Learn Generative AI with LangChain and Python
Generative AI creates new and original content, such as text, images, videos, music, and code. Unlike traditional AI that might classify data or make predictions based on existing information, generative AI learns the patterns and structures of a dataset and then uses that knowledge to produce new, novel outputs. LangChain is an open-source software development framework designed to simplify the creation of Generative AI applications. LangChain provides a set of pre-built, modular components that can be “chained” together to create a cohesive workflow.
Who is this Training for?
This course is for developers who have prior programming experience (in any language) and who want to pursue a career in Generative AI using LangChain or similar frameworks. This course will provide conceptual clarity and lay the necessary foundations to become Generative AI.
What you’ll learn in this Training?
- Python Fundamentals (data types, control statements, package and library organization)
- Essential concepts of python that are used in LangChain with hand-on exercises in labs
- LangChain ecosystem and how it is organized
- Build simple Generative AI application
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Training Course Structure
- Introduction to Large Language Models (LLMs)
- How the LLM model works
- Key Characteristics of the LLM models
- Understanding of different types of AI
- Understanding of Discriminative AI
- Understanding of Generative AI
- Difference between Generative AI applications, AI Agents, Workflows, Graphs, and Multi-Agent Systems (MAS)
- Building Blocks: Simple Generative AI Applications
- Understanding of Prompts
- What are Output Parsers
- Understanding of Embeddings and Embedding models
- What is Vector Database
- Introduction to Python
- Choices for environment creation
- Concept of Library, Modules, and Packages
- Understanding of Data Types (Mutable and Immutable)
- Understanding of String Manipulation
- What are the Control Flows
- Error and exception handling
- Introduction to Common Libraries Used in GenAI
- Understanding of Python Functions
- Define functions
- Positional and keyword arguments
- Scope of variable (LGEB – local, enclosing, global, built-in)
- Concept of First-Class Functions
- Understanding of Callable
- Concept of Higher Order Functions
- What is Callable
- Defining a Callable
- Understanding of Classes
- Defining Class in Python
- Naming conventions in Python Classes (mangling)
- Attaching Data to Classes
- Instance, Class, and static methods
- Dunder method
- Absolute Base Class (ABC)
- Learning LangChain Frameworks
- LangChain and LangGraph to Frameworks
- Langsmith and Langserve Platform
- Learning LangChain Libraries
- Key libraries in LangChain
- Organization of Libraries
- Which Library to use when
- LangChain Runnable, LCEL and “|” Operator
- What is Runnable
- Introduction to LangChain Expression Language (LCEL) Creating “Chains”
- Collections Module in Python
- What is Runnable
- Introduction to LangChain Expression Language (LCEL)
- How to create Chains
- LangChain “Document” data structure
- Creating an Instance of a Document
- Components and Methods to Construct / Manipulate a Document
- Packaging and unpacking a LangChain Document
- LangChain “Message” data structure
- Backbone of conversational applications in LangChain
- Components and Methods to Construct / Manipulate Document
- Different Types of Messages
- Understanding of Prompts
- Learn Prompt concepts
- Understanding different types of prompts
- Explore basic prompts and Chat prompts
- Pydantic Basics
- Data model based on a data class
- Data validation
- Type hinting and annotations
- Structured Output with LangChain
- Output parsers
- Craft prompts using Chat GPT and other models
- String manipulation using python
- Read / Write different file formats (XLSX, CSV, UTF8, PDF) using Python libraries
- Interacting with data sources such as database using python
- Crafting prompts using Chat GPT and other models
- Creating prompts programmatically using variables
- Create Conversational prompts for Chat
- Building QA Chat bot
- Building Conversational Chat bot
- Techniques to Unpack LangChain Message and Document
- Unpack LangChain Message and Document
- Deploy Chat bot on browser using Streamlit
- Structured output using QA bot
- Build Gen AI application for data validation (Date, String, integer)
- Generating SQL statements using LLM
Training Details
Training Schedule
Timings: 07:00 PM to 09:00 PM IST
Day: Weekends ( Saturday and Sunday )
Total Duration: 24 hours
Date: 31-Aug-2025
Mode: Online
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Testimonials
Good training and got to learn lot of new things on VBCS . Highly recommend!
Jithin Kumar
It was wonderful training sessions. Very precise content and good coverage of all concepts with in depth knowledge. It was a great learning experience. I would definitely recommend this training for an individual who would like to pursue career in Oracle VBCS.
Naveen Reddy
Teaching sessions of Ankur in VBCS is very helpful to excel our career in VBCS and we need to make sure we practice each day class thoroughly without fail.
Shravan Reddy
Ankur Jain has extensive knowledge of VBCS, and he conducts training in a professional manner. The agenda of the training is very clear and attempts to cover all possible common scenarios,those who are looking for new learner in VBCS can consider him, needs practice from our side is also very important thing to be considered. Thanks Ankur