chat sessions.

   • Create chat applications with memory to improve user experience.

6. Build and Deploy Chatbots

   • Build a chatbot application using Streamlit.

   • Maintain chat history and handle user inputs efficiently.

7. Document Loaders and Retrievals

   • Work with loaders for web pages, PDFs, HTML data.

   • Retrieve and summarize documents, convert text data, and use vector stores.

8. Vector Stores and Retrievals

   • Integrate vector stores for document retrieval using FAISS and Chroma.

   • Reload retrievers, index documents, and enhance retrieval accuracy.

9. Tool Calling and Custom Agents

   • Set up tools for Tavily Search, PubMed, Wikipedia, and more.

   • Design custom tools that can be used with the Agents and execute step-by-step instructions.

10. Real-World Integrations

• Execute text-based queries on MySQL.

• Parse LinkedIn Profile with LLM

• Parse Job Resume with LLM

• Deploy LLAMA with OLLAMA on AWS

Who This Course Is For

• Developers and data scientists who want to use Langchain and Ollama for AI applications.

• AI enthusiasts looking to automate workflows and create document retrieval systems.

• Professionals needing to build end-to-end chatbots or deploy applications on AWS.

• Learners with basic Python knowledge who want practical experience with real-world AI tools.

Who this course is for:

• Developers aiming to integrate language models into applications.
• Data scientists interested in automating workflows and leveraging document retrieval.
• AI enthusiasts eager to build custom chatbots and conversational tools.
• Professionals seeking skills in deploying applications on AWS and other platforms.
• Learners with basic Python and API knowledge who want to create end-to-end AI solutions.