CourseGenix

Generative AI (Langchain, Langgraph, Langsmith, Pydantic AI)

4 Units16 Lessons

Unit 1

langsmith

Introduction to Langsmith for AI Tracing

Key Features of Langsmith for Evaluation

Real-World Project: Implementing Langsmith in AI Workflow Monitoring

Advanced Langsmith Techniques for AI Optimization

Unit 2

langchain

Basics of Langchain Framework

Building Simple Chains with Langchain

Integrating Prompts and Models in Langchain

Real-World Project: Developing a Chatbot with Langchain

Enhancing Langchain Chains for Complex AI Tasks

Unit 3

pydantic AI

Fundamentals of Pydantic in AI Data Modeling

Advanced Data Schemas with Pydantic for AI

Real-World Project: Applying Pydantic in AI Data Pipelines

Optimizing Pydantic Models for Generative AI Outputs

Unit 4

langgraph

Introduction to Langgraph Workflows

Real-World Project: Building a Multi-Agent System with Langgraph

Advanced Graph Structures in Langgraph

Unit 4•Chapter 2

Real-World Project: Building a Multi-Agent System with Langgraph

Summary

User requests summaries of YouTube transcripts limited to 250 words, excluding sponsors, unrelated content, and introductory statements.

Concept Check

0/5

What is the core purpose of Langgraph in multi-agent systems?

Provides basic UI interfaces

Enables simple data storage

Manages external APIs only

Facilitates graph-based agent coordination

How does Langgraph handle agent state persistence?

With random access memory

Using simple file storage

Via direct database queries

Through graph nodes and edges

What challenge arises in scaling multi-agent systems with Langgraph?

Synchronization of agent interactions

Basic network latency

Increased hardware costs

Limited code complexity

In Langgraph, how are agent dependencies modeled?

As directed graph edges

Via independent modules

With random connections

Through linear sequences

What role does error handling play in Langgraph multi-agent projects?

Focuses on user notifications

Ignores failures completely

Only logs errors passively

Ensures robust agent recovery

PreviousIntroduction to Langgraph Workflows

NextAdvanced Graph Structures in Langgraph