Complete Agentic AI with Langchain Framework [3 Months]
- Introduction of AI
- Installation Of Anaconda And VS Code IDE
- Installation of Anaconda And VS Code
- Virtual Environment creation using VSCode
- Python Requirements
- Python Asics-Syntax And Semantics
- Variables in Python
- Basic Datatypes in python
- Operators in python
- Conditional statements in python
- Loops in Python
- List and List Comprehension In python
- Set in python
- Dictionaries in python
- Tuples in python
- Getting Started With Functions
- More Coding Examples with Functions
- Python Lambda Function
- Maps Function python
- Filter Function in python
- Import Modules And Package in Python
- Standard Library Overview
- File Operation In python
- Working With File Paths
- Exception Handling
- Classes And Objects in python
- Inheritance in OOPS
- Polymorphism In OOPS
- Encapsulations In oops
- Abstraction in OOPS
- Magic Methods In python
- Operative Overloading in Python
- Custom Exception Handling
- Iterators in python
- Generators in python
- Function copy closures and Decorators
- Numpy in python
- Pandas-DataFrame And Series
- Data Manipulation With Pandas And Numpy
- Reading Data From Various Data Source Using Pandas
- Logging Practical Implementation In Python
- Logging With Multiple Loggers
- Logging With A Real World Examples
- Getting Started With Pydantic in python
- Introduction To Pydantic
- Pydantic Practical Implementation
Langchain Framework
- LangChain Implements Step by Step
- Start With Langchain And Open Ai
- Creating Virtual Environment
- Important Components Of LangChain
- Data Ingestion With Documents Loaders
- Recursive Character Text Splitter
- Character Text Splitter With Langchain
- Introduction To OPENAI Embeddings
- OLLAMA Embeddings
- HuggingFace Embeddings
- Vector Stores-FAISS
- Vector Store And Retriever Chroma DB
- Building Basic LLM Application Using LCE
- Getting Started With Open Source Models Using Groq API
- Building LLM Prompt and StrOutput Parser Chain With LCEL
- Deploy Langserve Runnable And Chains As API
- Building AI agents With conversation History Using LangChain
- Building Chatbot with Message History Using Langchain
- Working With Prompt Template And Message ChatHistory Using Langchain
- Managing the Chat Conversation Histrory Using LangChain
- LangChain Updates
- Creating Virtual Environment Using UV Package
- Creating Agent Using Langchain
- LangChain LLM Model Integration
- Invoke And Batch Streaming Using LangChain
- Tools In Langchain
- Messages Types In LangChain
- LLM structurd Output Using Pydantic
- LLM Structured Output Using DataClass
- Middleware Summarization
- Human In The Loop Middleware
LANGGRAPH
- Started with LangGraph
- Introduction to LangGraph
- LangGraph Application Creation The Environment
- Setting Up OPENAI,GROQ,LANGSMITH API Keys
- Developing A Simple Graph or Workflow Using LangGraph-Building Nodes And Edges
- Building Simple Graph StateGraph And Graph Compiling
- Developing LLM Powered Simple Chatbot Using LangGraph
- Different Workflows in LangGraph
- Prompt Chaining Implementation with Langgraph
- Parallelization
- Routing in LangGraph
- Orchestrator Worker Implementation
- Evaluator-optimizer
RAG with LangGraph
- Agentic RAG Application Practical Implementation
Project 1:
End to End Agentic AI Project with Lang Graph
- Project setup with US Code
- Setting up the Github Repository
- Setting up the project structure
- Designing The Front End Using streamlit
- Implementing The LLM Module in Graph Builder
- Implementing The Graph Builder Module
- Implementing The Node Implementation
- Integrating the Entire Pipeline With Front end
- Testing The End To End Agentic Application
- Git and GitHub Introduction
- Git Installations
- Create GITHUB Repository
- GIt Commends
- [add, commit, origin, branch, push, switch, remote,origin]
- Using Git How to push project into GITHUB Repository
- Using Git How to clone project