LangChain Agents Deep Dive with GPT 3.5 - LangChain

Dive deep into the world of LangChain Agents and their integration with GPT 3.5 in this comprehensive 32-minute video tutorial. Explore the limitations of Large Language Models (LLMs) and discover how agents can enhance their capabilities. Learn about the concept of agents, their functionality, and practical implementation using the LangChain library. Follow along as the tutorial demonstrates initializing calculator tools, creating LangChain agents, and posing questions to test their abilities. Gain insights into adding multiple tools, creating custom tools, and understanding various agent types such as Zero-shot ReAct, Conversational ReAct, and Self-ask with search. By the end, grasp the potential of LangChain agents in supercharging LLMs for improved performance in logic, calculation, and search tasks.

Why LLMs need tools
What are agents?
LangChain agents in Python
Initializing a calculator tool
Initializing a LangChain agent
Asking our agent some questions
Adding more tools to agents
Custom and prebuilt tools
Francisco's definition of agents
Creating a SQL DB tool
Zero shot ReAct agents in LangChain
Conversational ReAct agent in LangChain
ReAct docstore agent in LangChain
Self-ask with search agent
Final thoughts on LangChain agents